Machine learning techniques in protein design for vaccine production

Abstract

One or more data objects defining a plurality of wild-type amino acid sequences are received. A plurality of dimensionality reduction sequences are generated in a dimensionality reduction space from one or more data objects. A plurality of candidate sequences are generated in a dimensionality reduction space using the plurality of dimensionality reduction sequences. One or more data objects defining a viral amino acid sequence are received. A virus sequence in a dimensionality reduced space is received. Each of the candidate sequences and at least one of the dimensionality-reduced viral sequences are provided as inputs to a titer predictor. A candidate score for each candidate sequence is received as an output of the slave titer predictor. At least one candidate sequence is selected from the candidate sequences. And generating at least one new amino acid sequence. Each generated amino acid sequence is suitable for the manufacture of a corresponding vaccine.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 319,700 filed on March 14, 2022 and U.S. Provisional Application No. 63 / 319,692 filed on March 14, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the use of machine learning techniques in vaccine design. Background Art

[0003] Machine learning (ML) is the use of computer algorithms that can automatically improve through experience and use of data. It is considered a part of artificial intelligence. Machine learning algorithms build models based on sample data (called training data) in order to make predictions or decisions without being explicitly programmed. Machine learning algorithms are widely used in fields such as medicine, email filtering, speech recognition, and computer vision where it is difficult or infeasible to develop traditional algorithms to perform the required tasks.

[0004] A vaccine is a biological preparation that provides acquired immunity for a specific infectious disease. Vaccines typically contain a substance similar to the pathogenic microorganism, and are usually made of a weakened or killed form of the microorganism, its toxins, or one of its surface proteins. The substance stimulates the body's immune system to recognize the substance as a threat, destroy the substance, and further recognize and destroy any microorganisms associated with the substance that it may encounter in the future. Vaccines can be preventive (to prevent or improve the effects of future infections with natural or "wild" pathogens) or therapeutic (to combat diseases that have already occurred, such as cancer). Some vaccines provide complete sterilizing immunity, in which infection is completely prevented. Summary of the invention

[0005] The strains used for seasonal influenza vaccines are currently and almost universally selected by public health authorities. Based on the observation of immune responses in animal models and human studies, these selections are carried out every year. However, the H3N2 vaccine using the strains recommended by public health authorities, for example, has not been enough to cause widespread protection in the general population in the past 5 years. In addition, during this time frame, public data show that immune correlation has split into different clades, each of which is protective to itself, and the protection to other clades may be limited. The present disclosure provides a solution to this problem. The embodiment described in the present disclosure provides an algorithm that can produce influenza (or other) antigens used as vaccines. In one embodiment, this can include: 1) Generate a reduced dimensionality space for all wild-type hemagglutinin sequences using machine learning (e.g., a variational autoencoder architecture) using two steps: a. Variably embedded in a parsimonious space, e.g., the model predicts the mean and variance from the input sequence, and the embedded coordinates are chosen from a normal distribution with the predicted mean and variance. b. Then, decode back to the original sequence according to the parsimonious spatial position “autoencoder” loss function, simplifying by the similarity of the input and output sequences. 2) Train an immune response prediction model based on the positions of the antigen (vaccine candidate) and the readout strain (target sequence) in the reduced dimensionality space [Input: antigen and readout embedded by the model from step 1, Output: measurement of immune response, such as antibody titer]. 3) Sampling candidate vaccine component representatives from the parsimonious space, ranking the candidate vaccine component representatives by predicted performance against the target sequence using the model described in step 2, and identifying the top candidates. 4) The top candidate representatives are decoded [using the model from step 1b] to generate hemagglutinin sequences that may or may not have been observed in the original wild-type set. In an example experiment, the algorithm was used to optimize the HA1 sequence of the H3 hemagglutinin (positions 16 to 345), and then the wild-type signal peptide and HA2 regions were transplanted to generate a complete hemagglutinin sequence. An exemplary modified antigen sequence starting from A / Singapore / INFIMH-16-0019 / 2016 is provided with the mutated residues indicated in bold:

[0006] A system of one or more computers may be configured to perform specific operations or actions by software, firmware, hardware, or a combination thereof installed on the system, which software, firmware, hardware, or a combination thereof causes the system to perform these actions when in operation. One or more computer programs may be configured to perform specific operations or actions by including instructions, which, when executed by a data processing device, cause the device to perform these actions. A general aspect includes a dimensionality reduction method for generating an amino acid sequence, which is performed by a system of one or more computers. The method includes receiving one or more data objects defining a plurality of wild-type amino acid sequences. The method also includes generating a plurality of dimensionality reduction sequences in a dimensionality reduction space from one or more data objects, in which: each dimensionality reduction sequence contains corresponding data of at least one of the wild-type amino acid sequences, the dimensionality reduction space having a lower dimension than the wild-type amino acid sequence, and the plurality of dimensionality reduction sequences define the distribution of values ​​along each dimension of the dimensionality reduction space. The method also includes generating a plurality of candidate sequences in the dimensionality reduction space using a plurality of dimensionality reduction sequences. The method also includes receiving one or more data objects defining a viral amino acid sequence. The method also includes generating at least one dimensionality reduction viral sequence in the dimensionality reduction space. The method also includes providing each of the candidate sequences and at least one of the reduced-dimensional viral sequences as input to the potency predictor. The method also includes receiving the candidate score of each candidate sequence as output from the potency predictor. The method also includes selecting at least one candidate sequence from the candidate sequences. The method also includes generating at least one new amino acid sequence for each selected candidate sequence. The method also includes providing at least one generated amino acid sequence. The method also includes an operation in which each of the generated amino acid sequences is suitable for manufacturing a corresponding vaccine, and the corresponding vaccine may include at least one of the groups that may include the following: i) a protein defined by the generated amino acid sequence, ii) a nucleic acid capable of producing a protein defined by the generated amino acid sequence, and iii) a delivery vehicle capable of producing a protein defined by the generated amino acid sequence. Other embodiments of this aspect include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each of which is configured to perform the actions of these methods.

[0007] Embodiments may include one or more of the following features. The method includes the following operations, in which generating multiple reduced dimensionality sequences may include creating a representative of a wild-type amino acid sequence using a variational autoencoder that predicts the mean and variance values ​​of the input data. Each reduced dimensionality sequence includes a corresponding set of values, and generating multiple candidate sequences in the reduced dimensionality space may include sampling the distribution of the values ​​of the multiple reduced dimensionality sequences. The valence predictor is configured to: receive i) a first sequence in the reduced dimensionality space and ii) a second sequence in the reduced dimensionality space as input; and provide a valence score (as a candidate score) as an output, which defines a measure of the biological response between the first sequence and the second sequence. Selecting at least one candidate sequence as a selected candidate sequence may include selecting n candidate sequences with the highest candidate score. The method includes an operation in which the value of n is 1, thereby selecting a single candidate sequence. The method includes an operation in which the value of n is greater than 1, thereby selecting multiple candidate sequences. Selecting at least one candidate sequence as a selected candidate sequence may include selecting a candidate sequence with a corresponding candidate score greater than a threshold. Each generated amino acid sequence is different from any wild-type amino acid sequence. At least one of the candidate sequences is in a plurality of reduced dimensionality sequences. The corresponding vaccine is directed against one of the group which may include: i) influenza, ii) human rhinovirus, iii) hiv and iiiv) coronavirus disease. Implementations of the described technology may include hardware, methods or processes, or computer software on a computer accessible medium.

[0008] A general aspect includes a system for generating an amino acid sequence, which may include a computer memory. The system also includes one or more processors. The system also includes a computer memory storing instructions that, when executed by the processor, cause the processor to perform operations that may include: receiving one or more data objects defining a plurality of wild-type amino acid sequences; generating a plurality of reduced-dimensionality sequences from the one or more data objects in a reduced-dimensionality space, wherein: each reduced-dimensionality sequence contains corresponding data for at least one of the wild-type amino acid sequences, the reduced-dimensionality space having a lower dimension than the wild-type amino acid sequence, and the plurality of reduced-dimensionality sequences define a distribution of values ​​along each dimension of the reduced-dimensionality space, and generating a plurality of candidate sequences in the reduced-dimensionality space using the plurality of reduced-dimensionality sequences; receiving one or more data objects defining a viral amino acid sequence; generating at least one reduced-dimensionality viral sequence in the reduced-dimensionality space. ; providing each of these candidate sequences and at least one of these reduced-dimensionality viral sequences as input to a potency predictor; receiving a candidate score for each candidate sequence as output from the potency predictor; selecting at least one candidate sequence from the candidate sequences; generating at least one new amino acid sequence for each selected candidate sequence; and providing at least one generated amino acid sequence, wherein each of the generated amino acid sequences is suitable for making a corresponding vaccine, the corresponding vaccine comprising at least one of the group consisting of: i) a protein defined by the generated amino acid sequence, ii) a nucleic acid capable of producing the protein defined by the generated amino acid sequence, and iii) a delivery vehicle capable of producing the protein defined by the generated amino acid sequence. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of these methods.

