B-cell epitope prediction

Abstract

Predicting whether a protein contains B cell epitopes that could trigger antibody binding events. [Solution] (a) obtain one or more structural and / or surface properties of a protein; and (b) implement a computer to predict whether a protein contains a true B cell epitope by inputting one or more structural and / or surface properties of the protein into a first trained machine learning model.

Description

[Background technology]

[0001] background Lymphocytes are a type of white blood cell and are a central component of the immune system in most vertebrates. They include B cells, T cells, and natural killer (NK) cells. Their function is to identify pathogens such as bacteria, viruses, and malignant cancer cells with mutated protein antigens, and to protect against them. Both B cells and T cells express surface receptors that identify specific molecular components on pathogens called antigens (Ag). Successful recognition of Ag triggers a broader adaptive immune response, which can ultimately eliminate the potential pathogenic threat.

[0002] Antibodies (Ab), also known as immunoglobulins (Ig), are glycosylated protein molecules (surface immunoglobulins) or B cell-eluting Ig molecules present on the surface of B cells that act as antigen-specific B cell receptors (BCRs). Upon successful Ag recognition, Ig or membrane-bound BCRs bind to specific Ag amino acid contact sites, collectively referred herein as B cell epitopes (BCEs). This binding event can trigger the activation of an immune response, including clonal B cell proliferation that produces large quantities of effector B cells possessing Ag-specific Ig serum-eluting molecules or Ag-specific BCRs. The secreted soluble Ig and effector BCRs, also called antibodies (Ab), have the same binding specificity as the original Ig-Ag binding interaction event. The Abs then circulate in the serum and bind to the same BCEs, signaling the elimination of pathogenic or cellularly stressed cells corresponding to the identified Ag.

[0003] Ab-based vaccines have the potential to prepare or develop each individual's immune system against potentially harmful Ag, resulting from infectious pathogenic threats or stressed malignant cancer cells. Vaccine design aims to modify Ag in a way that bypasses harmful malignant, pathogenic, or infectious properties while maintaining a potential protective immune response driven by immunogenic epitopes. Therefore, accurate identification or prediction of B-cell epitopes on antigens is crucial not only for vaccine design but also in the areas of molecular diagnostics, immune monitoring, and immunotherapy.

[0004] BCEs can be classified into two main categories: linear B cell epitopes (LBCEs) and conformational B cell epitopes (CBCEs). Both LBCEs and CBCEs can bind to Abe and therefore trigger an immune response. It is estimated that over 90% of BCEs are conformational, while the remainder are linear.

[0005] LBCEs consist of sequential amino acid sequences. These BCEs can be discovered by identifying the position on an unfolded Ag sequence to which Ab can bind. LBCEs are often cataloged in immunoepitope databases (IEDBs) derived from a wide variety of experimental assays. Each assay provides information about the tested protein sequence (i.e., Ag), the LBCE sequence and its coordinates on Ag, and the Ab to which the current LBCE was tested. Furthermore, this provides an indicator of whether the LBCE was bound by a given Ab. Binding information may also be described in the IEDB in the form of human-readable labels, i.e., positive, highly positive, moderately positive, lowly positive, and negative. Because LBCEs are convenient as linear molecular entities and the experimental methods used to characterize them are well established, a relatively large amount of available LBCE data is available compared to CBCE data. Nevertheless, annotation errors frequently occur in this type of data because many amino acids in the identified BCEs do not actually interact with Ab under the tertiary conformation of Ag.

[0006] CBCEs are composed of short segments of amino acids with a variety of potential cis, trans, short, or distant coordinate distances between each of the amino acid contact points constituting the BCE. These segments are brought into close proximity when Ag folds during binding to Ab. CBCEs can be experimentally characterized using X-ray crystallography of Ag-Ab structural complexes. This data is typically cataloged in the Protein Database (PDB) in the form of 3D structures, and usually consists of one or more Ag structures bound by at least one Ab structure. PDB structural data provides information about the 3D coordinates of each molecule in the structure. However, because this method is complex, CBCE data is somewhat limited compared to LBCE data. Moreover, 3D structures may not accurately reflect the true CBCE. For example, Ab on the complex may be missing its heavy or light chain, or the 3D structure may only provide Ag-Ab contacts outside the complementarity-determining region (CDR). Furthermore, the extremely sparse available information regarding undiscovered CBCEs on any Ag, and ground truth-negative epitopes on Ag, makes predicting true CBCEs even more difficult.

[0007] Various algorithms exist for predicting either LBCE and / or CBCE. However, while many of these algorithms perform well on test data, they do not yield the same satisfactory results on new, independent test data when compared to other algorithms of a similar type.

[0008] Many conventional CBCE prediction algorithms require a three-dimensional protein structure file of Ag as the expected input. However, laboratory-scale experiments to determine this are labor-intensive and costly, so in most use cases, the three-dimensional protein structure of Ag will almost certainly not be available at the time of prediction; therefore, the use of three-dimensional structure input algorithms is limited.

[0009] Recently, a method for predicting B-cell epitopes has been published that enables the input of protein sequences without strictly requiring the input of accompanying three-dimensional structural information at the prediction time. One example is the "DiscoTope 3.0" algorithm (https: / / doi.org / 10.1101 / 2023.02.05.527174). A machine learning strategy is adopted where epitopes from antibody-antigen complexes are mapped onto the antigen and used as training data. DiscoTope-3.0 is trained on both the predicted and resolved structures of the merged antigen structure (i.e., the antibody-antigen structure), while previous versions of this method were trained on experimentally resolved antibody-antigen structures. B-cell epitope prediction techniques that require the protein sequence as input are user-friendly compared to those that require the complete three-dimensional structure, but it can be difficult to capture the relationships between spatially separated amino acids or to distinguish between potentially different CBCEs even on the same antigen.

Summary of the Invention

Problems to be Solved by the Invention

[0010] Therefore, there is a need to improve the prediction of B-cell epitopes, particularly CBCEs.

Means for Solving the Problems

[0011] Summary of the Invention In a first aspect of the present invention, a computer-implemented method for predicting whether a protein contains a B-cell epitope that has the potential to cause a binding event with an antibody, comprising: (a) obtaining one or more structures and / or surface properties of the protein; and (b) predicting whether the protein contains a true B-cell epitope by inputting one or more structures and / or surface properties of the protein into a trained first machine learning model In the method comprising, the first machine learning model is Generating a first reference dataset, the first reference dataset comprising: (i) a plurality of first reference proteins, each of the first reference proteins comprising at least one B-cell epitope classified as a true B-cell epitope; and (ii) one or more structures and / or surface characteristics of each of the first reference proteins in an unbound state ; and training a first machine learning model using the first reference dataset to learn a relationship between one or more structures and / or surface characteristics of the first reference proteins in an unbound state and a B-cell epitope classified as a true B-cell epitope A method is provided that is trained thereby.

[0012] Upon binding between a B-cell epitope and an antibody, the three-dimensional structure of the BCE can change. In other words, the three-dimensional structure of the BCE in the bound antibody-antigen complex can be different from the three-dimensional structure of the BCE before the binding event is triggered.

[0013] The present invention provides a method of predicting whether a protein (e.g., a query) contains a B-cell epitope using a trained first machine learning model trained using a first reference dataset comprising the structure and / or surface characteristics of a plurality of first reference proteins in an unbound state. Each of the first reference proteins comprises at least one B-cell epitope classified as a true B-cell epitope. Thus, the first machine learning model is advantageously trained to predict true B-cell epitopes on a query protein before a binding event occurs.

[0014] As used herein, the term "unbound state" (or "native state") means the state (e.g., three-dimensional structure) of a protein or BCE that is not affected by other proteins, structures or interacting molecules (e.g., before a binding event occurs). Importantly, it is this unbound state that the Ab "sees" and triggers the binding event, which is not something the Ab has already "seen" and bound to.

[0015] Therefore, using one or more structural and / or surface properties of each first reference protein in an unbound state offers a key advantage, compared to the fact that models of the domain's current level of expertise, trained on Ag-Ab complexes (i.e., proteins in a bound state), can be erroneous because predictions are based on structural data obtained after the binding event has occurred.

[0016] Typically, the output of a trained first machine learning model is a probability indicating whether a protein contains a true B cell epitope. As further described herein, this probability can typically take the form of the probability that a candidate epitope on the query protein is a true epitope. In some embodiments, the output can take the form of the probability that individual amino acids (or groups of amino acids) within the amino acid sequence of the query protein constitute part of a true B cell epitope. However, it is also conceivable that the output can be at the level of the entire protein (e.g., the probability that the query protein contains a true BCE).

[0017] In this specification, the term “true” B cell epitope means a B cell epitope that triggers an antibody binding event (e.g., triggers an immunogenic reaction, and / or is triggered by an immunogenic reaction).

[0018] A B cell epitope can be a structural B cell epitope. A B cell epitope can be a linear B cell epitope. The first trained machine learning model of the present invention can typically predict the presence of both linear and structural B cell epitopes, but in some embodiments, different models may be employed for predicting linear and structural B cell epitopes, based on the reference data used for training.

[0019] Generally, a protein can be a protein, a protein domain, or a protein subunit. The term "protein" can include any protein subsequence that can have a feasible three-dimensional structure or functional structure. Typically, a protein contains an antigen. A protein may contain a neoantigen.

[0020] As described above, in step (a) of this method, one or more (e.g., three-dimensional) structural and / or surface (e.g., surface exposure) properties of a protein are accessed. Since Ab is highly specific to its binding BCE, the structural and / or surface properties of the protein under investigation are important for predicting B cell epitopes (particularly CBCEs). One or more structural and / or surface properties of a protein may be properties at the whole protein level (e.g., secondary structure) or at the amino acid level (e.g., features related to individual amino acids of the protein). One or more structural and / or surface properties may be in the form of continuous features (such as relative solvent-accessible RSA or hemispheric-exposed HSA) and / or categorical features such as secondary structure.

[0021] In embodiments, one or more structural and / or surface properties (e.g., for both the input protein being queried and a first reference protein) include one or more of the protein's secondary structure (SS); relative solvent exposure (RSA); hemispheric exposure (HSA). The HSA value may be divided into upper hemispheric exposure and lower hemispheric exposure values. SS can be considered a structural property, while RSA and HSE can be considered surface properties. A particular advantage of the present invention is that it does not require the complete three-dimensional folded structure of the input protein (which can be difficult to measure or, if predicted, may contain numerous errors). Instead, the method obtains one or more structural and / or surface properties of the protein, which are advantageously easier to obtain and typically contain fewer errors compared to the complete folded structure. The inventors have found that RSA, HSE, and secondary structure, in particular, as properties, provide good predictive results, along with providing reliable indicators of the protein's three-dimensional structure. Further structural and / or surface properties may also be used as input to the model. Thus, the method of the present invention can provide good BCE prediction results without requiring the complete three-dimensional structure of the protein under investigation.

[0022] The method may further include obtaining one or more physiochemical properties of a protein and / or the amino acids that make up the protein, and inputting these one or more physiochemical properties (in addition to, for example, one or more structural and / or surface properties) into a first machine learning model. In such embodiments, the first reference dataset typically further includes one or more physiochemical properties of a first reference protein and / or the amino acids that make up the protein. Examples of such physiochemical properties include bulkiness, mole fraction of embedded residues, mean flexibility index, normalized frequency for alpha helices, known codon number encoding each amino acid of the universal genetic code, polarity, conformational parameters for beta turns, normalized frequency for beta sheets, side chain class, side chain polarity, retention coefficient, hydrophobicity constant, normalized frequency of beta turns, PK1 a-COOH, membrane-embedded helix parameters, and antigenicity. Different sets of physiochemical properties may be used to predict CBCE and LBCE. One or more physiochemical properties may be calculated or measured using techniques known in the art (e.g., from the amino acid sequence of a protein). The physiological and chemical properties can be determined for each amino acid in a protein.

[0023] Typically, each amino acid (or side chain) constituting a protein sequence has physiological and chemical properties that contribute in some way to its SS and 3D properties. Thus, embodiments that include the use of one or more physiological and / or surface properties in addition to the physiological and chemical properties can further enhance the performance of the first machine learning model.

[0024] In general, each of the structural and / or surface properties, as well as the physiological and chemical properties (if used), may contribute separately or in combination to the training and subsequent use of a first machine learning model for predicting whether a protein contains a B cell epitope.

[0025] Preferably, one or more structural and / or surface properties of each first reference protein are: Obtaining the amino acid sequence of the first reference protein; and To predict one or more structural and / or surface properties by applying one or more second machine learning models to that amino acid sequence. Predicted by, here One or more second machine learning models are trained on a second reference dataset containing multiple amino acid sequences of each second reference protein and their corresponding structure and / or surface properties in an unbound state.

[0026] Therefore, typically, one or more second machine learning models are trained on a second reference dataset to learn the relationship between the amino acid sequence (e.g., one or more of its features) of each second reference protein and the corresponding structure and / or surface properties in an unbound state.

[0027] Thus, according to embodiments of the present invention, it becomes possible to predict the structure and / or surface properties of a first reference protein in an unbound state, which, as stated above, is a key aspect of the present invention. Typically, one or more structure and / or surface properties include one or more of the protein's secondary structure; relative solvent exposure (RSA); hemispheric exposure (HSE). These properties of amino acids in a protein used to train a model of a second reference dataset can be obtained using known resources or tools, such as DSSP and BioPython algorithms, applied to the structure file of the second reference protein (e.g., obtained from the Protein Data Bank, PDB).

[0028] Typically, in addition to the amino acid sequence, the input to one or more second machine learning models includes several properties (e.g., features) of the first reference protein. The input properties are typically physiological and chemical properties, but in some embodiments, they may include structural and / or surface properties. These features are typically calculated or measured amino acid-wise using techniques known in the art. Examples of such (e.g., physiological and chemical) properties include one or more of hydrophobicity, side-chain class, side-chain polarity (e.g., for predicting HSE and RSA), and one or more of the coil's conformational parameters and the alpha-helix's conformational parameters (e.g., for predicting secondary structure). Further features may be used. Thus, one or more second machine learning models can be trained on the second reference dataset to learn the relationship between one or more (e.g., physiological and chemical) properties of the amino acid sequence of each second reference protein and the corresponding structural and / or surface properties (e.g., one or more of RSA, HSE, and secondary structure) in the unbound state. Typically, the second machine learning algorithm utilizes a different set of protein properties than the first machine learning algorithm.

[0029] The amino acid sequence of the first reference protein can be obtained using techniques known to those skilled in the art, for example, by oligonucleotide hybridization methods, nucleic acid amplification-based methods (including, but not limited to, polymerase chain reaction-based methods), automated prediction based on DNA or RNA sequencing, de novo peptide sequencing, Edman sequencing, or any peptide-related sequencing. The amino acid sequence can be downloaded from a bioinformatics repository such as UniProt (www.uniprot.org).

[0030] In some embodiments, different second machine learning models may be used to predict different structural and / or surface properties. In some embodiments, one second machine learning model may be used to predict categorical features, and different second machine learning models may be used to predict continuous features. Accordingly, the input properties of the models and the reference dataset may differ. For example, in one embodiment, a trained second machine learning model may be used to predict the secondary structure of a protein, and different trained second machine learning models may be used to predict RSA and HSE properties.

[0031] Referring again to step (a) of the method of the present invention, the structure and / or surface properties of a protein (e.g., in its unbound state) can be obtained using known techniques. For example, known algorithms (including computational approaches for predicting a complete three-dimensional protein structure) or X-ray crystallography techniques may be used to predict or measure protein features. For example, RSA, HSE, and secondary structure (and other) parameters may be obtained from the complete three-dimensional structure of the protein by prediction or experiment, but the complete structure itself is not required as input to the model.

[0032] However, in a particularly preferred embodiment, one or more structural and / or surface properties of the query protein can be predicted by applying one or more second machine learning models discussed above. This may require obtaining the amino acid sequence of the query protein. Thus, the structure and / or surface properties of the protein under investigation may be advantageously calculated from the amino acid sequence of the query protein, without requiring the user to supply or input any additional information.

[0033] One problem with conventional approaches to predicting B cell epitopes, particularly CBCEs, is the limited amount of data available to define true CBCEs. Therefore, embodiments of the present invention provide a method for obtaining a first reference protein, each containing at least one true B cell epitope.

[0034] Therefore, in some embodiments, at least some of the first reference proteins are (i) Accessing multiple (preferably non-absolute) protein complexes containing at least three different protein chains; (ii) Filter multiple protein complexes to determine the effective weight of the antibody (V H ) Protein chain mapped as a chain, effective light (V) of the antibody L ) Only those containing protein chains mapped as chains, and protein chains mapped as antigens, should be retained; (iii)V H Chain and V L Pairing the chains, V H and V L To form a chain pair; (iv) Each V H and V L For each chain pair, define the true B cell epitope on the corresponding antigen; It can be obtained by, however, At least some of the first reference proteins in the first reference dataset correspond to the antigen mapped in step (ii) that includes at least one true B cell epitope defined in step (iv).

[0035] The first reference protein obtained or identified as described above is typically mapped to its corresponding amino acid sequence. Thus, the structure and / or surface properties of each unbound first reference protein can be obtained from their respective amino acid sequences, independently of the original protein complex from which it was identified. The properties may preferably be predicted using one or more second machine learning models as described above. However, other known methods for predicting one or more structure and / or surface properties (e.g., from amino acid sequences), such as publicly available computational protein structure prediction algorithms, may be employed.

[0036] Typically, the protein complex obtained in step (i) can be of any organism or entity to which a taxonomic ID has been assigned in an established institution such as NCBI. This advantageously makes it possible to make the prediction made by the trained first machine learning model non-Ab specific.

[0037] In an embodiment, each true B cell epitope is defined as the amino acids of the corresponding antigen that contact the corresponding V H and V L chain pair, and preferably the amino acids are those that are within a predefined distance range of one or more complementarity determining regions, one or more CDRs of the V H and V L chain pair, and preferably contain any atoms within a predefined distance range of one or more complementarity determining regions, one or more CDRs of the V H and V L chain pair, and preferably the predefined distance is 4 angstroms or less.

[0038] Preferably, the method further comprises obtaining the amino acid sequence of the (e.g., query) protein; and generating one or more candidate encodings for each of the one or more candidate B cell epitopes on the protein, each candidate encoding representing the respective candidate B cell epitope as a plurality of data elements corresponding to the amino acids of the amino acid sequence ; wherein the input to the trained first machine learning model includes the amino acid sequence of the protein, one or more structures and / or surface properties of the protein, and one or more candidate encodings.

[0039] The inventors noticed a common problem with many conventional sequence-based techniques used to predict the presence of B cell epitopes on proteins: typically, the output prediction is in the form of a binary amino acid-by-amino acid value indicating whether each particular amino acid in the protein sequence is part of a B cell epitope. With this conventional method, users cannot distinguish between potentially distinct BCEs present on a protein that contain some overlapping amino acid residues, and furthermore, the relationships between spaced-out amino acids in the entire protein sequence under analysis in the three-dimensional structural BCE are not necessarily taken into account.

[0040] To improve upon these conventional BCE prediction techniques, embodiments of the present invention may apply a first trained machine learning model to combinations of an input amino acid sequence of a protein under investigation, one or more structural and / or surface properties, and one or more candidate encodings of each of the one or more candidate B cell epitopes, where each candidate encoding represents a respective candidate B cell epitope.

[0041] Thus, the predictions provided by the first trained machine learning model are made not in an "amino acid-by-amino acid" manner, but rather in an "epitope-by-epitope" manner on the query protein (by considering each candidate encoding corresponding to a candidate epitope). Therefore, such embodiments consider whether a candidate B cell epitope encoded on the amino acid sequence under test is a true BCE on the protein (or not). This method is particularly advantageous because it provides increased confidence that the epitope as a whole is actually present (or not present) on the protein, rather than having to infer the presence of the epitope (or multiple overlapping epitopes) based on individual amino acid scores. For example, by using a candidate encoding that represents the entire candidate epitope, it becomes possible to consider the potential relationships between spaced amino acids on the sequence. Providing predictions in such an "epitope-by-epitope" manner also provides increased accuracy and confidence in predicting multiple (potentially overlapping) epitopes on the same protein.

[0042] Providing predictions in an "epitope-by-epitope" manner using candidate encodings is particularly advantageous because the first machine learning model is trained on unbound data. Candidate encodings allow for the consideration of relationships between widely spaced amino acids in the sequence, which, as discussed herein, can differ significantly between the protein in a protein complex (bound state) and its unbound (natural) state.

[0043] In such embodiments where candidate encodings are used, the output of the trained first machine learning model is typically the probability that each of the one or more candidate encodings represents a true B cell epitope on the protein.

[0044] In such embodiments, the structure and / or surface properties of the protein obtained (e.g., predicted) are typically in the form of values ​​and / or classes that represent the structure and / or surface properties of the protein, assigned to each amino acid in the protein amino acid sequence. Thus, the input to the first machine learning model can be a set of properties for each amino acid of the protein.

[0045] As outlined above, candidate encodings for B cell epitopes represent each candidate B cell epitope as a set of data elements corresponding to amino acids in an amino acid sequence. Typically, each data element (or at least part of a set of data elements) of a candidate encoding corresponds to an amino acid in the amino acid sequence of a protein. Preferably, each of one or more encodings is in the form of a binary vector. In such embodiments, the binary vector is typically in the form of a series of "1"s and "0s," where each "1" corresponds to an amino acid in the protein being part of a candidate epitope (e.g., a proposed Ag-Ab contact point), and each "0" corresponds to an amino acid in the protein not being part of a candidate epitope (e.g., not a proposed Ag-Ab contact point). In some embodiments, different encoding protocols can be used to represent candidate epitopes (e.g., one-hot encoding).

[0046] In some embodiments, one or more candidate encodings represent all possible combinations of amino acids within an amino acid sequence. This may include both linear B-cell epitopes (where each encoded amino acid is located consecutively in the amino acid sequence) and structural B-cell epitopes (where at least some of the encoded amino acids are spaced apart in the amino acid sequence). However, in some embodiments, prior knowledge of the structure of the protein and / or B-cell epitopes may be used to reduce the search space for encodings. For example, if the protein contains a neoantigen, the encodings may be fixed to the location of the mutation site (e.g., each encoding would have the same data element at the location of the mutation site on the amino acid sequence).

[0047] One or more candidate encodings representing amino acid combinations within an amino acid sequence may be queried through a deep reinforcement learning model. One or more candidate encodings representing amino acid combinations within an amino acid sequence may be supplied by a generative model. In some embodiments, deep reinforcement learning (DRL) techniques may be used to reduce or query large search spaces.

[0048] Typically, the first reference dataset is: The amino acid sequences of each first reference protein; and Multiple reference encodings for each true B cell epitope, wherein each reference encoding represents each true B cell epitope as multiple data elements corresponding to amino acids in an amino acid sequence. It may further include the following.

[0049] The reference encoding for each true B cell epitope used in the first reference dataset is typically generated in the same way as described above.

[0050] The first machine learning model can be (or may include) any neural network, such as a convolutional neural network and a feedforward neural network. Preferably, the first machine learning model is (or includes) a recurrent neural network. Typically, the first machine learning model is (or includes) a long short-term memory network, LSTM, preferably a bidirectional long short-term memory network, BLSTM. This is particularly advantageous in embodiments where the trained first machine learning model takes the amino acid sequence of a query protein as input. Typically, each amino acid in the query protein sequence is treated as a time step in the network, whose properties can be influenced by both preceding and succeeding amino acids. Thus, the entire protein amino acid sequence (e.g., paired with other input features) may be treated as a "single data point," in contrast to conventional methods that require segmenting the protein amino acid sequence into smaller amino acid subsequences, e.g., "k-mers." Thus, the present invention is advantageous in that it is possible to discover relationships between spatially distant amino acids in a protein sequence, such as those that may be part of the same (e.g., structural) BCE, relationships that may not be discoverable through the "k-mer" method. The use of BLSTM is particularly advantageous because it allows for processing of protein amino acid sequences from both directions.

[0051] One or more second machine learning models can be (or include) any neural network, such as convolutional neural networks and feedforward neural networks. Typically, one or more trained second machine learning models are (or include) recurrent neural networks, preferably long short-term memory networks, LSTMs, and more preferably bidirectional long short-term memory networks, BLSTMs. This provides the same advantages outlined above.

[0052] As discussed above, in embodiments, the present invention is advantageous in that it can generate encodings for one or more candidate B cell epitopes to be tested in order to model and "test" each candidate epitope as a whole. As a result of this approach, the first reference dataset used to train the first machine learning model may contain only observed true positive BCEs (i.e., only "positive" training data). Therefore, in some preferred embodiments, the method may further include generating pseudorandom data based on multiple reference codes for each true B cell epitope in the first reference dataset, with the first reference dataset containing the pseudorandom data as negative data. In this way, the performance of the first machine learning model may be improved because the model can be trained even without validated negative data.

[0053] The pseudorandom data is based on the encoded true B cell epitopes. Preferably, the pseudorandom data includes multiple permutations of multiple data elements representing true B cell epitopes. In some embodiments, the pseudorandom data may be random permutations of data elements. In some embodiments, the pseudorandom data may share at least one property with the code of the true B cell epitope. The property may be a physical property of the true B cell epitope, for example, the position on the amino acid sequence of one or more amino acids (e.g., the first and last amino acids of the epitope). In another example, the property may be the straight-line distance between specific amino acids.

[0054] In some embodiments, the method includes, Identifying the first B-cell epitope that is predicted to be a true B-cell epitope on a protein; Substituting one or more amino acids that form an identified first B cell epitope with different amino acids to generate a modified amino acid sequence of the protein; and By using a pre-trained first machine learning model, we can predict whether a candidate encoding representing an identified first B cell epitope is likely to correspond to a true B cell epitope on a modified amino acid sequence of the protein. It may further include the following.

[0055] In this way, by applying one or more amino acid substitutions to the identified B cell epitopes, the first machine learning model can be used to predict B cell epitopes that do not naturally exist on the protein under analysis. This may be advantageous for identifying potential future viral variants and vaccine candidates that offer protection across a wide range of viral or pathogen species, as described in further detail herein.

[0056] In the substitution application step, one or more amino acids in the first B cell epitope (typically a wild-type B cell epitope) are substituted with any other amino acids present. This can be called an amino acid "mutation." Using a trained first machine learning model, the likelihood of each possible amino acid substitution in the identified first B cell epitope forming a true B cell epitope on the protein can be predicted. Further analysis can be performed on B cell epitopes predicted to be positive for the modified amino acid sequence. For example, structural stability analysis can be performed using techniques known in the art (e.g., computational).

[0057] Typically, if the candidate code is in the form of a binary vector, only the amino acids corresponding to the "1" in the binary vector (e.g., representing direct amino acid contact points on the 3D protein sequence with the antibody) are substituted ("mutated").

[0058] In the step of using a trained first machine learning model to predict whether a candidate code representing an identified first B cell epitope is likely to correspond to a true B cell epitope on a modified amino acid sequence of a protein, the input to the model includes the modified amino acid sequence, one or more structural and / or surface properties of the modified protein, and a candidate code representing the identified first B cell epitope. The structural and / or surface properties can be obtained, for example, by applying a second machine learning model to the modified amino acid sequence using the techniques described above.

[0059] In a further embodiment of the present invention, a method for training a machine learning model, The process involves generating a reference dataset, where the reference dataset is Multiple first reference proteins, each containing at least one B cell epitope classified as a true B cell epitope; and One or more structural and / or surface properties of each first reference protein in an unbound state. Including; and Training a machine learning model using a reference dataset to learn the relationship between the structure and / or surface properties of one or more first reference proteins in an unbound state and the corresponding B cell epitopes that are classified as true B cell epitopes. A method including this is provided.

[0060] Therefore, the method for training the machine learning model provides the advantages described above and can be used in combination with any of the examples described in the present invention. Advantageously, after training, the machine learning model can be used to predict whether a B cell epitope that could trigger an antibody binding event is contained in the query protein.

[0061] Typically, the reference dataset is: The amino acid sequence of each reference protein; and Multiple reference encodings for each true B cell epitope, wherein each reference encoding represents each true B cell epitope as multiple data elements corresponding to amino acids in an amino acid sequence. It also includes.

[0062] As described above, the reference dataset may further include one or more physiological and chemical properties of each protein and / or the amino acids that make up the reference protein.

[0063] The machine learning model may be (or include) any neural network, such as a convolutional neural network or a feedforward neural network. Preferably, the machine learning model is (or includes) a recurrent neural network. Typically, the first machine learning model is (or includes) a long-term short-term memory network (LSTM), preferably a bidirectional long-term short-term memory network (BLSTM).

[0064] The present invention also provides the use of a machine learning model trained using the methods described above (for example, to predict the presence of BCE on a query protein). The present invention also provides a machine learning model trained using the methods described above.

[0065] The present invention may further include synthesizing one or more proteins that are predicted to contain a B cell epitope, and / or the predicted B cell epitope using the techniques described herein.