[0009] Embodiments may include one or more of the following features. A system for generating multiple reduced dimensionality sequences may include creating a representative of a wild-type amino acid sequence using a variational autoencoder that predicts the mean and variance values ​​of input data. Each reduced dimensionality sequence includes a corresponding set of values, and generating multiple candidate sequences in the reduced dimensionality space may include sampling the distribution of the values ​​of the multiple reduced dimensionality sequences. The valence predictor is configured to: receive i) a first sequence in the reduced dimensionality space and ii) a second sequence in the reduced dimensionality space as input; and provide a valence score (as a candidate score) as output, which defines a measure of the biological reaction between the first sequence and the second sequence. Selecting at least one candidate sequence as the selected candidate sequence may include selecting n candidate sequences with the highest candidate score. Embodiments of the described technology may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0010] A general aspect includes a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising: receiving one or more data objects defining a plurality of wild-type amino acid sequences; generating a plurality of reduced-dimensionality sequences in a reduced-dimensionality space from the one or more data objects, wherein: each reduced-dimensionality sequence contains corresponding data of at least one of the wild-type amino acid sequences, the reduced-dimensionality space having a lower dimension than the wild-type amino acid sequence, and the plurality of reduced-dimensionality sequences define a distribution of values ​​along each dimension of the reduced-dimensionality space, and generating a plurality of candidate sequences in the reduced-dimensionality space using the plurality of reduced-dimensionality sequences; receiving one or more data objects defining a viral amino acid sequence; generating at least one candidate sequence in the reduced-dimensionality space; A reduced dimensionality viral sequence; providing each of these candidate sequences and at least one of these reduced dimensionality viral sequences as input to a potency predictor; receiving a candidate score for each candidate sequence as output from the potency predictor; selecting at least one candidate sequence from the candidate sequence; generating at least one new amino acid sequence for each selected candidate sequence; and providing at least one generated amino acid sequence, wherein each of the generated amino acid sequences is suitable for manufacturing a corresponding vaccine, the corresponding vaccine comprising at least one of the group consisting of: i) a protein defined by the generated amino acid sequence, ii) a nucleic acid capable of producing the protein defined by the generated amino acid sequence, and iii) a delivery vehicle capable of producing the protein defined by the generated amino acid sequence. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each of which is configured to perform the actions of these methods.

[0011] Embodiments may include one or more of the following features. A medium generating multiple reduced dimensionality sequences may include a variational autoencoder that uses the mean and variance values ​​of the predicted input data to create a representative of a wild-type amino acid sequence. Each reduced dimensionality sequence includes a corresponding set of values, and generating multiple candidate sequences in the reduced dimensionality space may include sampling the distribution of the values ​​of the multiple reduced dimensionality sequences. The valence predictor is configured to: receive i) a first sequence in the reduced dimensionality space and ii) a second sequence in the reduced dimensionality space as input; and provide a valence score (as a candidate score) as output, which defines a measure of the biological reaction between the first sequence and the second sequence. Embodiments of the described technology may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0012] Also disclosed herein are vaccine compositions comprising any of the plurality of amino acid sequences generated by the methods described herein.

[0013] Also disclosed are vectors, fusion proteins, and cells comprising one or more peptides and / or proteins produced according to the methods described herein.

[0014] Also disclosed herein are methods of eliciting an immune response in a subject, comprising administering one or more of the isolated nucleic acids, peptides and / or proteins described herein, thereby eliciting an immune response in the subject.

[0015] In one aspect, disclosed herein are methods of inhibiting viral infection, comprising administering to a subject any of one or more of the isolated nucleic acids, peptides and / or proteins described herein, or administering any vaccine comprising any of the isolated nucleic acids, peptides and / or proteins described herein.

[0016] Also disclosed herein is a method for making a subject immune to influenza virus, the method comprising administering to the subject an immune effective amount of a vaccine composition as disclosed herein. Also disclosed herein is a vaccine composition as disclosed herein for use in a method for making a subject immune to a virus (e.g., influenza virus). Also disclosed herein is a vaccine composition as disclosed herein for the manufacture of a medicament for use in a method for making a subject immune to a virus (e.g., influenza virus). In certain embodiments, the method prevents a subject from viral infection (e.g., influenza virus infection), and in certain embodiments, the method causes a protective immune response (e.g., HA antibody response and / or NA antibody response) in the subject. In certain embodiments, the subject is a person, and in certain embodiments, the vaccine composition is administered intramuscularly, intradermally, subcutaneously, intravenously or intraperitoneally.

[0017] Another aspect of the disclosure relates to a method for alleviating one or more symptoms of a viral infection (e.g., influenza virus infection), the method comprising administering to a subject a preventive effective amount of a vaccine composition disclosed herein. Also disclosed herein is a vaccine composition as disclosed herein for use in a method for alleviating one or more symptoms of a viral infection (e.g., influenza virus infection). Also disclosed herein is a vaccine composition as disclosed herein for use in a method for alleviating one or more symptoms of an infection (e.g., influenza virus infection) for the manufacture of a medicament as disclosed herein for use in a method for alleviating one or more symptoms of an infection (e.g., influenza virus infection).

[0018] In various embodiments, the methods and compositions disclosed herein treat or prevent disease caused by one or both of a seasonal viral strain or a pandemic viral strain (eg, a seasonal influenza strain or a pandemic influenza strain).

[0019] In certain embodiments of the methods disclosed herein, wherein the subject is a human, the human is 6 months or older, less than 18 years old, at least 6 months and less than 18 years old, at least 18 years old and less than 65 years old, at least 6 months and less than 5 years old, at least 5 years old and less than 65 years old, at least 60 years old, or at least 65 years old. For example, the subject is 6 months, 8 months, 10 months, 12 months, 14 months, 16 months, 18 months, 20 months, 22 months, 24 months, 3 years old, 4 years old, 5 years old, 6 years old, 10 years old, 12 years old, 15 years old, 18 years old, 20 years old, 21 years old, 25 years old, 30 years old, 35 years old, 40 years old, 50 years old, 60 years old, 70 years old, 75 years old, 80 years old, 85 years old, or 90 years old. In certain embodiments, the methods disclosed herein comprise administering two doses of the vaccine composition to the subject at intervals of 2-6 weeks, such as 4 weeks.

[0020] The embodiments discussed in this disclosure may provide one or more of the following advantages. Embodiments may be used to generate hemagglutinin sequences that have the potential to induce broad protection against influenza infection following vaccination. Notably, embodiments may be used to generate antigens that have higher than expected cure rates for functional influenza viruses containing designed hemagglutinin sequences. These antigens are believed to have broad protection, greater than current standard of care antigens in animal models. Embodiments may be used to generate broadly protective hemagglutinin proteins for use as influenza vaccine antigens.

[0021] By transforming amino acid sequences into a lower dimensional space and adding mutations in representatives of these amino acid sequences, new non-wild-type amino acid sequences can be generated. These new amino acid sequences can be used to make new vaccines that are more effective than other methods. For example, there may be non-wild-type amino acid sequences that, when a subject (e.g., a human) is exposed to a vaccine containing a protein with the non-wild-type amino acid sequence, will cause the subject to have a stronger immune response, resulting in better protection and better health.

[0022] Another advantage of the techniques provided in the present disclosure is that it increases the likelihood of generating protein sequence data for proteins that can actually exist and be manufactured. As will be appreciated, protein sequences can be described that cannot exist due to geometry, physical forces, etc. The processes described in this document can advantageously be limited to processes that are known to be manufacturable or expected to be manufacturable.

[0023] Other features, aspects and potential advantages will be apparent from the accompanying description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a block diagram of an example system that can be used to manufacture vaccines.

[0025] Figure 2 It is a schematic diagram of the data that can be used to create a vaccine.

[0026] Figures 3 to 6 is a flow chart of an example process that can be used to process high-dimensional data in a lower-dimensional space, such as can be used to make vaccines.

[0027] Figure 7 is a swim lane diagram of an example process for making a vaccine.

[0028] Figure 8 is a schematic diagram illustrating examples of computing devices and mobile computing devices.

[0029] Like reference numbers in the various drawings indicate like elements. DETAILED DESCRIPTION

[0030] This document describes the creation of vaccines through a machine learning process. The creation of vaccines uses candidate proteins generated through a computational process including machine learning. A corpus of wild-type amino acid sequences is provided to a variational autoencoder to generate low-dimensional representations (latent space) of the sequences. After training such a model, some of the representatives may generate non-wild-type amino acid sequences when decoded. The representatives in the latent space are tested to identify one or more representatives that are computationally predicted to generate amino acid sequences that will produce a desired biological response in a subject. One or more of these candidate representatives are selected based on the predicted desired response and the one or more candidate representatives are converted to a higher dimensional space defined by traditional amino acid sequences. One or more vaccines are manufactured based on these newly defined one or more amino acid sequences. The sequences can be filtered to exclude non-wild-type sequences or wild-type sequences as needed.

[0031] Influenza viruses are members of the Orthomyxoviridae family. There are three subtypes of influenza viruses: influenza A, influenza B, and influenza C. Influenza A viruses infect a wide variety of birds and mammals, including humans, chickens, ferrets, pigs, and horses. In mammals, most influenza A viruses cause mild localized infections of the respiratory tract and intestinal tract.

[0032] Influenza virions contain a negative sense RNA genome that encodes the following nine proteins: hemagglutinin (HA), matrix (M1), proton ion channel protein (M2), neuraminidase (NA), nonstructural protein 2 (NS2), nucleoprotein (NP), polymerase acidic protein (PA), polymerase basic protein 1 (PB1) and polymerase basic protein 2 (PB2). HA, M1, M2 and NA are membrane-associated proteins, while NP, NS2, PA, PB1 and PB2 are nucleocapsid-associated proteins. M1 protein is the most abundant protein in influenza particles. HA and NA proteins are envelope glycoproteins that are responsible for viral attachment and cell entry. HA and NA proteins are the source of the main immunodominant epitopes for virus neutralization and protective immunity. HA and NA proteins are considered to be the most important components of preventive influenza vaccines.

[0033] HA is a viral surface glycoprotein that typically contains about 560 amino acids and accounts for 25% of the total viral protein.

[0034] NA is a membrane glycoprotein of influenza virus. NA is 413 amino acids in length and is encoded by a gene of 1413 nucleotides. Nine different NA subtypes (N1, N2, N3, N4, N5, N6, N7, N8 and N9) have been identified in influenza virus, all of which are found in wild birds.

[0035] The ability of influenza viruses to cause widespread illness derives from their ability to evade the immune system by undergoing antigenic changes.

[0036] definition

[0037] In order to more easily understand the present disclosure, certain terms are first defined below. The following terms and other definitions of other terms can be set forth through the specification. If the definition of a term set forth below is inconsistent with the definition in the application or patent incorporated by reference, the definition set forth in the present application should be used to understand the meaning of the term.

[0038] As used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a method" includes one or more methods, and / or steps of the type described herein and / or which will become apparent to those skilled in the art upon reading this disclosure, etc.