[0066] The proteins predicted by this invention to contain B cell epitopes may be used to treat or prevent infectious diseases or other diseases, such as autoimmune or immune-related diseases and cancer. This invention is used in one or more of the following applications: • Therapeutic antibody mapping • Vaccine development and production (both for prevention and treatment) • Immunodiagnosis and immunomonitoring.

[0067] In some embodiments, the method may further include encoding one or more proteins predicted to contain a B cell epitope, and / or the predicted B cell epitope, and / or B cell epitope variants, and / or predicted or simulated variants of the B cell epitope, into corresponding protein, peptide, DNA, or RNA sequences. The DNA or RNA sequences may be incorporated into a delivery system for use in a vaccine (e.g., using naked or encapsulated DNA or encapsulated RNA). The method may also include incorporating the protein, peptide, DNA, or RNA sequences into the genome of a bacterial or viral delivery system to produce a vaccine.

[0068] Therefore, according to a further aspect of the present invention, a method for producing a vaccine, the following: To predict that a protein contains a B cell epitope, follow any of the examples discussed above; Synthesizing a protein and / or a predicted B cell epitope, or encoding at least one of a protein, a predicted B cell epitope, a B cell epitope variant, or a predicted or simulated variant of a B cell epitope in a corresponding protein, peptide, DNA, or RNA sequence. A method including this is provided.

[0069] Such proteins, peptides, DNA, or RNA sequences can be delivered in a naked or encapsulated form, or incorporated into the genome or cells of a bacterial or viral delivery system to produce a vaccine. In addition, DNA can be delivered to vaccinated host cells using bacterial vectors. In the case of peptide vaccines, the identified proteins can typically be synthesized as amino acid sequences or "chains."

[0070] Such vaccines may be preventive or therapeutic vaccines. For example, this method can be used to produce personalized vaccines for individuals, such as cancer treatment vaccines if the protein contains a newly synthesized antigen.

[0071] A further aspect of the present invention provides a method for creating (e.g., and / or designing) a diagnostic assay for determining whether a patient has or has had a past infection with cancer or a pathogen, the diagnostic assay being performed on a biological sample obtained from a subject and comprising identifying at least one protein of a pathogen or tumor that is predicted to contain a B cell epitope using a method according to any of the examples discussed above, the diagnostic assay comprising utilization or identification of the identified at least one protein and / or B cell epitope in the biological sample.

[0072] Thus, the present invention can be advantageously used to create diagnostic tests or assays by rapid and / or automated molecular target discovery. Query proteins identified as likely to contain true B-cell epitopes can be further analyzed in laboratory tests to create such diagnostic tests or assays, thereby significantly reducing the time required for test development compared to conventional laboratory methods.

[0073] The term “utilization,” as used herein, is intended to mean that at least one protein and / or a B-cell epitope on it is used in an assay for identifying an immune response (e.g., protective) in a patient. In this context, the identified protein and / or epitope within is a component of the assay, not a target of the assay.

[0074] An in vitro diagnostic assay may involve the identification of an immune system component in a biological sample that recognizes at least one epitope within an identified protein. Thus, the diagnostic assay may utilize at least one identified protein and / or at least one predicted epitope. Typically, the diagnostic assay contains at least one identified protein, protein subunit, and / or predicted epitope (e.g., a synthesized one). In a preferred embodiment, the immune system component may be a B cell. For example, the assay may involve the identification of an antibody or B cell that recognizes a predicted B cell epitope within the identified protein.

[0075] As an example of such a diagnostic application, a sample isolated from a patient, preferably a blood sample, may be analyzed for the presence of B cells or antibodies in the biological sample that recognize and bind to epitopes in identified proteins, which are identified as part of the present invention and included in the assay.

[0076] Suitable diagnostic assays, as will be recognized by those skilled in the art, may include enzyme-coupled immunosorbent spot (ELISPOT) assays, enzyme-coupled immunosorbent assays (ELISA), cytokine capture assays, intracellular staining assays, tetramer staining assays, microfluidic devices, lab-on-a-chips, microarrays, flow cytometry, CyTOF, proteomics, molecular sequencing, or limiting dilution culture assays.

[0077] In a method for creating a diagnostic test, the amino acid sequences of one or more proteins to be tested for the presence of BCE may be selected based on the desired response to be tested. For example, one or more source proteins may be one or more source proteins of any pathogen or virus (or fragment thereof), such as the SARS-CoV-2 virus. In such cases, the present invention may be used to create a diagnostic test for determining whether a patient has or has had a past infection with the SARS-CoV-2 virus and / or its variants and / or related viral species. However, as will be recognized by those skilled in the art, one or more source proteins may originate from any pathogen (e.g., any virus, parasite, bacterium, or cancer indication).

[0078] Further disclosed herein is a diagnostic assay for determining whether a patient has or has had a past infection with a pathogen, wherein the diagnostic assay is performed on a biological sample obtained from a subject, and the diagnostic assay comprises the utilization or identification in the biological sample of at least one protein of a pathogen or tumor predicted to contain a B cell epitope, identified using any of the methods discussed above. The diagnostic assay may also comprise the identification of an immune system component (e.g., a B cell) in the biological sample that recognizes at least one identified protein and / or at least one predicted epitope.

[0079] A further aspect of the present invention provides a computer program product that, when executed by a computer, includes instructions causing the computer to perform any of the methods described above. The present invention also provides a non-temporary computer-readable medium that, when executed by a computer, includes executable instructions causing the computer to perform any of the methods described above.

[0080] A further aspect of the present invention provides a system for predicting whether a protein contains a B cell epitope that is likely to trigger an antibody binding event, the system comprising at least one processor communicating with at least one memory device, the at least one memory device storing instructions for causing the at least one processor to perform a method according to any of the examples of the present invention considered above.

[0081] A further aspect of the present invention provides a method for synthesizing a protein, comprising predicting that the protein contains a B cell epitope that is likely to induce an antibody binding event by any of the examples discussed above, and synthesizing the predicted protein. The synthesis of the predicted protein can be carried out using techniques known in the art. A further aspect of the present invention provides a protein synthesized using the method.

[0082] Embodiments of the present invention may be advantageously used to identify one or more B cell epitopes that are predicted to induce a protective immunogenic response across multiple species of pathogens or viruses. Such “broad-spectrum protective” B cell epitopes may be used, for example, as vaccine elements or to provide diagnostic tests or assays.

[0083] Therefore, further herein are provided a method (e.g., computer-implemented) for identifying one or more B-cell epitopes that are predicted to evoke a protective immunogenic response across multiple pathogens or viruses (e.g., and / or variants thereof), (i) Performing a method relating to any of the examples described above that utilize candidate encodings, thereby identifying, for each of several proteins of each different pathogen or virus, several first B cell epitopes that are predicted to be potentially true B cell epitopes in at least one of the proteins; (ii) For each of the identified first B cell epitopes, determine the number of different species of pathogens or viruses that are predicted to be true B cell epitopes; and (iii) Classifying one or more of the first B cell epitopes as broad-spectrum protective B cell epitopes based on the number of different species in which the first B cell epitope is predicted to be a true B cell epitope, or the number of variants of any one given species. A method including the following is disclosed.

[0084] Thus, the present invention can be used to classify one or more B cell epitopes as those expected to induce a broad immunogenic response (e.g., cross-reactivity) across multiple different species or variants of pathogens or viruses. The classification is based on determining the number of different species of pathogens or viruses on which the B cell epitope is expected to be a true B cell epitope (e.g., a viable B cell epitope). For example, a first B cell epitope expected to be a viable B cell epitope on the maximum number of different species or variants tested may be classified as a broad-protective B cell epitope (e.g., may be classified as a vaccine candidate). In another example, if the number of species on which the B cell epitope is expected to be a true B cell epitope is greater than a predetermined threshold, the identified first B cell epitope may be classified as broad-protective.

[0085] Preferably, to reduce the computational load, one or more candidate codes may be mapped only to the receptor-binding domain (RBD) of the amino acid sequence of each protein being tested.

[0086] In some embodiments, the method applies to at least one (preferably each) of the first B cell epitopes classified as broad-area protective B cell epitopes. The method involves substituting one or more amino acids that form the first B cell epitope with different amino acids, thereby generating a modified first B cell epitope; For each of the multiple proteins of each of the multiple species of viral pathogens, To generate a modified amino acid sequence of a protein according to a modified first B cell epitope; Using a first trained machine learning model, predict whether a candidate code representing a first B cell epitope, classified as a broad-spectrum protective B cell epitope, is likely to correspond to a true B cell epitope on the modified amino acid sequence of the protein. It may further include the following.

[0087] Thus, the method of the present invention can be advantageously used to predict viable B cell epitopes that may not naturally exist on proteins under analysis. This can be advantageously used to identify broad-protective vaccine candidates that provide broad-protective responses across multiple different species (and / or variations thereof) of pathogens or viruses and may also be protective against future viral variants or pathogens that have not yet spread pathogenically in humans. Such identified B cell epitopes can form vaccine candidates or be used to create diagnostic tests or assays as outlined above.

[0088] The inventors have found that some of the modified B cell epitopes identified in this manner are predicted to generate broader immunogenic responses across a greater number of different virus or pathogen species compared to the most cross-reactive wild-type B cell epitopes.

[0089] Further analysis may be performed on identified modified B cell epitopes that do not exist naturally in viruses or pathogens to assess their structural stability. For example, any "mutant" B cell epitopes predicted to have low structural stability may be discarded from future analysis. Such structural analysis may be performed using techniques known in the art (e.g., computational).

[0090] Brief explanation of the drawing Here, embodiments of the present invention will be described with reference to the attached figures. [Brief explanation of the drawing]

[0091] [Figure 1] This is a flowchart illustrating the steps of a method according to one embodiment of the present invention. [Figure 2] This flowchart illustrates the steps of a method for training a first machine learning algorithm according to one embodiment of the present invention. [Figure 3] This flowchart illustrates the steps of a method for training a second machine learning algorithm according to one embodiment of the present invention. [Figure 4] An example of a system suitable for realizing an embodiment of this method is shown. [Figure 5] This is an example of a suitable server. [Figures 6a-6f] The correlation results between feature pairs are shown. [Figure 7] (a) Spearman correlation coefficients for continuous features of RSA, (b) UHSE, and (c) LHSE are shown. [Figure 8] (a) Kendall correlation coefficients for categorical features of RSA, (b) UHSE, and (c) LHSE are shown. [Figure 9] (a) Kendall correlation coefficients for continuous features of SS, (b) CBCE, and (c) LBCE are shown. [Figure 10] (a) Mutual information criteria for categorical features of SS, (b) CBCE, and (c) LBCE are shown. [Figure 11] The structure of a predictive model for RSA and HSE according to one embodiment of the present invention is schematically shown. [Figure 12a-f] The cross-validation results for an RSA / HSE model according to one embodiment of the present invention are shown. [Figure 13a-f] The test results of an RSA / HSE model according to one embodiment of the present invention are shown. [Figure 14a-f]The final training results of an RSA / HSE model according to one embodiment of the present invention are shown. [Figure 15a-f] The cross-validation (CV) results for the secondary structure (SS) prediction model according to the present invention are illustrated. [Figure 16a-b] The test results for the SS model are shown below. [Figure 17a-d] The final training results for an SS model according to one embodiment of the present invention are shown. [Figure 18a-b] A paratope analysis of CBCE is presented. [Figure 19a] A schematic example of the architecture of a first machine learning algorithm for predicting CBCE according to one embodiment of the present invention is provided. [Figure 19b] A schematic representation of the architecture of a first machine learning algorithm for predicting CBCE according to a further embodiment of the present invention is shown. [Figure 20a-d] This section presents an outlier analysis of CBCE. [Figure 21a-f] Further embodiments of the present invention show the results of the precision-recall metric for a first machine learning algorithm for predicting CBCE. [Figure 22a-f] The results of the precision-recall metric for a first machine learning algorithm for predicting CBCE according to one embodiment of the present invention are shown. [Figure 23a-b] The results of the precision-recall metric for a first machine learning algorithm for predicting CBCE according to one embodiment of the present invention are shown. [Figure 24a-b] The results of the precision-recall metric for a first machine learning algorithm for predicting CBCE according to one embodiment of the present invention are shown. [Figure 25a-c] The final training results for a first machine learning algorithm for predicting CBCE according to one embodiment of the present invention are shown. [Figure 26a-b] The final training results for a first machine learning algorithm for predicting CBCE according to one embodiment of the present invention are shown. [Figure 27a-d] This example illustrates outlier analysis using LBCE. [Figure 28] A pipeline according to one embodiment of the present invention is schematically illustrated. [Figure 29a-c] This example illustrates the successful mapping of receptor-binding domains (RBDs) to betacoronavirus species. [Figure 30] This example illustrates the prediction of a structural epitope that is predicted to be broadly immunogenic across multiple virus species. [Figures 31a-31b] This section illustrates the changes in predicted immunogenicity after the implementation of a mutation policy. [Modes for carrying out the invention]

[0092] Detailed explanation 1. Overview Embodiments of the present invention provide a B cell epitope (BCE) prediction algorithm ("First Machine Learning Algorithm" or "First Machine Learning Model") which is trained using a reference dataset designed so that a trained first machine learning model predicts B cell epitopes in their unbound state. This first machine learning algorithm is trained on an unbound three-dimensional Ag protein structure before an Ag-Ab binding event to learn the properties of BCEs on Ag before an Ag binding event in order to capture the ("true") BCE of a bone fide on an Ag protein, which is the query input to the model. In other words, this BCE prediction algorithm predicts the form of BCE that the antibody "sees" to trigger a binding event.

[0093] In this embodiment, the first machine learning model accurately predicts a BCE on a given Ag from its primary sequence combined with one or more three-dimensional structures and / or surface properties of Ag, without requiring the complete three-dimensional structure of Ag as input to the predictor. Since this model is trained using BLSTM, the entire Ag sequence can be treated as a single data point processed from both directions without the need for k-mer segmentation. Each amino acid in Ag is treated as a time step in the network, and its properties can be influenced by both preceding and succeeding amino acids adjacent to the amino acid in question, thus making it possible to discover contextual relationships between spatially distant amino acids that may be important parts of the steric BCE (CBCE).

[0094] The first machine learning model has the ability to discover distinct or independent CBCEs that may exist on a single Ag protein sequence. In a particularly advantageous embodiment, the trained model is designed to take as input an Ag protein sequence and binary "substitution vectors" ("encodings") representing each single candidate CBCE, as well as one or more three-dimensional structures and / or surface properties of Ag. The model's output is a single probability indicating the likelihood that the input substitution vector is a bone fide CBCE. In other words, the user asks the model whether a particular query CBCE, encoded as a binary substitution vector on a given Ag sequence, is a true CBCE. However, the model may also be used to provide a second output in the form of a probability vector containing probabilities for each amino acid in the Ag sequence. The second output can be seen as the contribution of each amino acid to the CBCE in question, and this can be used to compare the current method with conventional models that predict in an "amino acid-by-amino acid" manner.

[0095] The structural properties of the Ag sequence are important for predicting CBCE. However, embodiments of the present invention negate the need for the complete 3D structure of the protein sequence, which is experimentally measured or predicted and then used as input to the algorithm. The first machine learning algorithm does not require the coordinates of each atom of each amino acid in the 3D protein sequence, and instead can predict BCE using one or more structural and / or surface properties of the query protein, such as RSA, HSE, SS.

[0096] The most important features of "top-level" proteins such as RSA, HSE, and SS can typically be predicted using one or more trained machine learning algorithms ("second machine learning algorithms" or "second machine learning models"), taking the protein's amino acid sequence as input. This way, advantageously, the structure and / or surface properties of the protein under investigation can be calculated directly from the amino acid sequence without requiring any additional information supplied or input by the user. These second algorithms also utilize BLSTM networks and have been shown to have high performance.

[0097] In this way, the first and second machine learning models can work together to form a "pipeline" that requires only the amino acid sequence of the query protein as input to predict whether the query protein contains a true B cell epitope. A schematic diagram of such a pipeline constituting the first and second machine learning models according to one embodiment of the present invention is shown in Figure 28.

[0098] Figure 1 is a flowchart 100 outlining the steps of a preferred embodiment of the present invention. In step S101, the amino acid sequence of the protein to be analyzed for the presence of a B cell epitope is accessed. Typically, the protein under investigation includes an antigen. The amino acid sequence can be accessed using various techniques known to those skilled in the art, and for example, the sequence can be downloaded from a bioinformatics repository such as UniProt.

[0099] In step S103, one or more (e.g., three-dimensional) structural and / or surface properties of the unbound protein are accessed. Typically, these properties of the protein are predicted using one or more trained second machine learning models (schematically illustrated in 300 and described below) that take the amino acid sequence accessed in step S101 as input. The structural and / or surface properties typically include the secondary structure and relative solvent accessibility of the query protein. Hemispheric exposure (both upper and lower hemispheric exposure) may also be used. A value or category for each of these properties is typically assigned to each amino acid in the sequence. However, other techniques may be used to obtain the three-dimensional structure and surface properties of the query protein, such as database entries, X-ray crystallography, and computational approaches to predict the complete 3D protein folding structure (from which the structural and / or surface properties can be obtained).

[0100] In step S105, candidate codes for one or more candidate BCEs on the query protein are generated. Each candidate code is typically in the form of a binary vector, where each data element in the binary vector corresponds to an amino acid in the amino acid sequence that forms each candidate BCE. In such a binary “permutation vector” code, each amino acid in the sequence that forms part of the candidate BCE is assigned a “1”, and each amino acid in the sequence that does not form part of the candidate BCE is assigned a “0”. For example, in the case of the exemplary amino acid sequence “ABCDEFG”, a candidate conformational B cell epitope consisting of amino acids B, D, F, and G may be coded as the binary vector (0101011). In some embodiments, a candidate code may be generated for each possible candidate BCE on the protein. However, to reduce the computational load, the search space may be limited based on physical prior knowledge (e.g., knowledge of mutation sites on the nascent antigen of the input protein or the receptor-binding domain on the SARS-CoV-2 spike protein) or techniques such as deep reinforcement learning models or association-generative models.

[0101] In step S107, the amino acid sequence accessed in step 101, one or more structural and / or surface properties accessed in step 103, and the candidate codes generated in step S105 are input to a trained first machine learning model being trained using BLSTM. One or more biochemical properties assigned to each amino acid in the amino acid sequence may also be used as input to the trained first machine learning model. The output of the trained first machine learning model is the probability that each candidate code represents a true B cell epitope on the query protein.

[0102] Importantly, the first machine learning model is trained on unbound data. In other words, the first machine learning model is trained on a dataset containing reference proteins with B cell epitopes classified as true B cell epitopes, and the structure and / or surface properties of the reference proteins in an unbound state. Thus, the first machine learning model is trained to learn the relationship between one or more structure and / or surface properties of the reference proteins and the classified true B cell epitopes. Consequently, the trained first machine learning model predicts the presence of B cell epitopes on the query protein before a binding event.

[0103] Figure 2 is a flowchart 200 outlining the steps of a preferred embodiment for training a first machine learning model. In step S201, multiple protein complexes are accessed. These can be accessed from publicly available sources, for example, the Protein Databank (PDB) for CBCE or the Immune Epitope Database (IEDB) for LBCE.

[0104] In step S203, the protein complex accessed in step S201 is analyzed to define the true epitope and antigen. A linear BCE may be defined in the IEDB complex. Further analysis may be required to define the true conformational epitope from the protein complex accessed from the PDB, and further details of these techniques are described herein.

[0105] In step S205, each antigen defined in step S203 as having a true B cell epitope is mapped onto its amino acid sequence. In step S207, each true epitope is encoded onto the amino acid sequence of the corresponding antigen. In this way, multiple reference epitope codes corresponding to true epitopes are generated. The reference codes are typically encoded as binary vectors in the same manner as described above in step S105.

[0106] In step S209 of this method, the three-dimensional structure and / or surface properties of the unbound antigen are accessed, for example, by prediction from the amino acid sequence of the antigen obtained in step S205. Typically, the structure and / or surface properties of the antigen are predicted using a trained second machine learning model (schematically illustrated in 300). However, other techniques may be used to access the structure and / or surface properties of the unbound antigen, such as known computational approaches for predicting 3D protein structures. Typically, these properties include secondary structure, hemispheric exposure (both UHSE and LHSE), and relative solvent accessibility.

[0107] In step S211, a first reference dataset is generated. The first reference dataset includes the amino acid sequence of the reference antigen (step S205), the encoding of the true epitope on the corresponding reference antigen (step S207), and the accessed (e.g., predicted) structure and / or surface properties of the unbound reference antigen. In some embodiments, the first reference dataset may further include one or more physiological properties assigned to each amino acid of the reference antigen.

[0108] In step S213, a first machine learning model is trained using the first reference dataset generated in step S211. Here, the first machine learning model is or includes BLSTM and can be trained using techniques known in the art. By training the model using the first reference dataset generated by performing steps S201 to S211, the first machine learning model is trained to learn the relationship between the structure and / or surface properties of one or more first reference proteins in an unbound state and B cell epitopes that are classified as true B cell epitopes.

[0109] As discussed above in relation to Figures 1 and 2, in a preferred embodiment, one or more second machine learning models ( schematically illustrated in 300) are used to predict the structure and / or surface properties both when training the first machine learning model (Figure 2) and when predicting the presence of B cell epitopes on the target protein using the trained first machine learning model (Figure 1). The second machine learning models are trained to predict the structure and / or surface properties of the query protein in its unbound state. Thus, the first machine learning model is configured to predict the BCE in its unbound state, i.e., the three-dimensional form and properties that Ab "sees" that induce binding.

[0110] Figure 3 is a flowchart summarizing the steps of a preferred embodiment for training a second machine learning model according to an embodiment of the present invention.

[0111] In step S301, multiple second reference proteins are obtained in an unbound state (i.e., as a single protein structure). These second reference proteins may be obtained from publicly available databases such as the PDB.

[0112] In step S301, multiple unbound second reference proteins (i.e., single protein structures) are accessed. These second reference proteins can be accessed from publicly available databases such as the PDB.

[0113] In step S303, the amino acid sequence of the second reference protein is accessed.

[0114] In step S305, the three-dimensional structure and / or surface properties of the second reference protein are accessed. Typically, this includes secondary structure, relative solvent accessibility, and upper and lower hemisphere exposure. The secondary structure and RSA were calculated using the DSSP algorithm (Wolfgang Kabsch and Christian Sander. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22(12):2577-2637, 2018 / 11 / 30 December 1983), and the UHSE and LHSE were calculated using the BioPython package (Peter JA Cock, Tiago Antao, Jeffrey T Chang, Brad A Chapman, Cymon J Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, et al. Biopython: freely available python tools for computational molecular biology and bioinformatics. Bioinformatics, 25(11):1422-1423, 2009). DSSP and BioPython algorithms can be applied to a second reference protein structure file (e.g., obtained from the Protein Data Bank, PDB).

[0115] In step S307, a second reference dataset is generated using the amino acid sequence accessed in step S303 and the structure and / or surface properties of the second reference protein accessed in step S305. In some embodiments, the second reference dataset may further include one or more biochemical properties assigned to each amino acid of the second reference protein.

[0116] In step S309, a second machine learning model is trained using a second reference dataset (for example, to learn the relationship between one or more features of the amino acid sequence of a second reference protein and the corresponding structure and / or surface properties). Here, the second machine learning model is or includes BLSTM and may be trained using techniques known in the art. It will be recognized that different second machine learning models may be trained to predict different respective structures and / or surface properties. For example, one second machine learning model may be trained to predict RSA and HSE metrics, and a further second machine learning model may be trained to predict secondary structure.

[0117] The various steps outlined in Figures 1-3 are described in more detail in the following sections. Section 2 describes the pre-training techniques used in all models. Section 3 presents some preliminary analyses performed to select the input features of the models. Section 4 presents the RSA, HSE, and SS structure algorithms used in the current BCE algorithm. Section 5 presents the RSA, HSE, and SS structure algorithms used as features in the BCE model. Section 6 presents an example of the first machine learning algorithm according to the present invention used to predict CBCE, while Section 7 presents an overview of the first model used to predict linear BCE (LBCE). Section 8 presents the applications of the present invention.

[0118] 2. Pre-training techniques An autoencoder (aE) was used to ensure reproducible results and to avoid the need for random seeds in the network used in the model. An aE is a type of neural network trained on compressed data that reflects the input of the main model. More specifically, the AE receives the input at layer number i=0 and processes it through any number of layers that make up the encoder section, e.g., i=1, ..., N. It then processes it through a mirror structure of the encoder section called the decoder section. Finally, it returns an output identical to the input. Hidden layers can reduce or expand the dimensionality of the input.

[0119] Each layer of the main network is converted to an AE and trained with some compressed data. The pre-trained weights of the outer layers are used as constants for subsequent aEs. This is repeated until all main network layers are pre-trained. The pre-trained weights can then be used as the starting point for the main training procedure.

[0120] The following procedure was performed once, and the pre-trained weights were used in all cross-validation splits, testing of unknown data, and the final model.

[0121] First, 10,000 protein sequences, ranging in length from 50 to 1,000 amino acids, were randomly generated. The actual amino acids used were also randomly selected. Next, each layer of the neural network was pre-trained as an individual AE (Enhanced Aspect) on the entire generated data. Pre-training was performed for a maximum of 1,000 epochs for each AE, with the possibility of stopping early if the loss did not decrease after 10 epochs. The weights of the outer layers were copied to deeper AEs after pre-training and were kept constant during their pre-training as well. The final layer of the model (output) was not pre-trained at all.

[0122] This procedure was performed once, and the same pre-training weights were used for all validation steps. This procedure was faster, used significantly more data for pre-training, and used the same initial weights for all downstream analyses.

[0123] 3. Preliminary Analysis and Feature Selection Techniques A preliminary analysis was conducted to investigate how various features correlate with and with the target we want to predict. This analysis had two objectives. First, it aimed to identify highly relevant feature-target correlations that would help determine which features to use to predict the target. Second, feature-feature correlations help to subset the feature-target correlation space by preserving features that are not correlated with each other, thus reducing the input space for the model. However, it should be noted that everything analyzed using this selection method captures, in most cases, linear relationships between features and targets and between features.

[0124] 3.1. Method 1 Correlation analysis was performed using the features in Table 0 to determine which features to use as input for predicting different targets of the models. The analysis was then performed using the entire dataset for these models; that is, the datasets used for these analyses were those described in Section 4.1. This first analysis method was run for the 3D structure prediction model but not for the CBCE or LBCE models.

[0125] 3.1.1 Preprocessing of Continuous Features Continuous features provide a single value for each amino acid in the alphabet. To model the effect of neighboring amino acids in the protein sequence, the values ​​of each feature were averaged by scanning the protein sequence using a sliding window of 9 amino acid peptide lengths. The average feature value was assigned to the middle amino acid in the peptide. This was done for all features on each protein sequence across the entire training and test datasets.

[0126] 3.1.2 Correlation between continuous features and continuous targets These analyses were performed for RSA (relative solvent accessibility), UHSE (upper hemisphere exposure), and LHSE (lower hemisphere exposure). First, continuous features were preprocessed as described in Section 3.1.1. The values ​​of the target variables were used as is; that is, they were not averaged as they already represent proximity values. Spearman's correlation coefficient was calculated for all window-mean features and all target variables in each protein sequence. Then, the correlations from all proteins were averaged for each feature-target pair. Next, for all three targets, the 30 most correlated features (positive or negative) were selected, and features with high correlation to all three targets were chosen.

[0127] 3.1.3 Correlation between categorical features and continuous targets These analyses were performed for RSA, UHSE, and LHSE. Categorical features provide a single value for each amino acid. These features were used as is for each amino acid in any given sequence. Point bilinear correlation coefficients between features and targets were calculated for each class. Targets were also used as is; i.e., there was no window for the same reasons as in Section 3.1.2. The correlations from all proteins were then averaged for each feature-target pair. The features that correlated most strongly (positively or negatively) with all three targets were selected as inputs to the model.

[0128] 3.1.4 Correlation between continuous features and categorical targets This analysis was performed on SS. The same procedure as in Section 3.1.2 was used for the feature window. The only difference was that the target was categorical. Point bilinear correlation coefficients were used to assess the correlation with continuous features. The correlations from all proteins were then averaged for each feature-target pair. Top-level correlated features (positive or negative) were used in the model.

[0129] 3.1.5 Correlation between categorical features and categorical targets This analysis was performed on SS. Chi-squared values ​​were used for each protein to assess the categorical feature-target dependency. Multiple hypothesis correction of the resulting p-values ​​was then performed using the Benjamin-Hochberg procedure. Finally, the harmonic mean was used to average the corrected p-values ​​from all sequences for each feature-response pair. Top-class significant feature-target correlations were used in the model.