[0039] Adjuvant: As used herein, the term "adjuvant" refers to a substance or combination of substances that can be used to enhance the immune response to the antigenic component of a vaccine.

[0040] Antigen: As used herein, the term "antigen" refers to a factor that elicits an immune response; and / or (ii) a factor that is bound by a T cell receptor (e.g., when presented by an MHC molecule) or bound to an antibody (e.g., produced by a B cell) when exposed or administered to an organism. In some embodiments, an antigen elicits a humoral response in an organism (e.g., including the production of antigen-specific antibodies); alternatively or additionally, in some embodiments, an antigen elicits a cellular response in an organism (e.g., involving a T cell whose receptor specifically interacts with the antigen). One skilled in the art will appreciate that a particular antigen may elicit an immune response in one or several members of a target organism (e.g., mouse, ferret, rabbit, primate, human), but not in all members of the target organism species. In some embodiments, the antigen triggers an immune response in at least about 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% of the members of the target organism species. In some embodiments, the antigen binds to an antibody and / or a T cell receptor and may or may not induce a specific physiological response in an organism. In some embodiments, for example, an antigen may bind to an antibody and / or a T cell receptor in vitro, regardless of whether such interaction occurs in vivo. In some embodiments, the antigen reacts with a product of a specific humoral or cellular immunity (including those induced by a heterologous immunogen). Antigens include NA and HA forms as described herein.

[0041] Carrier: As used herein, the term "carrier" refers to a diluent, adjuvant, excipient or vehicle with which the composition is administered. In some exemplary embodiments, the carrier may include sterile liquids such as, for example, water and oils, including oils of petroleum, animal, vegetable or synthetic origin, such as, for example, peanut oil, soybean oil, mineral oil, sesame oil, etc. In some embodiments, the carrier is or includes one or more solid components.

[0042] Epitope: As used herein, the term "epitope" includes any portion that is specifically recognized by an immunoglobulin (e.g., an antibody or receptor) binding component, in whole or in part. In some embodiments, an epitope is composed of a plurality of chemical atoms or groups on an antigen. In some embodiments, such chemical atoms or groups are surface exposed when the antigen adopts a related three-dimensional conformation. In some embodiments, such chemical atoms or groups are physically close to each other in space when the antigen adopts such a conformation. In some embodiments, when the antigen adopts an alternative conformation (e.g., linearized), at least some of such chemical atoms or groups are physically separated from each other.

[0043] Excipients: As used herein, the term "excipient" refers to non-therapeutic agents that may be included in a pharmaceutical composition, for example, to provide or contribute to a desired consistency or stabilization effect. Suitable pharmaceutical excipients include, for example, starch, glucose, lactose, sucrose, sorbitol, gelatin, malt, rice, flour, chalk, silica gel, sodium stearate, glyceryl monostearate, talc, sodium chloride, skim milk powder, glycerol, propylene, ethylene glycol, water, ethanol, and the like.

[0044] Immune response: As used herein, the term "immune response" refers to the response of cells of the immune system (such as B cells, T cells, dendritic cells, macrophages or polymorphonuclear cells) to stimuli (such as antigens, immunogens or vaccines). The immune response can include any cell of the body that participates in the host defense response, including, for example, epithelial cells that secrete interferons or cytokines. Immune responses include, but are not limited to, innate and / or adaptive immune responses. As used herein, a protective immune response refers to an immune response that protects a subject from infection (prevents infection or prevents the occurrence of diseases associated with infection) or alleviates symptoms of infection. Methods for measuring immune responses are well known in the art and include, for example, measuring the proliferation and / or activity of lymphocytes (such as B or T cells), the secretion of cytokines or chemokines, inflammation, antibody production, etc. Antibody response or humoral response is an immune response that produces antibodies. "Cellular immune response" is an immune response mediated by T cells and / or other white blood cells.

[0045] Immunogen: As used herein, the term "immunogen" or "immunogenic" refers to a compound, composition or substance that can stimulate an immune response (such as the production of antibodies or T cell responses) in an animal under appropriate conditions, including compositions injected or absorbed into an animal. As used herein, "immunization" means to protect a subject from an infectious disease.

[0046] Immunologically effective amount: As used herein, the term "immunologically effective amount" means an amount sufficient to immunize a subject.

[0047] Prevention: As used herein, the term "prevention" refers to preventing, avoiding disease manifestation, delaying the onset of one or more symptoms of a particular disease, disorder or condition (e.g., infection, such as influenza virus infection), and / or reducing the frequency and / or severity of the one or more symptoms. In some embodiments, prevention is assessed on a population basis, such that if a statistically significant reduction in the development, frequency and / or intensity of one or more symptoms of the disease, disorder or condition is observed in a population susceptible to a particular disease, disorder or condition, then the agent is considered to "prevent" the disease, disorder or condition.

[0048] Sequence identity: Similarity between amino acid or nucleic acid sequences is expressed as similarity between the sequences, also known as sequence identity. Sequence identity is often measured as a percentage identity (or similarity or homology); the higher the percentage, the more similar the two sequences are. "Sequence identity" between two nucleic acid sequences indicates the percentage of nucleotides that are identical between the sequences. "Sequence identity" between two amino acid sequences indicates the percentage of amino acids that are identical between the sequences. Homologs or variants of a given gene or protein will have a relatively high degree of sequence identity when aligned using standard methods.

[0049] The terms "% identical", "% identity" or similar terms are intended to refer in particular to the percentage of identical nucleotides or amino acids in an optimal alignment between the sequences to be compared. The percentages are purely statistical, and the differences between the two sequences may, but are not necessarily, randomly distributed over the entire length of the sequences to be compared. The comparison of two sequences is usually performed in the following manner: after optimal alignment, the sequences are compared with respect to segments or "comparison windows" to identify local regions of corresponding sequences. Optimal alignment for comparison can be performed manually or with the aid of the local homology algorithm of Smith and Waterman, 1981, Ads App. Math. 2, 482, with the aid of the local homology algorithm of Needleman and Wunsch, 1970, J. Mol. Biol. 48, 443, with the aid of the similarity search algorithm of Pearson and Lipman, 1988, Proc. Natl Acad. Sci. USA 88, 2444, or with the aid of computer programs that use such algorithms (GAP, BESTFIT, FASTA, BLAST P, BLAST N, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group, 575 Science Drive, Madison, Wis.).

[0050] The percent identity is obtained by determining the number of corresponding identical positions in the sequences being compared, dividing this number by the number of compared positions (eg, the number of positions in the reference sequence), and multiplying this result by 100.

[0051] In some embodiments, the degree of identity given for a region is at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, or about 100% of the entire length of the reference sequence. For example, if the reference nucleic acid sequence consists of 200 nucleotides, the degree of identity is given for at least about 100, at least about 120, at least about 140, at least about 160, at least about 180, or about 200 nucleotides (contiguous nucleotides in some embodiments). In some embodiments, the degree of identity is given for the entire length of the reference sequence.

[0052] A nucleic acid sequence or an amino acid sequence having a specific degree of identity to a given nucleic acid sequence or an amino acid sequence, respectively, may have at least one functional and / or structural property of the given sequence, for example and in some cases, functionally and / or structurally equivalent to the given sequence. In some embodiments, a nucleic acid sequence or an amino acid sequence having a specific degree of identity to a given nucleic acid sequence or an amino acid sequence is functionally and / or structurally equivalent to the given sequence.

[0053] Subject: As used herein, the term "subject" means any member of the animal kingdom. In some embodiments, "subject" refers to a human. In some embodiments, "subject" refers to a non-human animal. In some embodiments, subjects include, but are not limited to, mammals, birds, reptiles, amphibians, fish, insects, and / or worms. In some embodiments, non-human subjects are mammals (e.g., rodents, mice, rats, rabbits, ferrets, monkeys, dogs, cats, sheep, cattle, primates, and / or pigs). In some embodiments, the subject may be a transgenic animal, a genetically engineered animal, and / or a clone. In some embodiments, the subject is an adult, a teenager, or an infant. In some embodiments, the term "individual" or "patient" is used and is intended to be interchangeable with "subject."

[0054] Vaccination: As used herein, the term "vaccination" refers to the administration of a composition intended to generate an immune response, for example, to a pathogenic agent such as influenza virus. Vaccination can be administered before, during, and / or after exposure to a pathogenic agent and / or the appearance of one or more symptoms, and in some embodiments, before, during, and / or shortly after exposure to the pathogenic agent. Vaccines may induce prophylactic / preventative and therapeutic responses. The method of administration varies depending on the vaccine, but may include inoculation, ingestion, inhalation, or other forms of administration. Vaccination can be delivered by any of a variety of routes, including parenteral, such as intravenous, subcutaneous, intraperitoneal, intradermal, or intramuscular. The vaccine may be administered together with an adjuvant to enhance the immune response. In some embodiments, vaccination includes multiple administrations of the vaccination composition at appropriate intervals.

[0055] Vaccine efficacy: As used herein, the term "vaccine efficacy" or "vaccine effectiveness" refers to an indicator of the percentage of reduction in disease evidence in subjects to which a vaccine composition has been administered. For example, a vaccine efficacy of 50% indicates that the number of disease cases in the group of vaccinated subjects is reduced by 50% compared to a group of unvaccinated subjects or a group of subjects administered a different vaccine.

[0056] Wild-type (WT): As understood in the art, the term "wild-type" generally refers to the normal form of a protein or nucleic acid, as found in nature. For example, wild-type HA and NA polypeptides are found in natural isolates of influenza virus. A variety of different wild-type HA and NA sequences can be found in the NCBI influenza virus sequence database.

[0057] Measuring hemagglutinin activity

[0058] Hemagglutinin activity can be measured using techniques known in the art, including, for example, hemagglutinin inhibition assay (HAI).HAI uses the following hemagglutination process, wherein the sialic acid receptors on the surface of red blood cells (RBC) bind to the hemagglutinin glycoprotein found on the surface of influenza virus (and several other viruses), and form a network or lattice structure of interconnected RBC and virions, called hemagglutination, which occurs on virions in a concentration-dependent manner. This is a physical measurement, used as an indicator of the ability of similar sialic acid receptors on virus-binding pathogen targeting cells in vivo. The introduction of antiviral antibodies produced in human or animal immune responses (for another virus, which may be genetically similar or different from the virus used to bind RBC in the assay) interferes with virus-RBC interactions, and changes the virus concentration, which is enough to change the concentration when hemagglutination is observed in the assay. A target of HAI can be to characterize the concentration of antibodies in antiserum or other samples containing antibodies, which is related to their ability to induce hemagglutination in the assay. The highest dilution of antibodies that prevent hemagglutination is called HAI titer (i.e., the reaction measured).

[0059] Another method of measuring HA antibody response is to measure the potential larger group of antibodies caused by human or animal immune response, which may not necessarily affect the hemagglutination in HAI determination. For this reason, a common method is to utilize enzyme-linked immunosorbent assay (ELISA) technology, in which viral antigens (such as hemagglutinin) are fixed on a solid surface, and then the antibodies from the antiserum are allowed to bind to the antigen. The readout measures the catalysis of the exogenous enzyme substrates compounded with the antibodies from the antiserum or other antibodies that bind to the antibodies of the antiserum. The catalysis of the substrate produces a product that is easy to detect. There are many variants of this in vitro assay. One such variant is called antibody forensics (AF), which is a multiple bead array technology that allows single serum samples to be measured simultaneously for multiple antigens. Compared with HAI titer, these measurements characterize concentration and total antibody recognition, and HAI titer is considered to be more particularly related to the interference of hemagglutinin molecules and sialic acid binding. Thus, in some cases, the antibody measurements of an antiserum may be proportionally higher or lower than the corresponding HAI titer of the hemagglutinin molecule of one virus relative to the hemagglutinin molecule of another virus; in other words, the two measurements of AF and HAI may not be linearly related.

[0060] Another method for measuring HA antibody responses includes virus neutralization assays (e.g., microneutralization assays) in which antibody titers are measured by the reduction of plaques, foci, and / or fluorescent signal (depending on the specific neutralization assay technique) in permissive cultured cells following incubation of virus with serial dilutions of antibody / serum samples.

[0061] Measurement of neuraminidase activity

[0062] Neuraminidase activity can be measured using techniques known in the art, including, for example, the MUNANA assay, the ELLA assay, or Assay (ThermoFisher Scientific, Waltham, MA). In the MUNANA assay, 2'-(4-methylumbelliferyl)-α-DN-acetylneuraminic acid (MUNANA) is used as a substrate. Any enzymatically active neuraminidase contained in the sample will cleave the MUNANA substrate, releasing the fluorescent compound 4-methylumbelliferyl (4-MU). Therefore, the amount of neuraminidase activity in the test sample is related to the amount of 4-MU released, which can be measured using fluorescence intensity (RFU, relative fluorescence units).

[0063] To determine the neuraminidase activity of the soluble tetrameric NA disclosed herein, the MUNANA assay should be performed using the following conditions: the soluble tetrameric NA is mixed with a buffer [33.3 mM 2-(N-morpholino)ethanesulfonic acid (MES, pH 6.5), 4 mM CaCl2, 50 mM BSA] and a substrate (100 μM MUNANA), and incubated with shaking at 37°C for 1 hour; the reaction is terminated by adding an alkaline pH solution (0.2 M Na2CO3); the fluorescence intensity is measured using excitation and emission wavelengths of 355 and 460 nm, respectively; and the enzyme activity relative to the 4MU reference value is calculated. If necessary, an equivalent assay can be used to measure the neuraminidase enzyme activity.

[0064] Vaccine composition

[0065] In certain aspects, disclosed herein are vaccine compositions comprising a plurality of generated amino acid sequences.

[0066] Each amino acid sequence produced can be present in the compositions disclosed herein in an amount effective to induce an immune response in a subject to which the composition is administered. In certain embodiments, each amino acid sequence produced can be present in a vaccine composition disclosed herein in an amount ranging, for example, from about 0.1 g to about 500 g, such as from about 5 g to about 120 g, from about 1 g to about 60 g, from about 10 g to about 60 g, from about 15 g to about 60 g, from about 40 g to about 50 g, from about 42 g to about 47 g, from about 5 g to about 45 g, from about 15 g to about 45 g, from about 0.1 g to about 90 g, from about 5 g to about 90 g, from about 10 g to about 90 g, or from about 15 g to about 90 g. In certain embodiments, each recombinant HA may be present in the vaccine compositions disclosed herein in an amount of about 5 g, 10 g, 15 g, 20 g, 25 g, 30 g, 35 g, 40 g, 45 g, 50 g, 55 g, 60 g, 65 g, 70 g, 75 g, 80 g, 85 g or about 90 g.

[0067] The vaccine composition may further include an adjuvant. As used herein, the term "adjuvant" refers to a substance or vehicle that non-specifically enhances the immune response to an antigen. The adjuvant may include a suspension of a mineral (alum, aluminum salt, including, for example, aluminum hydroxide / aluminum hydroxide (AlOOH), aluminum phosphate (AlPO4), aluminum hydroxyphosphate sulfate (AAHS) and / or potassium aluminum sulfate) on which an antigen is adsorbed; or an oil-in-water emulsion, wherein the antigen solution is emulsified in mineral oil (e.g., Freund's incomplete adjuvant), sometimes including killed mycobacteria (Freund's complete adjuvant) to further enhance antigenicity. Immunostimulatory oligonucleotides (e.g., those including CpG motifs) may also be used as adjuvants (e.g., see U.S. Patent Nos. 6,194,388; 6,207,646; 6,214,806; 6,218,371; 6,239,116; 6,339,068; 6,406,705; and 6,429,199). Adjuvants also include biomolecules, such as lipids and co-stimulatory molecules. Exemplary biological adjuvants include AS04 (Didierlaurent, AM et al., AS04, an Aluminum Salt-and TLR4 Agonist-Based Adjuvant System, Induces a Transient Localized Innate Immune Response Leading to Enhanced Adaptive Immunity [AS04--an adjuvant system based on aluminum salts and TLR4 agonists, inducing transient local innate immune responses, thereby enhancing adaptive immunity], J. IMMUNOL. [Journal of Immunology] 2009, 183: 6186-6197), IL-2, RANTES, GM-CSF, TNF-?, IFN-?, G-CSF, LFA-3, CD72, B7-1, B7-2, OX-40L and 41BBL.

[0068] In certain embodiments, the adjuvant is a squalene-based adjuvant comprising an oil-in-water adjuvant emulsion comprising at least squalene, an aqueous solvent, a polyoxyethylene alkyl ether hydrophilic nonionic surfactant, and a hydrophobic nonionic surfactant. In certain embodiments, the emulsion is thermoreversible, optionally wherein 90% of the population by volume of the oil droplets is less than 200 nm in size.

[0069] In certain embodiments, the polyoxyethylene alkyl ether has the formula CH3-(CH2)x-(O-CH2-CH2)n-OH, wherein n is an integer from 10 to 60, and x is an integer from 11 to 17. In certain embodiments, the polyoxyethylene alkyl ether surfactant is polyoxyethylene (12) cetearyl ether.

[0070] In certain embodiments, 90% of the population by volume of the oil droplets has a size less than 160 nm. In certain embodiments, 90% of the population by volume of the oil droplets has a size less than 150 nm. In certain embodiments, 50% of the population by volume of the oil droplets has a size less than 100 nm. In certain embodiments, 50% of the population by volume of the oil droplets has a size less than 90 nm.

[0071] In certain embodiments, the adjuvant further comprises at least one alditol, including but not limited to glycerol, erythritol, xylitol, sorbitol, and mannitol.

[0072] In certain embodiments, the hydrophilic nonionic surfactant has a hydrophilic / lipophilic balance (HLB) greater than or equal to 10. In certain embodiments, the hydrophobic nonionic surfactant has an HLB less than 9. In certain embodiments, the hydrophilic nonionic surfactant has an HLB greater than or equal to 10, and the hydrophobic nonionic surfactant has an HLB less than 9.

[0073] In certain embodiments, the hydrophobic nonionic surfactant is a sorbitan ester (such as sorbitan monooleate) or a mannide ester surfactant. In certain embodiments, the amount of squalene is between 5% and 45%. In certain embodiments, the amount of polyoxyethylene alkyl ether surfactant is between 0.9% and 9%. In certain embodiments, the amount of hydrophobic nonionic surfactant is between 0.7% and 7%. In certain embodiments, the adjuvant comprises: i) 32.5% squalene, ii) 6.18% polyoxyethylene (12) hexadecyl ether, iii) 4.82% sorbitan monooleate, and iv) 6% mannitol.

[0074] In certain embodiments, the adjuvant further comprises an alkyl polyglycoside and / or a cryoprotectant, such as a sugar, in particular dodecyl maltoside and / or sucrose.

[0075] In certain embodiments, the adjuvant comprises AF03, as described in Klucker et al., AF03, an alternative squalene emulsion-based vaccine adjuvant prepared by a phase inversion temperature method, J. PHARM. SCI. 2012, 101(12):4490-4500, which is hereby incorporated by reference in its entirety. In certain embodiments, the adjuvant comprises a liposome-based adjuvant, such as SPA14. SPA14 is a liposome-based adjuvant (AS01-like) comprising a toll-like receptor 4 (TLR4) agonist (E6020) and a saponin (QS21).

[0076] In addition to recombinant HA, recombinant NA and optional adjuvants, the vaccine composition may further include one or more pharmaceutically acceptable excipients. Generally, the nature of the excipient will depend on the specific mode of administration used. For example, parenteral formulations generally include injections that include pharmaceutically and physiologically acceptable fluids, such as water, saline, balanced salt solutions, dextrose aqueous solutions, glycerol, etc. as vehicles. For solid compositions (e.g., powders, pills, tablets or capsule forms), conventional non-toxic solid carriers may include, for example, pharmaceutical grade mannitol, lactose, starch or magnesium stearate. In addition to biological neutral carriers, the vaccine composition to be administered may also contain a small amount of non-toxic auxiliary substances, such as wetting agents or emulsifiers, pharmaceutically acceptable salts (to adjust osmotic pressure), preservatives, stabilizers, buffers, sugars, amino acids and pH buffers, such as sodium acetate or sorbitan monolaurate.