[0130] 3.2 Second Method Correlation analysis was performed using the features in Table 0 to determine which features to use as input for predicting different targets of the models. Further information on each feature (such as traits) is provided in the appendix. The analysis was performed using all the data for these models. That is, the datasets used for these analyses were those described in Section 4.1 (for RSA, UHSE, LHSE, and SS) and Sections 5.1 and 6.1 (for CBCE and LBCE). Therefore, which data was used depends on the model being tested. Furthermore, for the CBCE and LBCE analyses, these two datasets were merged.

[0131] [Table 1]

[0132] [Table 2]

[0133] 3.2.1 Preprocessing of Continuous Features The same procedure as in Section 3.1.1.

[0134] 3.2.2 Feature-to-Feature Correlation First, feature-feature correlation analysis was performed on all features in Table 0. This was done on two different datasets: one from the dataset in Section 5.1, and the other from the merged dataset from Sections 6.1 and 7.1. The purpose of these analyses was to identify highly correlated feature pairs so that if both features from a correlated pair correlated with the response in a later analysis, only one of them would be used as the input feature for the corresponding model. Thus, the input feature space was reduced.

[0135] First, continuous features were preprocessed as described in Section 3.2.1. Categorical features were used as is; that is, a single value for each amino acid was used for all sequences. Next, correlation metrics were calculated for all feature pairs on all sequences. Spearman's correlation coefficient was used for continuous-continuous feature pairs. Kendall's rank correlation coefficient was used for continuous-categorical features, and the mutual information criterion was used for categorical-categorical features. Outliers were removed from all pairwise feature coefficient populations using the IQR (box plot) method. The median of the entire population was then considered the final coefficient for all pairwise feature-feature correlations. Continuous-continuous feature pairs were defined as having a median Spearman correlation coefficient > 0.7, continuous-categorical feature pairs with a Kendall rank correlation coefficient > 0.7, and categorical-categorical feature pairs with mutual information > 2.

[0136] Figure 6 shows the feature-feature pair correlation results for the RSA / HSE model. Figures 6(a) to (c) show feature-feature correlations using data from 6.1 and 7.1. Figures 6(d) to (f) show feature-feature correlations using data from 5.1. Black dots correspond to correlated pairs. The heatmap is symmetrical. Note that not all feature names are listed on the x and y axes. For the datasets from 6.1 and 7.1, the current model was used to predict RSA, UHSE, LHSE, and SS features (see 4). For the dataset from 5.1, these figures were observed from the PDB. As can be seen, for all datasets, continuous-continuous feature pairs yielded many correlated features. There was very little correlation between continuous-categorical feature pairs, but no correlation was observed between categorical-categorical feature pairs.

[0137] 3.2.3 Characteristics-Response Correlation The same procedure was followed for each of the following feature-response correlation analyses. For continuous features, the same preprocessing as described in Section 3.1.1 was performed, but using windows of 0, 3, 7, 9, and 11. For each feature-response pair in all sequences of the corresponding data, correlations were calculated based on all windows. Furthermore, outliers were removed using the IQR (box plot) method, and then the median of each correlation group was calculated.

[0138] All correlations from all windows for each feature were merged into a single population, outliers were removed using the same method, and finally the median of the global population was recalculated. The final "pooled" median was used to rank the features correlated with the corresponding responses from highest to lowest. Categorical responses and predicted RSA, UHSE, and LHSE were used as is, i.e., without windows.

[0139] Each of the following analyses provided an index of highly correlated feature-response pairs. When two features correlated with the response and with each other (using the results from Section 3.2.3), we selected the top-level feature with the highest correlation to the response so that only the single feature with the highest correlation to the response was used.

[0140] 3.2.3.1 Correlation between continuous features and continuous targets These analyses were performed for RSA, UHSE, and LHSE. Data from 5.1 was used. The metric used here was Spearman's correlation coefficient. See Figure 7 for the results and Section 5 for the models and features used.

[0141] Figure 7 shows Spearman correlation coefficients for continuous features for (a) RSA, (b) UHSE, and (c) LHSE. In each figure, the x-axis represents the correlation, and the y-axis represents the feature names sorted from highest to lowest absolute correlation of the median of the population. Each plot is from all windows.

[0142] 3.2.3.2 Correlation between categorical features and continuous targets These analyses were performed for RSA, UHSE, and LHSE. Data from 5.1 was used. The metric used here was the Kendall rank correlation coefficient. See Figure 8 for the results and Section 5 for the models and features used.

[0143] Figure 8 shows Kendall correlation coefficients for categorical features for (a) RSA, (b) UHSE, and (c) LHSE. In each figure, the x-axis represents the correlation, and the y-axis represents the feature names sorted from highest to lowest absolute correlation of the median of the population. Each plot is from all windows.

[0144] 3.2.3.3 Correlation between continuous features and categorical targets These analyses were performed for SS, CBCE, and LBCE. Data from section 5.1 (for SS) and from sections 6.1 and 7.1 (for CBCE and LBCE) were used. The metric used here was the Kendall rank correlation coefficient. See Figure 9 for the results, and sections 5, 6, and 7 for the models and features used.

[0145] Figure 9 shows Kendall correlation coefficients for continuous features for (a) SS, (b) CBCE, and (c) LBCE. In each figure, the x-axis represents the correlation, and the y-axis represents the feature names sorted from highest to lowest absolute correlation of the median of the population. Each plot is from all windows.

[0146] 3.2.3.4 Correlation between categorical features and categorical targets These analyses were performed for SS, CBCE, and LBCE. Data from section 5.1 (for SS) and from sections 6.1 and 7.1 (for CBCE and LBCE) were used. The metric used here was the mutual information criterion. See Figure 10 for the results, and sections 5, 6, and 7 for the models and features used.

[0147] Figure 10 shows the mutual information criteria for categorical features for (a) SS, (b) CBCE, and (c) LBCE. In each figure, the x-axis is correlation, and the y-axis is the feature name sorted from highest to lowest absolute correlation of the median of the population. Each plot is from all windows.

[0148] 4. Prediction of 3D features ("Second Machine Learning Algorithm") Two BLSTM models were created to predict the structure and surface properties of proteins from their natural sequences. These models were later used in the first machine learning model to predict the structural and surface properties of Ag before the binding event with Ab occurs. For structural properties, we chose to create a model for SS, and for surface properties, we chose RSA, UHSE, and LHSE.

[0149] 4.1 Data preparation for 3D features To accurately predict SS, RSA, UHSE, and LHSE from native protein sequences, we used all available high-quality 3D protein structures from all available biological PDBs. The goal of these models was to predict the most appropriate surface and structural properties of a single protein, unaffected by binding interactions to any other protein, including Ab-to-B binding. Therefore, only structures not bound to other structures or molecules were retained. Structures containing two or more copies of the same molecule, but with slightly different conformations, were retained in the data. By filtering the structures and retaining only those with a resolution of ≤3 angstroms, we ensured that every atom of each amino acid was reliably mapped using coordinates and using protein chains longer than 200 amino acids.

[0150] After filtering, the database consisted of a total of 41,592 structures. These structures contained 70,489 protein sequences, resulting in 53,524 unique sequences. Subsequences of longer sequences were retained as separate data point entries because their shorter lengths may result in different 3D conformal folding characteristics.

[0151] The DSSP algorithm was used to calculate SS and RSA for each molecule in each structure file. DSSP calculates the following secondary structure classes for protein sequences: α-helix, 310-helix, π-helix, isolated β-bridge, β-strand, turn, bend, and coil. These classes were merged into three superclasses: helix (α-helix, 310-helix, and π-helix), strand (isolated β-bridge and β-strand), and coil (turn, bend, and coil). Finally, the BioPython package was used to calculate UHSE and LHSE.

[0152] At the end of filtering, each amino acid in the database was assigned RSA, UHSE, LHSE values, and SS class. To create a unique database, the RSA, UHSE, and LHSE values ​​for each amino acid were averaged across identical sequences. Finally, amino acids with identical sequences but different SS classes were assigned to a coil class.

[0153] 4.2 Predictive models for RSA and HSE (struct 3d rsa hse m2 model) We chose to create a single BLSTM model to predict all surface features. More specifically, this model predicts RSA, UHSE, and LHSE from the primary protein sequence. While LHSE may not provide useful information about the surface location of amino acids, both together form a stochastic metric around each amino acid, which can help predict UHSE more accurately.

[0154] This is a BLSTM model that takes a batch of features calculated for each amino acid from each input sequence as input and predicts a ternary output. For each protein sequence given as input, the values ​​of each RSA, UHSE, and LHSE are predicted for each amino acid.

[0155] For all three outputs, the individual losses were calculated based on the mean squared error (MSE), although the losses are not limited to this function in principle. The global model loss was the weighted sum of the individual losses, weighted 50, 100, and 125 for RSA, UHSE, and LHSE, respectively. The weights were determined by first training a model without them and then examining the difference in magnitude of the three losses. The weights were designed so that the three losses gave equal contributions to the global model loss.

[0156] Figure 11 shows a schematic, simplified architecture of the model. As shown, the first layer is the input layer. The input layer may have one or more input nodes to receive the input features, for example, as considered below. Each input node is coupled to its respective masking node in the masking layer. Similarly, each masking node is coupled to its respective BLSTM node in the BLSTM layer. Each BLSTM node is coupled to a concatenation node in the concatenation layer. Thus, multiple outputs from the nodes of the preceding BLSTM layers are concentrated and integrated in the concatenation layer. The concatenation layer may be coupled to one or more further BLSTM layers, each containing a corresponding BLSTM node. The final nodes of these BLSTM nodes are coupled to the output layer, which in this case contains multiple output nodes corresponding to the RSA, UHSE, and LHSE output predictions, respectively.

[0157] Note: The input features were calculated amino acid by amino acid without being averaged by a window.

[0158] 4.2.1 Preliminary analysis and characteristics used For the procedure, please refer to the procedure in Section 3.1. The 14 features (including the binary representation of protein sequences) selected for use in the model can be seen in Table 1.

[0159] [Table 3]

[0160] 4.2.2 Pre-training See section 2.1.

[0161] 4.2.3 Cross-Validation (CV) Results Approximately 80% of the data was used in a 5-fold cross-validation to evaluate the model's performance. See Figure 12 for the Spearman correlation coefficient metric. The model performed well without overfitting and exhibited high performance across all evaluation metrics. Furthermore, the model demonstrated high stability, with only slight variability observed between different cross-validation runs.

[0162] Figure 12 shows the results for the RSA / HSE model. Figures 12(a) to (c) show Spearman correlation coefficients, and 12(d) to (f) show mean squared error (MSE) loss. The x-axis is the number of epochs, and the y-axis is the metric value. The plots are the average of 5-fold cross-validation, including Gaussian CI. The blue line represents the training case, and the red line (dashed line) represents the validation set case.

[0163] 4.2.4 Test Results The remaining approximately 20% of the data was used as test data to evaluate the model's performance on completely unknown data. See Figure 13 for the Spearman correlation coefficient metric. The model performed well without overfitting and exhibited high performance across all evaluation metrics.

[0164] Figure 13 shows the results of the RSA / HSE model. (a)-(c): Spearman correlation coefficients. (d)-(f): MSE loss. The x-axis is the number of epochs, and the y-axis is the metric value. The solid line represents the training case, and the dotted line represents the validation set case.

[0165] 4.2.5 Final Training Final training was performed on the entire dataset, using the optimal number of epochs determined by the cross-validation procedure. See Figure 14 for the Spearman correlation coefficient metric. The model appears to perform well across all metrics. The model was trained for 70 epochs selected based on cross-validation and test set analysis.

[0166] Figure 14 shows the results for the RSA / HSE model. (a)-(c): Spearman correlation coefficients. (d)-(f): MSE loss. The x-axis is the number of epochs, and the y-axis is the metric value.

[0167] 4.3 Predictive model for SS (struct 3d ss m3 model) We chose to construct a BLSTM model to predict a three-layer class output for SS. The model uses categorical cross-entropy loss to assign each amino acid in its input sequence to one of the following classes: helix, strand, or coil. The model architecture is substantially identical to that illustrated in Figure 11, which has a single output node in the output layer. Note: Input features were calculated amino acid by amino acid because they are not averaged across the sliding window.

[0168] 4.3.1 Preliminary analysis and characteristics used Refer to the procedure in Section 3.1. The eight features chosen for use in the model (including the binary representation of protein sequences) can be seen in Table 2.

[0169] [Table 4]

[0170] 4.3.2 CV Results Approximately 80% of the data was used in a 5-fold cross-validation to evaluate the model's performance. See Figure 15 for the high-accuracy recall metric. The model appears to perform well without significant overfitting, giving high performance across all metrics. Furthermore, only slight variations in loss are observed between different cross-validation runs. Finally, for demonstration purposes, the model was trained for 70 epochs, exhibiting slight overfitting from epoch 60 onward.

[0171] Figure 15 illustrates the results of the SS model. Figures 15(a) to (e) show the PR curves. The x-axis is TPR and the y-axis is PPV. Figure 15(f) shows the categorical cross-entropy loss. The x-axis is the number of epochs and the y-axis is the loss value. The plot is the average of 5-fold cross-validation, including Gaussian CI. The blue line represents the training case and the red line (dashed line) represents the validation set case.

[0172] 4.3.3 Test Results The remaining approximately 20% of the data was used as test data to evaluate performance on completely unknown data. See Figure 16 for the high-precision recall metric. The model performed well without significant overfitting and showed high performance for all evaluation metrics. The model was trained for 100 epochs, selected based on the CV analysis, so that it required slightly more epochs than the CV analysis.

[0173] Figure 16 shows the results of the SS model. Figure 16(a): PR curve. The x-axis is TPR and the y-axis is PPV. Figure 16(b): Categorical cross-entropy loss. The x-axis is the number of epochs and the y-axis is the loss value. The solid line represents the training case and the dotted line represents the validation set case.

[0174] 4.3.4 Final Training Final training was performed on the entire dataset, using the optimal number of epochs determined by the cross-validation procedure. See Figure 17 for the F1 metric. The trained final model performed well without significant overfitting and demonstrated high performance across all metrics. The model was trained for 100 epochs, selected based on the cross-validation analysis, with a slightly greater number of epochs than the CV and test set analysis.

[0175] Figure 17 shows the results of the SS model. Figures 17(a)-(c): F1 metric. The x-axis is the number of epochs, and the y-axis is the F1 value. Figure 17(d): Categorical cross-entropy loss. The x-axis is the number of epochs, and the y-axis is the loss value.

[0176] 5. Prediction of 3D features ("Second Machine Learning Algorithm") (Version 2) 5.1 Data preparation for 3D features (version 2) This is identical to Section 4.1. The only difference is that there was one incorrect assignment of classes to the SS classes. Specifically, the isolated β-bridge class was assigned to the coil superclass, but here it is correctly moved to the strand superclass. This minor detail only affects a portion of the SS model data.

[0177] 5.2 RSA and HSE Prediction Model (Version 2) (RSA HSE M4 V1 Model) This section is identical to Section 4.2, with only the preliminary analysis and input features being modified. The model is also deeper. The model architecture is substantially the same as that illustrated in Figure 11. Note: Input features were calculated amino acid by amino acid without averaging across the sliding window.

[0178] The global model loss was a weighted sum of the individual models, as in the previous model. However, this time the weights were 1, 10, and 125 for RSA, UHSE, and LHSE, respectively. The weights were determined by first training a model without it and observing the difference in the magnitude of the three losses.

[0179] 5.2.1 Preliminary analysis and characteristics used For the procedure, please refer to Section 3.2. The 19 features (including binary representations of protein sequences) selected for use in the model can be seen in Table 3.

[0180] [Table 5]

[0181] 5.2.2 Pre-training See section 2.2.

[0182] 5.2.3 CV Results Approximately 80% of the data was used with 5-fold cross-validation to evaluate the model's performance.

[0183] 5.2.5 Test Results The remaining approximately 20% of the data was used as test data to verify performance with completely unknown data.

[0184] 5.2.6 Final Training The final training was performed on the entire dataset, using the optimal number of epochs determined by the cross-validation procedure.

[0185] 5.3 SS Prediction Model (Version 2) (SS3 M6 V1 Model) The results are similar to those described in Section 4.3. The model architecture is substantially identical to that illustrated in Figure 11. Note: Input features were calculated amino acid by amino acid without being averaged by a window.

[0186] 5.3.1 Outlier This is identical to Section 4.3.

[0187] 5.3.2 Preliminary analysis and characteristics used For the procedure, please refer to Section 3.2. The 19 features (including binary representations of protein sequences) selected for use in the model can be seen in Table 4.

[0188] [Table 6]

[0189] 5.3.3 Pre-training See section 2.2.

[0190] 5.3.4 CV Results Approximately 80% of the data was used with 5-fold cross-validation to evaluate the model's performance.

[0191] 5.3.5 Test Results The remaining approximately 20% of the data was used as test data to verify performance with completely unknown data.

[0192] 5.3.6 Final Training The final training was performed on the entire dataset, using the optimal number of epochs determined by the cross-validation procedure.

[0193] 6. The First Machine Learning Model (CBCE) A BLSTM-based model was used to predict CBCE. This allowed for bidirectional modeling of the entire protein sequence as a single observation without the need for segmentation. Thus, remote and anterior-posterior amino acid relationships were captured by the model. Furthermore, the use of BLSTM enabled simultaneous training of different protein lengths. The primary objective of the model was to create a non-ab-specific CBCE predictor. This was achieved by the training data preparation method described in Section 6.1.

[0194] 6.1 Data Preparation To model CBCE, we downloaded non-old protein complexes from the PDB. We allowed any resolution and biological structure as long as they had at least three different protein chains. This was because two of the chains were Ab's V H and V L This is because it can be a chain, and the third chain can be Ag. Naturally, multiple Ab or Ag atoms can exist in a single PDB structure.

[0195] A local database was created using all the V, D, and J region genes of immunoglobulins from the IMGT (International ImMunoGeneTics Information System) database. V genes were collected from all available organisms, namely humans, mice, rhesus monkeys, rabbits, and rats. H and V L I downloaded both genes. IMGT is the V of Ab. H and V L This provides information about the chains and their paratope regions (the CDR [antibody complementarity determination region] and FR [antibody framework region] of each chain).

[0196] To identify Ab in this local database, IgBlast (Jian Ye, Ning Ma, Thomas L Madden, and James M Ostell. Igblast: an immunoglobulin variable domain sequence analysis tool. Nucleic Acids Res, 41 (Web Server issue): W34-40, Jul 2013) was used for proteins in the IMGT database. To identify paratopes in the chain, the Kabat system provided by IgBlast was used (G Johnson and TT Wu. Kabat database and its applications: 30 years after the first variability plot. Nucleic acids research, 28(1):214-218, 01 2000). V was only identified if at least CDR1 and CDR2 were found by IgBlast. H and V L The chains were considered valid. Since CDR3 is more difficult to map, we allowed for its absence in the search. Then, all protein chains of each structure were aligned to their database. At least one chain was considered valid. H As and one valid V L If it was not mapped as such, the structure was filtered and removed. VH Even if V L All other chains that were not mapped were assumed to be Ag chains. They were longer than 100 amino acids and V H Chains and V L A chain that is not bound by any other chains was considered an effective Ag chain. Finally, at least one V H chain, V L Only the chains and structures containing the Ag chain were used for further analysis.

[0197] Next, in each structure, V H Chain and V L The effective Ab was reconstructed by pairing the chains. Multiple V molecules were found in a single structure. H Chain and V L If a chain exists, each V H From all atoms on the chain, each V L The average distance to all atoms on the chain was measured. V has the minimum average atomic distance. H Chain and V L We assigned the chains as pairs and assumed they belonged to a single Ab. Standalone V H Chain or V L The chain was not considered in downstream analysis because a complete Ab could not be defined. Paratope analysis provided information on contact formation with each Ag. Two amino acids were assumed to be in contact if either of their atoms was located within a certain probe distance from each other. Using probe distances of 4, 6, and 8 angstroms, the total number of Ag amino acids forming contact with each paratope moiety was calculated. Figure 18(a) shows the normalized mean of those contacts (x axis represents amino acids, y axis represents paratope moieties). The majority of contacts were V H CDR1 and CDR2 and V L V performed on CDR1 and CDR3 HThe absence of CDR3 above may be due to the difficulty in identifying it using IgBlast. Furthermore, the FR region does not appear to form as many contacts as the CDR region, as expected. In addition, the relatively low standard deviation of the total number of contacts per amino acid and paratope in Figure 18(b) suggests that a similar number of contacts occur between different Ag and Ab. This suggests the possibility of predicting CBCE without any further information about the actual Ab. Figure 18(b) shows the standard deviation of the number of contacts. The x-axis represents amino acids and the y-axis represents paratopes.

[0198] Each V H and V L For a chain pair of V, a single CBCE was defined. This is for any V H and V L This was done by first identifying any Ag atom whose distance from the CDR region of the chain pair was ≤4 angstroms. The amino acids to which those atoms belonged were defined as contacts between Ag and Ab, i.e., as CBCEs. Multiple CBCEs from different Abs may be defined on the same Ag. Finally, structures that did not define any CBCEs within a 4-angstrom distance were discarded.

[0199] Observed CBCEs were also mapped to similar Ag. Undiscovered CBCEs may increase the false negative rate in the predictive model. To reduce the likelihood of assigning undiscovered CBCEs as negative data, observed CBCEs were copied to similar Ag. First, clusters of similar Ag were identified using BlastPlus with >90% similarity and a maximum gap of 2. CBCEs were copied from Ag within a cluster to all other Ag within the same cluster, as long as the corresponding location and distance were exactly identical based on the mapping from BlastPlus.

[0200] Finally, a unique Ag local database was created. Duplicate Ag sequences were completely removed from the data. On the other hand, Ag sequences that are subsequences of longer Ag sequences were retained in the data and treated as different observations. The resulting database consisted of 1003 Ag sequences and 12968 CBCEs in FASTA format, of which 6986 were unique. Each Ag sequence was associated with at least one CBCE.

[0201] 6.2 Summary of the CBCE Model We created two main CBCE predictors, namely model 13 conf v1 and model 17 conf v1. The models have two main differences. First, the negative data generators are different; see Section 6.3 for details. Second, model 13 conf v1 gives two outputs, while model 17 conf v1 gives one output. Both models take permutation vectors as input features. Every sequence in the data is associated with at least one true CBCE, and these CBCEs are converted into binary 2D vectors and given as input to the models. The common output of the two models is a probability, as both models essentially ask the question, "Is this permutation vector a true CBCE on this particular sequence?" "Is this permutation vector a true CBCE on this particular sequence, or not?" The second output of model 13 conf v1 is the permutation vector itself. This part of the model acts as an AE, returning a probability for each amino acid. This output can be seen as the contribution of each amino acid in the sequence to the specific CBCE in question.

[0202] The objective of these models was to predict the CBCE on the protein Ag sequence before the Ab binding event occurred. The dataset used, described in Section 6.1, contains linear protein sequences and true CBCEs. Therefore, there is no information regarding the structural or surface properties of these protein sequences. However, information regarding the secondary structure and surface appearance of each protein sequence before the binding event is needed. Accordingly, predictive models for RSA, UHSE, and LHSE (model struct 3d rsa hse m2 res), as well as SS (model struct 3d ss m3 res1), described in Section 4, were used to predict their features for all proteins in the dataset.

[0203] The model differs from existing publicly available CBCE algorithms in three main ways. Firstly, by using BLSTM, it becomes possible to capture the relationships between distant and preceding / preceding amino acids that can constitute CBCE on the natural folded 3D protein structure. Other algorithms, on the other hand, segment the protein sequence into k-mers and therefore do not capture these important relationships. Secondly, the training dataset is constructed to predict true CBCE before the binding event occurs. This difference conceptually brings the model closer to modeling the reality of Ab binding. The 3D folded protein structure is, strictly speaking, what Ab recognizes and triggers binding, not what Ab has already recognized and bound to.

[0204] Thirdly, existing publicly available CBCE algorithms require experimentally measured or potentially predicted 3D protein Ag structures to predict epitopes. The first machine learning model does not require the 3D coordinates of all atoms of each amino acid on Ag or Ab. Finally, the model addresses the epitope question in a conceptually very different way, but in a way that is more finely tuned to capture bone fide ("true") BCEs. Because it predicts the probability of standalone CBCEs on each Ag as pairs or independent classes, multiple CBCEs on the same protein sequence can be discovered and evaluated independently. The other algorithms, on the other hand, provide amino acid-by-amino acid probabilities without the ability to separate different CBCEs.

[0205] Figure 19(a) schematically shows the architecture of the model 13 conf v1 model. The model architecture is substantially identical to that shown in Figure 11. Here, the output layer has two output nodes. The BCE amino acid output (BCE_aa) is the actual permutation output using binary cross-entropy loss. The BCE perm output is a probability vector indicating whether the input permutation is a true CBCE (second position on the vector) or not (first position on the vector). Figure 19(b) shows the architecture of the model 17 conf v1 model. BCE perm represents the same output as the previous model. Note: Input features were calculated amino acid by amino acid without averaging by a window.

[0206] For both models, the BCE perm output is calculated from amino acid values. The final layer of the output is a dense layer containing sigmoid activation. As a result, for each protein sequence, a value ∈[0,1] is returned for each amino acid. The two-class probability vector is then calculated for that sequence as follows:

number

[0207] The model 13 conf v1 model had a weighted global loss. The global model loss is the weighted sum of the individual model losses, i.e., the weighted sum of BCE perm and BCE aa, using weights 1 and 1, respectively. Similarly, it is possible to test various weights.

[0208] 6.3 Negative Data When attempting to model CBCE amino acids individually, positive data would be all amino acids in a sequence that are part of any true CBCE, and negative data would be any other amino acids on the same sequence. However, since we model the entire CBCE as a single permutation vector of a given protein sequence, the response to this vector is single probability (this is the BCE perm output from Section 6.2). Since we observed only true positive CBCEs from the data in Section 6.1, there are no permutations corresponding to negative data.

[0209] We developed a solution to improve the model's performance. In doing so, we generated negative permutations of data to resample as close to ground truth as possible. To cover a wide range of negative data and make the model more robust, we generated negative permutations for all sequences in the data at every epoch during training. The following sections describe two different generation schemes.

[0210] 6.3.1 CBCE Model (model 13 conf v1) This model generated completely random permutations. For each sequence in the training and validation data, 10 completely random permutations were generated, and it was assumed that they were not true CBCEs. Both the total number of amino acids in a given sequence and their arrangement were random. A new permutation was generated in each epoch.

[0211] The advantage of this method is that, since it is completely random, any generated CBCE permutation has a relatively low probability of being a false negative. However, the disadvantage is that the randomly generated CBCE permutation may be very different from the true CBCE permutation.

[0212] 6.3.2 CBCE model (model 17 conf v1) Three different types of negative data generation methods were applied to this model. For each array of training data and validation data, 30 complete random permutations, 10 from each method, were generated and assumed that they were not the true CBCE. The first method is the same as that described in 6.3.1. The second and third methods were applied to all true CBCEs of a given protein sequence. The second method kept the first and last amino acids of a given true CBCE in the correct positions, while randomly shuffling the internal CBCE amino acid indices within the region. The third method kept the total amount of amino acids in the true CBCE and their linear distances constant. Then, the entire true CBCE on the other part of the protein was randomly shifted. New permutations were generated from all methods in each epoch.

[0213] The advantage of this method is that the randomly generated CBCE permutation covers both very similar true CBCE permutations and very different true CBCE permutations. This will probably lead to the algorithm being more robust to both positive and negative data. On the other hand, the disadvantage of this method is that there is a possibility of an increase in false negatives, leading to a decrease in performance with new data.

[0214] 6.4 Outlier Outliers were removed before any training of any model. This was done by applying an Isolation Forest (IF) to the total amino acids of each observed CBCE with data from Section 5.1. The algorithm was run with all observations of total amino acids as the max samples argument with the IF algorithm from the sklearn Python package (F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit - learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825 - 2830, 2011.). The most common minimum and maximum bounds from all runs were considered the global bounds for inliers.

[0215] Figure 20 shows the total amount of amino acids from all observed CBCEs. Figures 20(a) and 20(b) show the density and box plot of the observed amounts of total amino acids in the true CBCE, respectively. Figures 20(c) and 20(d) show the logarithmic scale of Figures 20(a) and 20(b), respectively. Here, it can be seen that the median is 10 amino acids. After outlier analysis, it was found that the valid CBCEs should contain the total amount of amino acids within a smaller range than those considered in Section 1.3. However, in these tests, no detection or removal of outliers from the observed CBCEs was performed. As can be seen from Figure 20, the maximum amount of total amino acids before removing outliers was approximately 30 to 35, which is consistent with what was reported in previous tests. Finally, outliers were removed from the data and the model was trained without them.

[0216] 6.5 Preliminary analysis and characteristics used See Section 3.2 for the procedure. The 14 features selected for use in the model (including binary representations of protein sequences and permutation vectors) are shown in Table 5. Note that RSA and SS were predicted by the models struct 3D rsa hse m2 res and struct 3D ss m3 res1, respectively, as described in Section 4. The same features were used in both model 13 conf v1 and model 17 conf v1.