[0077] Typically, the vaccine composition is a sterile liquid solution formulated for parenteral administration (e.g., intravenous, subcutaneous, intraperitoneal, intradermal, or intramuscular administration). The vaccine composition may also be formulated for intranasal or inhalation administration. The vaccine composition may also be formulated for any other intended route of administration.

[0078] In certain embodiments, vaccine compositions are formulated for intradermal injection, intranasal administration or intramuscular injection. In certain embodiments, injections are prepared in conventional forms (as liquid solutions or suspensions, suitable for solid forms dissolved or suspended in liquids before injection, or as emulsions). In certain embodiments, injection solutions and suspensions are prepared from sterile powders or particles. General considerations for the preparation and manufacture of medicaments used by these approaches can be found in, for example, Remington's Pharmaceutical Sciences, 19th edition, Mack Publishing Co., Easton, PA, 1995 (incorporated herein by reference). At present, oral or nasal sprays or aerosol approaches (e.g., by inhalation) are most commonly used for direct delivery of therapeutic agents to the lungs and respiratory system. In certain embodiments, vaccine compositions are administered using a device for delivering a metered dose of vaccine compositions. Suitable devices for use in delivering the intradermal pharmaceutical compositions described herein include short needle devices, such as those described in the following patents: U.S. Patent No. 4,886,499, U.S. Patent No. 5,190,521, U.S. Patent No. 5,328,483, U.S. Patent No. 5,527,288, U.S. Patent No. 4,270,537, U.S. Patent No. 5,015,235, U.S. Patent No. 5,141,496, U.S. Patent No. 5,417,662 (all of which are incorporated herein by reference). Intradermal compositions can also be applied by devices that limit the effective penetration length of the needle into the skin, such as those described in WO 1999 / 34850 (incorporated herein by reference) and their functional equivalents. In addition, jet injection devices are also suitable, which deliver liquid vaccines to the dermis via a liquid jet syringe or a needle that pierces the stratum corneum and produces a jet that reaches the dermis.Jet injection devices are described, for example, in U.S. Pat. No. 5,480,381, U.S. Pat. No. 5,599,302, U.S. Pat. No. 5,334,144, U.S. Pat. No. 5,993,412, U.S. Pat. No. 5,649,912, U.S. Pat. No. 5,569,189, U.S. Pat. No. 5,704,911, U.S. Pat. No. 5,383,851, U.S. Pat. No. 5,893,397, U.S. Pat. No. 5,466,220, U.S. Pat. No. 5,339,163, U.S. Pat. No. 5,312,335, U.S. Pat. No. 5,503,627, U.S. Pat. No. 5,064,413, U.S. Pat. No. 5,520,639, U.S. Pat. No. 4,596,556, U.S. Pat. No. 4,790,824, U.S. Pat. No. 4,941,880, U.S. Pat. No. 4,940,460, WO 1997 / 37705 and WO 1997 / 13537 (all of which are incorporated herein by reference). In addition, ballistic powder / particle delivery devices are also suitable, which use compressed gas to accelerate the vaccine in powder form through the outer layer of the skin to the dermis. In addition, conventional syringes can be used in the classic mantoux method of intradermal administration.

[0079] Preparations for parenteral administration typically include sterile aqueous or non-aqueous solutions, suspensions and emulsions. Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, vegetable oils (such as olive oil) and injectable organic esters (such as ethyl oleate). Aqueous carriers include water, alcohol / water solutions, emulsions or suspensions, including saline and buffered media. Parenteral vehicles include sodium chloride solution, Ringer's dextrose, dextrose and sodium chloride, lactated Ringer's solution or fixed oils. Intravenous vehicles include fluids and nutritional supplements, electrolyte supplements (such as those based on Ringer's dextrose), etc. Preservatives and other additives may also be present, such as, for example, antimicrobial agents, antioxidants, chelating agents, and inert gases, etc.

[0080] Reagent test kit

[0081] Further disclosed herein are kits for the vaccine compositions as disclosed herein. The kit may include one suitable container comprising the vaccine composition or multiple containers comprising the different components of the vaccine composition, optionally with instructions for use.

[0082] In certain embodiments, a kit can include multiple containers, including, for example, a first container comprising one or more isolated nucleic acids, peptides, and / or proteins as disclosed herein.

[0083] Nucleic Acids, Cloning and Expression Systems

[0084] The present disclosure further provides artificial nucleic acid molecules. Nucleic acids may comprise DNA or RNA, and may be synthesized or recombinant in whole or in part. Unless the context otherwise requires, reference to a nucleotide sequence described herein encompasses a DNA molecule having a specified sequence, and encompasses an RNA molecule having a specified sequence in which U or a derivative thereof (such as pseudouridine) replaces T. Other nucleotide derivatives or modified nucleotides may be incorporated into the artificial nucleic acid molecule.

[0085] The present disclosure also provides constructs in the form of vectors (e.g., plasmids, phagemids, cosmids, transcription cassettes or expression cassettes, artificial chromosomes, etc.), which contain artificial nucleic acid molecules encoding the amino acid sequences produced as disclosed herein. The present disclosure further provides a host cell comprising one or more constructs as above.

[0086] Also provided are methods for preparing isolated peptides and / or proteins using recombinant techniques known in the art and as discussed above. The production and expression of recombinant proteins are well known in the art and can be performed using conventional procedures (such as those disclosed in Sambrook et al., Molecular Cloning: A Laboratory Manual (4th Edition 2012), Cold Spring Harbor Press). For example, the expression of HA or NA polypeptides can be achieved by culturing host cells containing artificial nucleic acid molecules encoding HA or NA as disclosed herein under appropriate conditions. For example, the expression of recombinant HA or NA polypeptides can be achieved by culturing host cells containing nucleic acid molecules encoding HA or NA as disclosed herein under appropriate conditions. After being produced by expression, HA or NA can be separated and / or purified using any suitable technique and then used as appropriate.

[0087] Systems for cloning and expressing polypeptides in a variety of different host cells are well known in the art.Any protein expression system (eg, stable or transient) compatible with the constructs disclosed in this application can be used to produce the amino acid sequences produced herein.

[0088] Appropriate vectors can be chosen or constructed so that they contain appropriate regulatory sequences, including promoter sequences, terminator sequences, polyadenylation sequences, enhancer sequences, marker genes and other appropriate sequences.

[0089] In order to express the amino acid sequence produced as disclosed herein, the nucleic acid encoding the amino acid sequence produced can be introduced into the host cell. The introduction can adopt any available technology. For eukaryotic cells, suitable techniques can include calcium phosphate transfection, DEAE-dextran, electroporation, liposome-mediated transfection and transduction using retrovirus or other viruses (such as vaccinia or baculovirus (for insect cells)). For bacterial cells, suitable techniques can include calcium chloride transformation, electroporation and transfection using bacteriophage. These technologies are well known in the art. (See, for example, "Current Protocols in Molecular Biology [Molecular Biology Laboratory Guide]," Ausubel et al., ed., John Wiley & Sons [John Wiley and Sons Publishers], 2010). After DNA is introduced, a selection method (e.g., antibiotic resistance) can be used to select cells containing the vector.

[0090] The host cell can be a plant cell, a yeast cell or an animal cell. Animal cells include invertebrates (e.g., insect cells), non-mammalian vertebrates (e.g., birds, reptiles, and amphibians) and mammalian cells. In one embodiment, the host cell is a mammalian cell. Examples of mammalian cells include, but are not limited to, COS-7 cells, HEK293 cells; baby hamster kidney (BHK) cells; Chinese hamster ovary (CHO) cells; mouse supporting cells; African green monkey kidney cells (VERO-76); human cervical cancer cells (e.g., HeLa); canine kidney cells (e.g., MDCK), etc. In one embodiment, the host cell is a CHO cell. In one embodiment, the host cell is an insect cell.

[0091] How to use

[0092] The present disclosure provides a method for administering a vaccine composition as described herein to a subject. These methods can be used to vaccinate a subject against a virus (e.g., influenza virus). In some embodiments, the vaccination method includes administering a vaccine composition to a subject in need, the vaccine composition comprising one or more separated nucleic acids, peptides and / or proteins encoding the amino acid sequence produced as described herein (e.g., recombinant influenza virus Has as described herein or recombinant influenza virus NA as described herein) and optional adjuvants, and the amount is effective for the virus (e.g., influenza virus) to vaccinate the subject. Similarly, the present disclosure provides a vaccine composition, the vaccine composition comprising one or more separated nucleic acids, peptides and / or proteins encoding the amino acid sequence produced as described herein (e.g., influenza virus Has or NA as described herein) and optional adjuvants, for vaccinating a subject against a virus (e.g., influenza virus) (or for the manufacture of a drug for use in vaccinating a subject against a virus (e.g., influenza virus)).

[0093] The present disclosure also provides methods of immunizing a subject against a virus (e.g., influenza virus), comprising administering to the subject an immunologically effective amount of a vaccine composition comprising one or more recombinant influenza virus HA or NA as described herein and optionally an adjuvant.

[0094] In some embodiments, the method or use prevents viral infection (e.g., influenza virus infection) or disease in a subject. In some embodiments, the method or use causes a protective immune response in a subject. In some embodiments, the protective immune response is an antibody response.

[0095] The method / uses of immunity provided herein can cause a broad neutralization immune response for one or more viruses (e.g., influenza virus).Therefore, in different embodiments, compositions described herein can provide a broad cross protection for different types of viruses (e.g., influenza virus).In certain embodiments, compositions provide cross protection for avian influenza virus, swine influenza virus, seasonal influenza virus and / or pandemic influenza virus.In certain embodiments, the method / uses of immunity can cause an improved immune response for one or more seasonal influenza strains (e.g., standard care strain).For example, the improved immune response can be an improved humoral immune response.In certain embodiments, the method / uses of immunity can cause an improved immune response for one or more pandemic influenza strains.In certain embodiments, immune methods can cause an improved immune response for one or more swine influenza strains.In certain embodiments, the method / uses of immunity can cause an improved immune response for one or more avian influenza strains.