[0217] [Table 7]

[0218] 6.6 Pre-training See section 2.2.

[0219] 6.7 CV results The "age" of protein Ag in the data was defined as the date the most recent CBCE was registered in the protein sequence. To evaluate the model's performance, approximately 94% of the "oldest class" proteins in the data were used in a 5-fold cross-validation. Custom callbacks were used in TensorFlow to allow for early termination if the loss did not decrease.

[0220] 6.7.1 Model model 13 conf v1 Figure 21 shows the results for the precision-recall metric. The model performed well without significant overfitting and exhibited high performance for all evaluation metrics. Furthermore, only slight variability in loss was observed between different CV runs. The model was trained for approximately 466 epochs, and the apparent bump in loss may suggest that the random generator produces negative CBCE permutations similar to positive ones. However, this negative impact diminished as the epochs progressed.

[0221] Figure 21 illustrates the results of CBCE model 13 conf v1. Figures 21(a)-(e): PR curves. The x-axis is TPR and the y-axis is PPV. Figure 21(f): Weighted cross-entropy loss. The x-axis is the number of epochs and the y-axis is the loss value. The plot shows the average of 5-fold cross-validation, including Gaussian CI. The blue line represents the training case and the red line (dashed line) represents the validation set case.

[0222] 6.7.2 Model model 17 conf v1 Figure 22 shows the results for the precision-recall metrics. The model appears to have performed well without significant overfitting before 250 epochs, giving high performance across all metrics. Furthermore, only slight variability is observed between different CV runs of loss. The model was trained for 455 epochs.

[0223] Figure 22 illustrates the results of CBCE model 17 conf v1. Figures 22 (a)~(e): PR curves. The x-axis is TPR and the y-axis is PPV. Figure 22(f): Weighted cross-entropy loss. The x-axis is the number of epochs and the y-axis is the loss value. The plots are the average of 5-fold cross-validation, including Gaussian CI. The blue line represents the training case and the red line (dashed line) represents the validation set case.

[0224] 6.8 Test Results To examine performance with completely unknown data, we used approximately 6% of the "latest" proteins in the data. The reason for retaining the latest data as a test set is to use the same data for comparison with other algorithms. This ensures that those algorithms have not been trained on that data. Training of the following models was performed on approximately 94% of the "oldest class" of data.

[0225] 6.8.1 Model model 13 conf v1 Figure 23 shows the results for the precision-recall metric. The model performed well without significant overfitting and showed high performance for all evaluation metrics. The model was trained for 466 epochs, which was selected based on the CV analysis to be slightly more epochs than the CV analysis.

[0226] Figure 23 illustrates the results for CBCE model 13 conf v1. Figure 23(a): PR curve. The x-axis is TPR and the y-axis is PPV. Figure 23(b): Overall model loss. The x-axis is the number of epochs and the y-axis is the loss value. The solid line represents the training case and the dotted line represents the validation set case.

[0227] 6.8.2 Model 17 conf v1 Figure 24 shows the results for the precision-recall metrics. The model appears to perform well without significant overfitting and gives high performance across all metrics. The model was trained for 455 epochs, which was chosen based on the CV analysis to be slightly more epochs than the CV analysis.

[0228] Figure 24 illustrates the results for CBCE model 17 conf v1. Figure 24(a): PR curve. The x-axis is TPR and the y-axis is PPV. Figure 24(b): Overall model loss. The x-axis is the number of epochs and the y-axis is the loss value. The solid line represents the training case and the dotted line represents the validation set case.

[0229] 6.9 Final Training The final training was performed on the entire dataset, using the optimal number of epochs determined by the cross-validation procedure.

[0230] 6.9.1 Model model 13 conf v1 Figure 25 shows the results of the F1 metric. The model appears to be performing well without significant overfitting and gives high performance in all metrics. The model was trained for 500 epochs selected based on CV analysis.

[0231] Figure 25 shows the results of CBCE model 13 conf v1. Figures 25(a)-(b): F1 metric. The x-axis is the number of epochs and the y-axis is the F1 value. Figure 25(c): Overall model loss. The x-axis is the number of epochs and the y-axis is the loss value.

[0232] 6.9.2 Model model 17 conf v1 Figure 26 shows the results of the F1 metric. The model appears to be performing well without significant overfitting and gives high performance in all metrics. The model was trained for 700 epochs selected based on CV analysis.

[0233] Figure 26 shows the results of CBCE model 17 conf v1. Figure 26(a): F1 metric. The x-axis is the number of epochs and the y-axis is the F1 value. Figure 26(b): Overall model loss. The x-axis is the number of epochs and the y-axis is the loss value.

[0234] 7 The First Machine Learning Model (LBCE) To predict LBCE, a BLSTM model is used similarly. This makes it possible to model the entire protein sequence as one observation without the need for segmentation. Therefore, long-range amino acid relationships should be captured by the model. Moreover, the use of BLSTM should make it possible to train different protein lengths simultaneously. Therefore, the model type here is the same as the CBCE model type in Section 6. The main purpose of the model was to create a non-Ab specific LBCE predictor. This is achieved by the method of preparing the training data as described in Section 7.1.

[0235] 7.1 Data Preparation For LBCE modeling, I downloaded LBCE data from IEDB. I downloaded all non-old assays from all organisms from IEDB.

[0236] The primary objective of the model was to create a non-ab-specific LBCE predictor, so all positive LBCEs were considered for the database. That is, an LBCE was included in the database if it tested positive for at least one Ab. Next, each Ag ID was mapped to the Universal Protein Resource (UniProt) to obtain the protein sequence in fasta format. Assays were discarded if the ID was unmappingable, or if the ID was mappingable but the coordinates of the corresponding LBCE did not give the same sequence on the searched Ag sequence.

[0237] Observed LBCEs were mapped to similar Ags, as was done for CBCEs in Section 6.1. Undiscovered LBCEs may increase the false negative rate in the predictive model. To reduce the likelihood of assigning undiscovered LBCEs as negative data, observed LBCEs were copied to similar Ags. First, clusters of similar Ags were identified using BlastPlus with >90% similarity and a maximum gap of 2. LBCEs were copied from Ags within a cluster to all other Ags within the same cluster, as long as the corresponding linear coordinates were exactly identical based on the mapping from BlastPlus.

[0238] Finally, a unique Ag database was created. Duplicate Ag sequences were completely removed from the data. On the other hand, Ag sequences that are subsequences of longer Ag sequences were retained in the data and treated as different observations. The resulting database consisted of 4695 Ag sequences and 177782 CBCEs in fasta format, of which 63363 were unique. Each Ag sequence was associated with at least one LBCE.

[0239] 7.2 Overview of the LBCE Model Every sequence of data is associated with at least one true LBCE. The model takes permutation vectors as input features, which are LBCEs, converted to binary 2D vectors, and given as input to the model. The model's output is a probability, as it essentially asks "is this permutation vector a true LBCE on this particular sequence?". Essentially, the permutation vectors are identical to those described in Section 6.2, the only difference being that they represent LBCEs instead of CBCEs.

[0240] The goal of these models was to predict LBCEs before the binding event occurred. The dataset used is described in Section 7.1 and contains linear protein sequences and true LBCEs. Therefore, there is no information about the structural or surface properties of those protein sequences. However, information about what the surface of each protein sequence looks like before the binding event is needed. Therefore, to predict their features for all proteins in the dataset, we used the RSA predictive model (model struct 3d rsa hse m2 res) described in Section 4.

[0241] The model differs from publicly available LBCE algorithms in three main ways. Firstly, by using BLSTM, it is possible to capture the relationships between distant amino acids that can constitute an LBCE. Other algorithms, on the other hand, segment the protein sequence and thus cleave such bonds. Secondly, the training dataset is designed to predict a true LBCE before the binding event occurs—one that Ab recognizes and triggers binding, not one that Ab has already recognized and bound to. Finally, to predict the probability of a standalone LBCE, it can discover and separate multiple LBCEs on the same protein sequence. Other algorithms, on the other hand, provide amino acid-by-amino acid probabilities without the ability to separate different LBCEs.

[0242] The architecture of the model 12 linear v1 model has substantially the same structure as the model depicted in Figure 19(b). The bce output is a probability vector indicating whether the input permutation is a true LBCE (second position on the vector) or not (first position on the vector). The bce output is calculated from amino acid values ​​as described in Section 6.2. Note: Input features were calculated amino acid by amino acid without averaging by a window.

[0243] 7.3 Negative Data A procedure similar to that in Section 6.3.1 was performed. The only difference here was that it was ensured that the randomly generated LBCEs would not be cleaved. Let N be the length of a given protein sequence. First, the total amount X of amino acids in a random LBCE was generated from a uniform distribution in [0,N]. Next, the starting position i of this LBCE was generated from a uniform distribution in [0,NX]. Finally, all amino acids in [i,i+X] were assigned to correspond to this random LBCE. The total amount of randomly generated LBCEs, 10 in total, was the presequence generated for each epoch.

[0244] 7.4 Outlier Outliers were removed before training any of the Model 12 linear v1 models. Following the exact same procedure as in Section 6.4, the data used was the total amount of each observed LBCE amino acid in the data from Section 7.1.

[0245] Figure 27 shows the total amount of amino acids from all observed LBCEs. Here, we can see that the median is 17 amino acids. After outlier analysis, we found that effective LBCEs should contain a total amount of amino acids within the range [5,30]. Outliers were ultimately removed from the data, and the model was trained without them.

[0246] Figure 27 shows the outlier analysis of LBCE. Figures 27(a) and (b) show box plots of the density and observed amounts of total amino acids in true LBCE, respectively. Figures 27(c) and (d) show the logarithmic scales of Figures 27(a) and 27(b), respectively.

[0247] 7.5 Preliminary analysis and characteristics used See Section 3.2 for the procedure. The 13 features chosen for use in the model (including the binary representation of the protein sequence and permutation vectors) can be seen in Table 6. Note that RSA was predicted by the model struct 3d rsa hse m2 res described in Section 4.

[0248] [Table 8]

[0249] 8. Applications Precise prediction of B cell epitopes (particularly 3D conformational B cell epitopes) will lead to significant improvements in diagnosis, drug design, and vaccine development in the fields of autoimmunity, infectious diseases, and cancer. Examples are provided below.

[0250] 8.1 Therapeutic Antibody Mapping Antibody binding events to antigens are crucial in activating humoral B-cell immune responses to any pathogenic threat, including cancer antigens in malignant tumor cells, as well as viral and bacterial infections. B-cell epitope mapping is essential for therapeutic antibody development. The prediction of true B-cell epitopes according to this invention not only provides a functional understanding of critical residues involved in antibody-antigen binding, but also aids in the selection of therapeutic antibodies against predicted epitopes that are preferred for further development as therapeutic antibodies.

[0251] 8.2 Vaccine Development In antibody-driven vaccine design, in silico prediction is performed to predict BCEs that can trigger a humoral immune response. Previous in silico-guided vaccine design initiatives tend to selectively apply BCE prediction by focusing on surface exposure sites within the antigen. However, by using the approach of the present invention (particularly by training on unbound protein structures and using candidate BCE codes ("permutation vectors") mapped to antigen protein sequences), BCEs can be identified with greater accuracy and guide Ab-based vaccine design with much higher fidelity. In addition to potentially generating long-lasting and potent universal immunity, BCE vaccines guided by the approach of the present invention can advantageously identify specific antibody-epitope interactions on a large scale that have the ability to evade undesirable immune responses. Moreover, the method described herein avoids the need for years of target discovery and laborious, costly, and time-consuming work with minimal cost and time.

[0252] 8.3 Immunological diagnosis and immunological monitoring Predicting B cell epitopes directly contributes to the design of therapeutic strategies and immunodiagnostic and immunomonitoring reagents. Diagnosis can be significantly accelerated by integrating predictive approaches for accurate and comprehensive detection of BCEs. One example is the rapid and accurate detection of BCE epitopes on the spike protein of betacoronavirus, enabling COVID diagnosis in a way that can distinguish between infections caused by common cold viruses and those caused by the SARS-CoV-2 virus that causes COVID. Following the same approach, BCE predictive techniques can be used to identify immunodominant or humorally reactive epitopes in tumor-associated or nascent antigens in immunodiagnostic and immunomonitoring clinical applications.

[0253] 8.4 System Example Figure 4 schematically illustrates an example of a system suitable for realizing an embodiment of the present method. The system 1100 comprises at least one server 1110 that communicates with a reference data store 1120. The server may also communicate with an automated peptide synthesis device 1130, for example, via a communication network 1140.

[0254] In a particular embodiment, the server may first use, for example, a reference data store to obtain the amino acid sequences of one or more query proteins. The server may then predict whether the query protein contains BCEs that are likely to trigger an antibody binding event (e.g., an immunogenic response).

[0255] A query protein predicted to contain one or more BCEs may be sent to an automated peptide synthesis device 1130 for the synthesis of the query protein or a portion thereof. Techniques for automated peptide synthesis are well known in the art, and it will be understood that any known technique may be used. Typically, the query protein or epitope is synthesized using standard solid-phase peptide synthesis chemistry, purified using reverse-phase high-performance liquid chromatography, and then formulated in aqueous solution. When used for vaccination, the peptide solution is usually mixed with an adjuvant before being administered to the patient.

[0256] Peptide synthesis technology has existed for over 20 years, but it has improved rapidly in recent years, and now, with commercial machines, synthesis can be completed in just a few minutes. For brevity, I will not describe such machines in detail, but their operation will be understood by those skilled in the art, and such conventional machines can be adapted to receive candidate regions or epitopes from a server.

[0257] The server may include the aforementioned functionality for identifying query proteins that are predicted to contain one or more viable BCEs. Naturally, it will be understood that such functionality can be subdivided across different processing entities and different processing modules communicating with each other within a computer network.

[0258] Techniques for identifying BCE-containing proteins can be integrated into a broader ecosystem for customized vaccine development. Exemplary vaccine development ecosystems are well-known and have been described at a high level in the art; however, for brevity, these ecosystems will not be described in detail here.

[0259] In an exemplary ecosystem, the first sampling step might involve isolating DNA from tumor biopsies and matching healthy tissue controls. In the second sequencing step, the data is sequenced and variants, i.e., mutations, are identified. In the immunoprofiler step, the relevant mutant peptides may be generated "in silico".

[0260] Using the relevant mutant peptides and the techniques described herein, protein candidates can be predicted, selected, and target epitopes identified for vaccine design. Specifically, candidate peptide sequences are selected based on their predicted binding affinity determined using the techniques described herein.