[0096] In certain embodiments, provided herein is a method of enhancing or expanding a protective immune response in a subject, the method comprising administering to the subject an immunologically effective amount of a vaccine composition disclosed herein, wherein the vaccine composition increases the vaccine efficacy of a standard of care influenza virus vaccine composition by an incremental amount ranging from about 5% to about 100%, such as about 10% to about 25%, about 20% to about 100%, about 15% to about 75%, about 15% to about 50%, about 20% to about 75%, about 20% to about 50%, or about 40% to about 80%, such as about 40% to about 60% or about 60% to about 80%. In certain embodiments, the vaccine compositions disclosed herein have a vaccine efficacy that is at least 5% higher than the vaccine efficacy of a standard of care influenza virus vaccine, for example, a vaccine efficacy that is at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or at least 100% higher than the vaccine efficacy of a standard of care influenza virus vaccine. Similarly, the present disclosure provides any vaccine composition described herein for use in enhancing or expanding a protective immune response in a subject (or for use in the manufacture of a medicament for use in enhancing or expanding a protective immune response in a subject).

[0097] Also provided are methods for preventing viral diseases (e.g., influenza virus diseases) in subjects, comprising administering to the subject a vaccine composition comprising one or more isolated nucleic acids, peptides and / or proteins encoding the generated amino acid sequences (e.g., recombinant influenza virus HA or NA as described herein), and an optional adjuvant, in an amount effective to prevent viral diseases (e.g., influenza virus diseases) in subjects. Similarly, the present disclosure provides a vaccine composition comprising one or more recombinant influenza virus HA or NA as described herein, and an optional adjuvant, for use in preventing viral diseases (e.g., influenza virus diseases) in subjects (or for use in the manufacture of a medicament for use in preventing viral diseases (e.g., influenza virus diseases) in subjects).

[0098] Also provided are methods of inducing an immune response against influenza virus HA and influenza virus NA in a subject, the methods comprising administering to the subject a vaccine composition comprising one or more recombinant influenza virus HA as described herein, one or more recombinant influenza virus NA as described herein, and optionally an adjuvant.

[0099] Figure 1is a block diagram of an example system 100 that can be used to make vaccines. In system 100, the techniques described in this document are used to design and make novel vaccines 116. For example, for viruses that have multiple strains and / or rapidly mutating strains (such as influenza, human rhinovirus, HIV, coronaviruses (such as COVID-19)) or new viruses that have never been encountered before, the techniques described herein can be used to quickly generate vaccine candidates that can be used for testing in humans or other subjects.

[0100] The system 100 receives virus strain data 102 and wild-type amino acid data 104 as input. The virus strain data 102 includes data about one or more virus strains for which a vaccine is needed. The virus strain data 102 may include amino acid sequence data, as well as other types of data, such as metadata (e.g., unique identifier, strain identification) or non-metadata attributes (e.g., records of physicochemical properties of amino acid sequences (e.g., molecular weight)). The wild-type amino acid data 104 may include an amino acid definition corpus of hundreds, thousands, hundreds of thousands, or more amino acid sequences. These sequences are referred to herein as wild-type, indicating that in some embodiments, they are typically amino acid sequences found in the wild. However, in other embodiments, the amino acid sequence may include artificial amino acid sequences, artificial amino acid sequences, or other types of amino acids that have never been seen before. The amino acid data 104 may include amino acid sequence data, as well as other types of data, such as metadata (e.g., unique identifier, strain identification) or non-metadata attributes (e.g., records of physicochemical properties of amino acid sequences (e.g., molecular weight)).

[0101] The system 100 includes a computer system 106 that can generate data 108 of candidate non-wild-type amino acid sequences by using the data 102 and 104. These non-wild-type amino acid sequences are amino acids that are not found in the wild, or are not known to be found in the wild. As will be appreciated, it is possible that one or more candidate non-wild-type amino acid sequences 108 may actually exist in the wild, but are unknown to the operator of the system 100 or even to society at large. The candidate non-wild-type amino acid data 108 may include amino acid sequence data, as well as other types of data, such as metadata (e.g., unique identifiers, strain identifiers) or non-metadata attributes (e.g., records of physicochemical properties of amino acid sequences (e.g., molecular weight)).

[0102] Computer system 106 verifies the manufacture of one or more candidates in data 108, thereby generating data 110. Data 110 may include amino acid sequence data, as well as other types of data, such as metadata (e.g., unique identifier, strain identification) or non-metadata attributes (e.g., a record of physicochemical properties of the amino acid sequence (e.g., molecular weight)). In some cases, data 102 / 104, 108, and 110 have the same data format, while in some cases, data 102 / 104, 108, and 110 have different data formats.

[0103] In some cases, the validation process for selecting a candidate may include determining whether an amino acid sequence can be synthesized, or whether it can be synthesized in an easy or economical manner. As will be appreciated, an amino acid sequence can define the structure of a molecule that cannot exist in the physical world due to the geometry and forces exhibited by the molecule. Therefore, these impossible sequences can be excluded from the validation process. In addition, some candidates may be excluded even if effective molecules are defined. For example, the computing system 106 may maintain a data repository of previous candidates that have not actually been effectively used as vaccines after being studied in clinical trials, or are predicted to be less immunogenic or less protective against target virus strains, and the data repository may include virus strain data 102. In this case, the candidates in data 108 may be excluded from validation data 110. In some cases, candidates may be excluded or prioritized based on synthesis and manufacturing considerations. For example, candidates with specific synthesis or handling conditions (e.g., refrigeration, shock sensitivity) may be excluded from validation or have a lower priority than other candidates with less onerous synthesis or handling conditions.

[0104] The system 100 may also include vaccine manufacturing equipment 112, which can use vaccine precursors 114 and one or more verified non-wild type amino acid sequence data 110 to manufacture one or more vaccine doses or vaccine molecules 116. As will be understood, the scale of synthesis required for preliminary exploration and testing is much smaller than that for large-scale manufacturing of vaccines that have been tested, proven to be safe and effective and approved for use in humans or other subjects. Therefore, the details of the manufacturing equipment 112 can be changed as needed. Similarly, although the vaccine precursors 114 include those articles, chemicals, materials, etc. used to manufacture the vaccine 116, the precursors 114 will also change as needed.

[0105] Figure 21 is a schematic diagram of data that can be used to make a vaccine. For example, the data shown here can be used by computer system 106 or other computer systems. In summary, data 104 is transformed into a lower dimensional space, modified to generate new amino acid sequences, and then one or more of the amino acid sequences are selected for vaccine production. The data can be used by computer system 106 or other computing systems.

[0106] Wild-type amino acid data 104 is one or more data objects defining a plurality of wild-type amino acid sequences.Wild-type amino acid data 104 are shown here, and the sub-parts of some sequences use the single-letter names recommended by the Joint Committee on Biochemical Nomenclature of the International Union of Pure and Applied Chemistry-International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) for ease of reading.For wild-type amino acid sequences, data 104 may include data value vectors (e.g., single American Standard Code for Information Interchange (ASCII) character, integer) to represent the amino acid in the sequence represented by data 104.As will be understood, longer sequences will have more indexes than those intuitively shown here, and more sequences than those shown may be stored in data 104.In addition, for the sake of clarity, the other parts of data 104 are not presented here.Each amino acid sequence may be recorded as a single letter or a string of letters. A string of letters may include a plurality of single letters.One or more amino acid sequences may include a first amino acid sequence and a second amino acid sequence, and each of the first amino acid sequence and the second amino acid sequence includes a corresponding single letter or a corresponding string of letters.That is, each amino acid sequence may be stored in data conforming to the same format, while preserving different values. This enables interoperability and consistent handling of data.

[0107] As will be appreciated, the vectors of data 104 have a certain length, and the length of each vector may be the same. These vectors define a particular dimension of data 104. For example, a length of 632 defines a space with 632 dimensions, a length of 88 defines a space with 88 dimensions, and so on. For a sequence that can contain one of 20 different amino acids, the domain or size of each dimension is 20. Therefore, the amino acid sequence corpus in data 104 defines a distribution of vectors (or point positions) in a space with the length of the amino acid sequence as the dimension.

[0108] Data 104 is variationally encoded (described elsewhere) and multiple reduced-dimensionality sequences 202 in a reduced-dimensionality space are generated from one or more data objects. In this example, the reduced-dimensionality space has 5 dimensions, and the data 202 can be recorded in a vector of length 5, but different dimensions (and lengths of vectors) can be used in other embodiments. Data 202 can record data (e.g., real numbers) or other appropriate data in each index of the vector, wherein the values ​​are encoded from the values ​​in the amino acid sequence of data 104 and the variational data obtained by variational encoding is added. In some embodiments, these real numbers are trained so that 1) similar sequences will be adjacent, and 2) the digital coordinates can be decoded using the decoder portion of the model. Therefore, each reduced-dimensionality sequence contains corresponding data for at least one of the wild-type amino acid sequences.

[0109] In some examples, the variational encoding includes a lossy data transformation, thereby producing data 202 that is based on data 104 but does not contain all of the information in data 104. However, this is still an advantageous process because it can allow the operations described in this document to generate new non-wild-type amino acid sequences that can be used for vaccine development and manufacturing.

[0110] The reduced dimensionality space has a lower dimension than the corresponding wild-type amino acid sequence. This can allow computational operations that are impossible, computationally inefficient, or otherwise undesirable in the higher dimensional space of data 104 to be performed in the lower dimensional space. For example, because the dimensions of the reduced dimensionality space are not collinear with the dimensions of the higher dimensional space, or do not represent a small subset of the higher dimensional space, the attributes of a single dimension cannot be directly mapped to a single dimension of the higher dimensional space. Therefore, operations in a single dimension of the reduced dimensionality space allow for efficient execution, and may produce impossible or unintuitive results when operating or thinking in a higher dimensional space.

[0111] In some implementations, data 206 stores candidate sequences generated from data 202. One such example random sampling is performed from the entire normal distribution space defined by data 202. For example, if there are 5 dimensions in data 202, there are typically 5 axes to choose from.

[0112] In some embodiments, data 206 stores candidate sequences generated from gap data 204. One such example is to assemble a distribution on each dimension of the reduced dimensionality space. Since data 202 is stored as multiple vectors, a statistical distribution of values ​​on a specific dimension can be assembled in data 204. For example, if integers are stored in each index of a vector in data 202, each index of a vector of data 204 can store a histogram of integers in the same index in a vector in data 202. In another example, if real numbers are stored in each index of a vector in data 202, each index of a vector of data 204 can store parameters of a function defining a best fit curve, such as a best fit curve that can be found via linear regression or similar analysis. As will be understood, the data type stored in each index of data 204 can be determined based on the data type stored in each index of data 202. In this way, multiple reduced dimensionality sequences define the distribution of values ​​along each dimension of the reduced dimensionality space.

[0113] Data 206 stores candidate sequences. For example, multiple candidate sequences can be generated in the same reduced dimensionality space used for data 202 and 204. This can be performed multiple times (tens, hundreds, thousands, tens of thousands, hundreds of thousands, millions, billions, trillions, or more times) to create many candidate sequences. Data 206 can store definitions of amino acid sequences that have similar properties to those in data 104 (although stored in a lower dimensional space like data 202), but may not actually appear in data 104. If the sequence in data 104 has particularly beneficial or desirable properties, it can be expected that these properties will be found in at least some of data 206. For example, if the amino acid sequence in data 104 triggers an immune response in a subject, the sequence defined by data 206 is likely to provide a similar immune response. Moreover, because they are different from the amino acids in sequence 104 to some extent, they may trigger a greater or lesser immune response. Therefore, as will be explained, this can generate new sequences that have never been known or realized before, thereby triggering a greater immune response, making it more suitable for use in vaccines. In this way, vaccine technology has been beneficially advanced.

[0114] In some cases, data 204 is randomly sampled according to its distribution to create data 206. For example, if each index contains a histogram, the values ​​in the histogram are selected according to the weight of the height of each value in the histogram. For example, if each index contains parameters that define a curve of Y values ​​for a given X value, the X values ​​can be selected according to the weight of the height of the curve or by randomly selecting a point below the curve. In this way, the distribution of values ​​in a given index in data 202 will be similar to the distribution of values ​​in the same index in data 206, even though they are statistically unlikely to be the same.

[0115] As will be described, each candidate sequence in the test data 206 can then be tested and the best candidate can be selected for analysis or for vaccine manufacturing. For example, an immune response predictor (such as an antibody titer predictor) can be used to predict the immune response of a subject for a given viral amino acid sequence. The titer predictor can be configured to accept two amino acid sequences as input. The function can be configured to return the predicted immune response of a subject (e.g., mankind, animal) as output. The output can take the form of a value between, for example, 0 and 1, wherein the higher the value, the greater the predicted immune response. The predictor function can be operated using a machine learning model.

[0116] To perform this operation, data 102 containing the amino acid sequence of the virus is modified in the same manner as data 104, thereby forming data 208 of the same format and of the same kind as data 202. That is, data 102 containing one or more data objects defining the amino acid sequence of the virus is subjected to variational encoding (described elsewhere) and produces reduced-dimensional viral sequence 208. This data 208 stores data in the same reduced-dimensional space as data 202 to 206, thereby allowing efficient computational operations to be performed on any of data 202 to 208.

[0117] For each sequence in data 206, the valence predictor generates a candidate score. The candidate score is the predicted immune response to the amino acid sequence. Three examples of many sequences and scores are shown here, but it will be understood that the valence predictor can be used many times (tens, hundreds, thousands, tens of thousands, hundreds of thousands, millions, billions, trillions, or more) to create as many candidate scores as there are candidate sequences.

[0118] These candidate scores indicate the predicted level of immune response and can therefore be considered a prediction of the candidate sequence's effectiveness in a vaccine. At least one selected candidate sequence is selected from the candidate sequences. Various computational processes can be used to identify the "best" sequence for testing and / or manufacturing.

[0119] In one example, a predefined number or a dynamically defined number of candidate sequences are selected. This involves selecting the candidate sequences with the highest N candidate scores. The value of N can be based on the throughput of the device and system capable of testing the vaccine, so if N amino acid sequences can be tested, the same N value can be used here. In the case where the value of N is greater than 1, multiple candidate sequences are selected. In the case where the value of N is equal to 1, a single candidate sequence is selected. An example is shown here where the value of N is 2, so 2 sequences are selected.

[0120] In one example, a predefined or dynamically defined threshold is used to select candidate sequences. The threshold can be, for example, based on a minimum value that is expected to produce good results. As will be appreciated, the threshold can be close to a maximum value (e.g., close to but less than 1) to select only the most promising candidate sequences, can be close to a minimum value (e.g., close to but greater than 0) to select all candidates except the least promising candidate, or can be any other appropriate value. In some cases, this may result in no candidate sequence being selected, depending on the threshold and the quality of the candidate.

[0121] Data 110 is created by constructing an amino acid sequence in the higher dimensional space used by data 102 and 104. Depending on the configuration of the operation, a single representative in data 212 may be mapped to two or more amino acid sequences. As previously mentioned, conversion from a high dimensional space to a low dimensional space may be lossy. In some such cases, this may mean that any given sequence representative in the low dimensional space may be fuzzy and specifies two or more actual amino acids. In the example shown, one candidate sequence is used to create a new amino acid sequence, and another candidate sequence is used to create two new amino acid sequences, but more than two amino acid sequences may also be created.

[0122] Due to the constraints of the data processing, the new amino acid sequence in data 110 may maintain a certain degree of similarity (e.g., defined by edit distance or other metrics) with the wild-type amino acid sequence in data 104. For clarity, the differences between data 104 and 110 have been presented by bolding certain letters in data 110.

[0123] It will be understood that one of the new amino acid sequences in data 110 may be identical to one of the wild-type amino acid sequences in data 104, but this is not required. In addition, one of the new amino acid sequences 110 may be identical to a wild-type amino acid sequence found in nature and not involved in the data operations described in this document, but this is not required. Further, one of the new amino acid sequences 110 may be identical to another new amino acid sequence previously created using this data processing operation or another data processing operation, tested for potential as a vaccine and discarded (e.g., due to low efficacy, safety issues, inability to manufacture, or other undesirable properties), but this is not required. Therefore, in some cases, data 110 can be filtered to remove known new amino acid sequences, leaving only unknown or unanalyzed amino acid sequences.

[0124] Figure 3 is a flow chart of an example process 300 that can be used to process high-dimensional data in a lower-dimensional space (such as can be used to make vaccines). For example, process 300 can use Figure 1 and Figure 22 to 212), so the elements of these figures will be used in the description. Possible embodiments of the various elements of process 300 will be described later in processes 400 to 700.

[0125] One or more data objects defining a plurality of wild-type amino acid sequences are received 302. For example, computer system 106 accesses data 104 from a disk, receives data 104 via a network connection, and so on.

[0126] A plurality of reduced dimensionality sequences 304 are generated in the reduced dimensionality space. For example, computer system 106 may use one or more data processing operations that use data 104 as input and produce data 202 as output. In doing so, computer system 106 may embed variability into data 202. Examples of 304 will be described in more detail in process 400.

[0127] One or more data objects defining the amino acid sequence of the virus are received 306. For example, computer system 106 accesses data 102 from a disk, receives data 104 via a network connection, and so on.

[0128] At least one reduced dimension virus sequence 308 is generated in the reduced dimension space. For example, computer system 106 may use one or more data processing operations that use data 102 as input and produce data 208 as output. In some cases, computer system 106 may embed variability into data 208 in the same manner as performed in 304 (e.g., see process 400). In other examples, the computing system embeds variation in a different manner or does not embed variation at all.

[0129] Multiple candidate sequences are generated in the reduced dimensionality space using the multiple reduced dimensionality sequences 310. For example, computer system 106 may analyze data 202 to generate data 204. To do so, computer system 106 may characterize the values ​​of each vector of data 202 and record these characterizations in data 204. In some cases, computer system 106 creates the multiple candidate sequences in the reduced dimensionality space by sampling the distribution of the values ​​of the multiple reduced dimensionality sequences.

[0130] Each candidate sequence is scored to generate a candidate score 312. For example, the computer system 106 can analyze the data 206 and 208 to generate the data 210. In some cases, the computer system 106 can use a predictor or classifier that has been trained on the amino acid history data in a low-dimensional space to generate a prediction of a biological response (e.g., the strength of the antibody produced by the subject). Examples of 312 will be described in more detail in process 500.

[0131] At least one candidate sequence is selected as selected candidate sequence 314. For example, computing system 106 can generate data 212 from data 210. Computing system 106 can use, for example, the candidate score to select the selected candidate sequence. Examples of 314 will be described in more detail in process 600.

[0132] At least one new amino acid sequence 316 is generated for each selected candidate sequence. For example, the computing system 106 can generate data 110 from the data 212 and provide the data 110 for use in making a vaccine. To this end, the computing system 106 can find a point or vector in the high-dimensional space that corresponds to a point or vector in the data 212. As will be appreciated, the projection of a vector from a low-dimensional space to a high-dimensional space can define a result area rather than a single point result. Therefore, in some cases, the computer system 106 can generate each valid amino acid sequence in the result area, resulting in each vector in the data 212 having more than one new amino acid sequence in the data 110.