[0261] Next, the target epitope is synthesized using the conventional techniques described above. Before administration, the peptide solution is usually mixed with an adjuvant before being administered to the patient (vaccinated). Alternatively, the target epitope can be incorporated into DNA or RNA, or into the genome of bacteria or viruses, by engineering, as in the case of any conventional vaccine.

[0262] The proteins predicted by the methods described herein may also be used to produce other types of vaccines besides peptide-based vaccines. For example, the protein (or the predicted epitope therein) may be encoded in a corresponding DNA or RNA sequence and used to vaccinate a patient. Note that DNA is typically inserted into a plasmid construct. Alternatively, the DNA can be incorporated into the genome of a bacterial or viral delivery system (which may also be RNA, depending on the viral delivery system) that can be used to vaccinate a patient, thus yielding a vaccine produced in a genetically engineered virus or bacterium that produces the target after immunization in the patient, i.e., in vivo.

[0263] A suitable example of a server 1110 is shown in Figure 5. In this example, the server includes at least one microprocessor 1200, memory 1201, an optional input / output device 1202, e.g., a keyboard and / or display, and an external interface 1203, all interconnected via a bus 1204 as shown. In this example, the external interface 1203 can be used to connect the server 1110 to peripheral devices, e.g., a communication network 1140, a reference data store 1120, other storage devices, etc. Although a single external interface 1203 is shown, this is for illustrative purposes only, and in practice, multiple interfaces using various methods (e.g., Ethernet, serial, USB, wireless, etc.) may be provided.

[0264] During use, the microprocessor 1200 enables the execution of necessary processing, including communicating with the reference data store 1120 to receive and process input data, and / or communicating with a client device to receive sequence data of one or more query proteins and generating immunogenicity potential predictions in accordance with the method described above, by executing instructions in the form of application software stored in memory 1201. The application software may include one or more software modules and may be executed in a suitable execution environment, such as an operating system environment.

[0265] Therefore, it will be recognized that server 1200 may be formed from any suitable processing system, such as a suitably programmed client device, PC, web server, network server, etc. In one particular example, server 1200 may be a standard processing system, such as an Intel architecture-based processing system that runs software applications stored on non-volatile (e.g., hard disk) storage, but this is not required. However, it will also be understood that the processing system may be any electronic processing device, such as firmware optionally associated with a logic implementation such as a microprocessor, microchip processor, logic gate configuration, FPGA (Field Programmable Gate Array), or any other electronic device, system, or configuration. Thus, the term “server” is used for illustrative purposes only and is not intended to be limiting.

[0266] Although server 1200 is presented as a single entity, it will be recognized that it can be distributed across multiple geographically dispersed locations, for example, by using a processing system and / or database 1201 provided as part of a cloud-based environment. Therefore, the configuration described above is not mandatory, and other suitable configurations can be used.

[0267] 9. Methodological Case Studies of Mutation Policy Generation Herein, embodiments of the present invention may be used to identify one or more B-cell epitopes that are predicted to induce a protective immunogenic response across multiple pathogens or viruses. For this analysis, unique permutations were randomly generated from previous distributions for each sequence and RBD combination. SARS, SARS-CoV-2, and MERS receptor-binding domains (RBDs) were aligned onto available spike proteins from 304 different betacoronavirus species.

[0268] Figures 29(a)-(c) show successful examples of RBD mapping to 304 betacoronavirus species. Species are divided by subgenus group on the X-axis, and the Y-axis plots the total number of species for which RBD alignment was successful.

[0269] Next, we selected each permutation vector from a given RBD region of each spike protein sequence. For each RBD type (SARS, SARS-CoV-2, MERS), we performed one random generation, using the same permutation for each sequence. From the random permutation generation of these alignments, the following results were revealed: 1. 450,000 permutation vectors were created for the SARS RBD. 2. 450,000 permutation vectors were created for the SARS-CoV-2 RBD. 3. A permutation vector of 450,000 was created for the MERS RBD. These permutations were used in various cases where the corresponding RBDs were aligned.

[0270] 9.1. Statistics of prediction results on all sequences (all RBDs of all types): Total predictions made for each RBD (note that there will be differences in the totals because not all species have all RBDs, and some RBDs will result in fewer permutations): Permutations are generated from prior knowledge about the RBD region.

[0271] [Table 9]

[0272] 9.2. Immunodominance permutation vectors and epitopes are revealed from data analysis. Based on the above data for positive results (confirmed BCE score > 0.9), we were able to capture robust structural motifs or B-cell immunodominant structural epitopes (broad immunogenicity across most virus species tested). These are plotted in Figure 30. Here, the BCE predictor is clearly demonstrated to have the ability to capture latent structural motifs shared across numerous RBD species, i.e., immunodominant structural motifs. In Figure 30, each panel is an RBD. The filtering of conformational epitopes used in this plot was BCE >= 0.9. The y-axis is the total number of unique species, and the x-axis represents a single permutation vector (structural motif). Thus, each column can be considered a single permutation vector shared among betacoronavirus species counted on the Y-axis. The X-axis is sorted in descending order of total unique species. The coloring of the bars is based on subgenus.

[0273] The permutation vectors shown in the columns of Figure 30 are structural representations of epitopes predicted by the BCEP predictor. Next, amino acid presentations were created for these immunodominant (broadly immunogenic) epitopes, and the sequence logos of the top 1 permutations (including ties) were calculated for each RBD. The logos were created by taking the highest-permutation column (permutation) from Figure 30 above, and the sequence logos for those epitopes were created based on a positive BCE prob > 0.9.

[0274] This was created by extracting the highest column (permutation vector) from Figure 30 and generating a sequence logo for its epitope based on a positive BCE prob > 0.9. It was also found that the shared permutation vectors consist of shared repeat amino acid sequences that capture broadly immunogenic B cell epitope amino acid sequences that are dominant across most species.

[0275] 9.3. Realization of mutation policies using a B cell epitope predictor Next, we proceeded to integrate the BCEP predictor into an in silico mutation policy in order to 1) identify more potent BCE epitopes and 2) identify B cell epitopes predicted by the model to be even more immunogenic across more species than those analyzed. We demonstrated the mutation policy method here by mutating the most common motif in each RBD and then carried out the following steps: 1. In silico mutation, each amino acid at each position of the motif is replaced with any other known acid. 2. For each in silico mutation, the permutation vector (candidate epitope) is re-predicted for all spike proteins from 304 betacoronavirus species using the new mutation motif, and the total number of species that are positive (BCE>0.9) for that mutation motif is counted.

[0276] The essential mutation is designed to be broadly immunogenic against a panel of 304 betacoronavirus species. The original, unmutated counterpart was chosen because it was shared across most species, and the mutation policy, as seen in Figure 31(a), captures an additional 27 species (with respect to predicted immunogenicity) in the most successful mutation, with the highlighted bar 310 representing the mutation being found to be positive in more species compared to the original counterpart 311. When this mutated spike protein is applied under vaccination conditions, it can be inferred that the immunodominant epitope within it will generate an antibody response that may be potentially broadly immunogenic and protective. The mutation policy results illustrated in Figures 31(a) and (b) are for demonstration purposes only and can be applied to the broadly shared permutation vector and all three pathogenic RBD motifs across 304 species.

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Claims

1. A computer implementation method for predicting whether a protein contains a B cell epitope that may trigger an antibody binding event, (a) obtaining one or more structures and / or surface properties of the protein; and (b) Predict whether the protein contains a true B cell epitope by inputting the one or more structural and / or surface properties of the protein into a first trained machine learning model. In a method including the above, the first machine learning model is To generate a first reference dataset, wherein the first reference dataset is (i) Multiple first reference proteins, each containing at least one B cell epitope classified as a true B cell epitope; and (ii) One or more structural and / or surface properties of each first reference protein in an unbound state. Including; and Training the first machine learning model using the first reference dataset to learn the relationship between the structure and / or surface properties of the first reference protein in an unbound state and the B cell epitopes that are classified as true B cell epitopes. A method of training.

2. The method according to claim 1, wherein the output of the trained first machine learning model is a probability indicating whether the protein contains a true B cell epitope.

3. The method according to claim 1 or 2, wherein the one or more structures and / or surface properties include one or more of the secondary structure of the protein; the relative solvent exposure, RSA; and the hemispheric exposure, HSA.

4. The one or more structural and / or surface properties of each first reference protein are Obtaining the amino acid sequence of the first reference protein; and Predicting one or more structures and / or surface properties by applying one or more second machine learning models to the aforementioned amino acid sequence. Predicted by, The method according to any one of claims 1 to 3, wherein one or more second machine learning models are trained on a second reference dataset comprising multiple amino acid sequences and their corresponding structures and / or surface properties in an unbound state for each second reference protein.

5. At least a portion of the first reference protein is (i) Obtain multiple protein complexes containing at least three different protein chains; (ii) By filtering the plurality of protein complexes, the effective heavy chain (V) of the antibody is extracted. Ｈ Protein chains mapped as chains, effective light chains of antibodies (V Ｌ To retain only those protein chains that are mapped as a chain, and protein chains that are mapped as an antigen; (iii) The above V Ｈ Chain and V Ｌ Pairing the chains into a V shape Ｈ Chain and V Ｌ Forming chain pairs; and (iv) Each V Ｈ Chain and V Ｌ For each chain pair, define the true B cell epitope on the corresponding antigen. Obtained by; The method according to any one of claims 1 to 4, wherein at least a portion of the first reference protein in the first reference dataset corresponds to an antigen mapped in step (ii) that includes at least one true B cell epitope defined in step (iv).

6. Obtaining the amino acid sequence of the aforementioned protein; and The method involves generating one or more candidate encodings for each of the one or more candidate B cell epitopes on the protein, wherein each candidate encoding represents each of the candidate B cell epitopes as a plurality of data elements corresponding to amino acids in the amino acid sequence. It further includes; The method according to any one of claims 1 to 5, wherein the input to the trained first machine learning model includes the amino acid sequence of the protein, one or more structures and / or surface properties of the protein, and one or more candidate encodings.

7. The first reference dataset mentioned above is The amino acid sequences of each first reference protein; and A plurality of reference encodings for each true B cell epitope, wherein each reference encoding represents the respective true B cell epitope as a plurality of data elements corresponding to the amino acids in the amino acid sequence. The method according to claim 6, further comprising:

8. The method according to claim 6 or 7, wherein the output of the first machine learning model is the probability that each of the candidate encodings represents a true B cell epitope on the protein.

9. The method according to any one of claims 1 to 8, wherein the first machine learning model includes a long short-term memory network, LSTM, preferably a bidirectional long short-term memory network, BLSTM.

10. The method according to any one of claims 1 to 9, wherein the B cell epitope is a three-dimensional B cell epitope.

11. The method according to any one of claims 1 to 10, wherein the protein comprises an antigen.

12. Identifying a first B-cell epitope that is predicted to be a true B-cell epitope on the aforementioned protein; Substituting one or more amino acids that form the identified first B cell epitope with different amino acids to generate a modified amino acid sequence of the protein; and By using the pre-trained first machine learning model, predict whether the candidate encoding representing the identified first B cell epitope is likely to correspond to the true B cell epitope on the modified amino acid sequence of the protein. The method according to any one of claims 6 to 11, as dependent on claim 6, further comprising:

13. A method for creating a vaccine, Predicting whether a protein contains a B cell epitope by the method according to any one of claims 1 to 12; and Synthesizing the protein and / or the predicted B cell epitope, or converting at least one of the protein, the predicted B cell epitope, a B cell epitope variant, a predicted or simulated B cell epitope variant into a corresponding protein, peptide, DNA, or RNA sequence. A method that includes this.

14. A method for preparing a diagnostic assay performed on a biological sample taken from a subject to determine whether a patient has or has had cancer or a history of pathogen infection or tumor, comprising identifying at least one protein of the pathogen predicted to contain a B cell epitope using the method according to any one of claims 1 to 12; A method comprising the use of the identified at least one protein and / or B cell epitope or its identification within the biological sample.

15. A method for training machine learning models, The process involves generating a reference dataset, wherein the reference dataset is Multiple first reference proteins, each containing at least one B cell epitope classified as a true B cell epitope; and One or more structural and / or surface properties of each first reference protein in an unbound state. Including; and Training the machine learning model using the reference dataset to learn the relationship between the structural and / or surface properties of one or more of the first reference proteins in an unbound state and the corresponding B cell epitopes that are classified as true B cell epitopes. A method that includes this.

16. The aforementioned reference dataset is The amino acid sequence of each reference protein; and A plurality of reference encodings for each true B cell epitope, wherein each reference encoding represents the respective true B cell epitope as a plurality of data elements corresponding to the amino acids in the amino acid sequence. The method according to claim 15, further comprising:

17. A computer program product that, when executed by a computer, includes an instruction causing the computer to perform the method according to any one of claims 1 to 16.

18. Use of a machine learning model trained using the method described in claim 15 or 16.

19. A machine learning model trained using the method described in claim 15 or 16.

20. A system for predicting whether a protein contains a B cell epitope that may trigger an antibody binding event, the system comprising at least one processor communicating with at least one memory device, wherein the at least one memory device stores instructions for the at least one processor to perform the method according to any one of claims 1 to 12.

21. A method for synthesizing a protein, wherein the method according to any one of claims 1 to 12 predicts that the protein contains a B cell epitope that is likely to trigger an antibody binding event; To synthesize the aforementioned protein and A method that includes this.

22. A protein synthesized using the method of claim 21.

23. A method for identifying one or more B cell epitopes that are predicted to induce a protective immunogenic response across multiple species of pathogens or viruses, (i) Performing the method according to any one of claims 6 to 11 on each of a plurality of proteins of a plurality of different species of pathogen or virus, thereby identifying a plurality of first B cell epitopes on at least one of the plurality of proteins that are each predicted to be likely to be true B cell epitopes; (ii) For each identified first B cell epitope, determine the number of different species of pathogens or viruses for which the first B cell epitope is predicted to be a true B cell epitope; (iii) Classifying one or more of the first B cell epitopes as broad-spectrum protective B cell epitopes based on the number of different species or the number of variants of any one given species in which the first B cell epitope is predicted to be a true B cell epitope. A method that includes this.

24. For at least one of the first B cell epitopes classified as a broad-area protective B cell epitope, The first B cell epitope is formed by substituting one or more amino acids with different amino acids, thereby generating a modified first B cell epitope; For each of the multiple proteins of each of the multiple species of viral pathogens, To generate the modified amino acid sequence of the protein according to the modified first B cell epitope; Using the trained first machine learning model, predict whether a candidate code representing the first B cell epitope, which has been classified as a broad-spectrum protective B cell epitope, is likely to correspond to a true B cell epitope on the modified amino acid sequence of the protein. The method according to claim 23, further comprising:

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