[0133] Vaccine 318 is manufactured for each new amino acid sequence. The vaccine may include a protein defined by the new amino acid sequence, and / or a nucleic acid or any other delivery vehicle (including a viral or bacterial vector), wherein such a nucleic acid or delivery vehicle produces a protein 318 defined by the new amino acid sequence. For example, computing device 106 and / or vaccine manufacturing device 112 may be operated to create vaccine 116 using data 110 and vaccine precursor 114. Such manufacturing may be in small batches for preliminary testing, clinical trials, and / or general use. As will be appreciated, elements 316 and 318 may be separated by a large amount of time and gap operations. For example, if the manufacturing in 318 is for large-scale manufacturing for general use, it may only be carried out after clinical trials have proven that the vaccine is safe and effective for its intended purpose.

[0134] Figure 4 is a flow chart of an example process 400 that can be used to process high-dimensional data in a lower-dimensional space (such as can be used to make vaccines), the process including using a variational autoencoder that predicts the mean and variance values ​​of the input data to create a representation of the wild-type amino acid sequence. For example, the process 400 can use Figure 1 and Figure 2 4 and 5. Therefore, elements of these figures will be used in the description. Process 400 is a possible example of how operation 304 may be performed, but other processes may also be used.

[0135] One or more variational autoencoders (which are discussed in further detail below) are accessed 402. For example, the computer system 106 accesses data for the autoencoder from disk, receives data over a network connection, etc. The data may define one or more functions, libraries, modules, etc. that operate on input data and return output data.

[0136] The variational autoencoder creates a low-dimensional representation of the amino acid sequence 404. For example, computing system 106 can execute the variational autoencoder using data 104 to create data 202.

[0137] Receive the reduced dimensionality sequence 406. For example, the computing system 106 may receive the data 202 from the variational autoencoder, which may include accessing the data from a disk, receiving the data 202 over a network connection, and so on.

[0138] Figure 5 is a flow chart of an example process 500 that can be used to process high-dimensional data in a lower-dimensional space (such as can be used to make vaccines). For example, process 500 can use Figure 1 and Figure 2 312 , and thus elements of these figures will be used in the description. Process 500 is one possible example of how operation 312 may be performed, but other processes may also be used.

[0139] Each candidate sequence and reduced-dimensionality virus sequence is provided as input to the antibody potency predictor 502. For example, the computer system 106 accesses data for the potency predictor from a disk, receives data through a network connection, etc. The data may define one or more functions, libraries, modules, etc. that operate on input data and return output data. This operation may be performed sequentially for one or more reduced-dimensionality virus sequences.

[0140] Generate predictions 504 using valence predictor. For example, computing system 106 can execute valence predictor using data 206 and 208 to create data 210.

[0141] The candidate score for each candidate sequence is received as output from the valence predictor 506. For example, the computing system 106 can receive data 210 from the valence predictor, which can include accessing the data from a disk, receiving the data 202 over a network connection, and so on.

[0142] Figure 6 is a flow chart of an example process 600 that can be used to process high-dimensional data in a lower-dimensional space (such as can be used to make vaccines). For example, process 600 can use Figure 1 and Figure 2 , and therefore elements of these figures will be used in the description. Process 600 is one possible example of how operation 314 may be performed, but other processes may also be used.

[0143] Sort candidate sequence by candidate score 602. For example, computer system 106 may sort data 210 into a list in memory such that the candidate score of each entry in the list is greater than or equal to (or less than or equal to) the candidate score of the subsequent entry.

[0144] The highest candidate scores are identified 604. For example, the computer system 106 may identify some candidate sequence:candidate score pairs from the beginning (or end) of the list. In some cases, the computer system 106 selects the N pairs with the highest candidate scores, where N is some positive integer value. In some cases, the computer 106 selects all pairs with candidate scores greater than a threshold, where the threshold is less than the maximum possible candidate score and greater than the minimum possible candidate score.

[0145] The candidate sequence corresponding to the highest candidate score is selected 606. For example, the computer system 106 can select the candidate sequence among the identified sequence:candidate score pairs.

[0146] Figure 7 704, and immune response predictor 706, although other computing architectures may also be used. Each element 702 to 706 may be embodied as a computer system 106 that is executed in the computer system 106 and is capable of transmitting, storing, and operating data (e.g., Figure 1 and Figure 2 106 ). As will be appreciated, the various elements 702 to 706 may be run on hardware that is remote from the hardware running the other elements of the computing system 106 .

[0147] Data handler 702 operates in computing system 106 to handle data operations (such as accessing data on disk, transferring data over network connections within computing system 106), and manipulating data (such as in 302, 310, 314, and 316), among other operations.

[0148] The variational self-encoder 704 includes one or more computational models, such as linear support vector machines (linear SVM), enhancements of other algorithms (e.g., AdaBoost), neural networks, logistic regression, naive Bayes, memory-based learning, random forests, bagged trees, decision trees, enhanced trees, or enhanced stumps. These models can operate on input data of a given dimension and generate corresponding output data of input data of a lower dimension. The variational self-encoder 704 can compress the input information into a constrained multivariate potential distribution, and these data can also be reconstructed into the format of the input. Some embodiments of the variational self-encoder can operate on input data characterized by an unknown probability distribution and approximate the distribution of the data. The gap operations of the encoding and reconstruction functions include but are not limited to predicting the mean and variance values ​​of the input data.

[0149] The potency predictor 706 includes one or more computational models, such as linear support vector machines (linear SVM), enhancements of other algorithms (e.g., AdaBoost), neural networks, logistic regression, naive Bayes, memory-based learning, random forests, bagged trees, decision trees, boosted trees, or boosted tree stumps. These models may have been trained on sequence data sets in low-dimensional space that have been labeled with outcome values ​​indicating a biological response, such as antibody titer, when the sequence is introduced into a subject (e.g., a human, a mammal, a patient).

[0150] Figure 8 An example of a computing device 800 and an example of a mobile computing device that can be used to implement the techniques described herein are shown. Computing device 800 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Mobile computing devices are intended to represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions are exemplary only and are not intended to limit the embodiments of the present invention described and / or claimed in this document.

[0151] The computing device 800 includes a processor 802, a memory 804, a storage device 806, a high-speed interface 808 connected to the memory 804 and a plurality of high-speed expansion ports 810, and a low-speed interface 812 connected to the low-speed expansion port 814 and the storage device 806. Each of the processor 802, the memory 804, the storage device 806, the high-speed interface 808, the high-speed expansion port 810, and the low-speed interface 812 is interconnected using various buses and can be installed on a common motherboard or in other appropriate ways. The processor 802 can process instructions for execution within the computing device 800, including instructions stored in the memory 804 or on the storage device 806, to display graphical information of a GUI on an external input / output device (such as a display 816 coupled to the high-speed interface 808). In other embodiments, multiple processors and / or multiple buses, as well as multiple memories and multiple types of memories, can be used as needed. In addition, multiple computing devices can be connected, wherein each device provides part of the necessary operations (for example, as a server group, a group of blade servers, or a multi-processor system).

[0152] The memory 804 stores information within the computing device 800. In some implementations, the memory 804 is one or more volatile memory units. In some implementations, the memory 804 is one or more non-volatile memory units. The memory 804 may also be other forms of computer-readable media, such as a magnetic disk or an optical disk.

[0153] The storage device 806 can provide mass storage for the computing device 800. In some embodiments, the storage device 806 can be or include a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a magnetic tape device, a flash memory or other similar solid-state memory device, or an array of devices, including devices in a storage area network or other configuration. A computer program product can be tangibly embodied in an information carrier. A computer program product can also include instructions that, when executed, perform one or more methods, such as the above-described methods. A computer program product can also be tangibly embodied in a computer or machine-readable medium, such as the memory 804, the storage device 806, or a memory on the processor 802.

[0154] The high-speed interface 808 manages bandwidth-intensive operations of the computing device 800, while the low-speed interface 812 manages less bandwidth-intensive operations. This allocation of functions is exemplary only. In some embodiments, the high-speed interface 808 is coupled to the memory 804, the display 816 (e.g., through a graphics processor or accelerator), and to a high-speed expansion port 810 that can accept various expansion cards (not shown). In an embodiment, the low-speed interface 812 is coupled to the storage device 806 and the low-speed expansion port 814. The low-speed expansion port 814 (which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet)) can be coupled to one or more input / output devices, such as a keyboard, a pointing device, a scanner, or a network device (such as a switch or router), for example, through a network adapter.

[0155] The computing device 800 can be implemented in a variety of different forms, as shown. For example, it can be implemented as a standard server 820, or multiple times in a group of such servers. In addition, it can also be implemented in a personal computer (such as a laptop computer 822). It can also be implemented as part of a rack server system 824. Alternatively, the components in the computing device 800 can be combined with other components in a mobile device (not shown) (such as a mobile computing device 850). Each such device can include one or more of the computing device 800 and the mobile computing device 850, and the entire system can be composed of multiple computing devices that communicate with each other.

[0156] The mobile computing device 850 includes a processor 852, a memory 864, an input / output device (such as a display 854), a communication interface 866, and a transceiver 868, among other components. The mobile computing device 850 may also be equipped with a storage device, such as a micro drive or other device, to provide additional storage. Each of the processor 852, the memory 864, the display 854, the communication interface 866, and the transceiver 868 are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other suitable ways.

[0157] The processor 852 can execute instructions within the mobile computing device 850, including instructions stored in the memory 864. The processor 852 can be implemented as a chipset including separate multiple analog and digital processors. The processor 852 can provide, for example, coordination of other components of the mobile computing device 850, such as control of a user interface, applications running on the mobile computing device 850, and wireless communications of the mobile computing device 850.

[0158] The processor 852 can communicate with the user through a control interface 858 and a display interface 856 coupled to the display 854. The display 854 can be, for example, a TFT (thin film transistor liquid crystal display) display or an OLED (organic light emitting diode) display, or other appropriate display technology. The display interface 856 may include appropriate circuits for driving the display 854 to present graphical information and other information to the user. The control interface 858 can receive commands from the user and convert them for submission to the processor 852. In addition, the external interface 862 can provide communication with the processor 852 to enable near-area communication of the mobile computing device 850 with other devices. The external interface 862 can, for example, provide wired communication in some embodiments, or provide wireless communication in other embodiments, and multiple interfaces can also be used.

[0159] The memory 864 stores information within the mobile computing device 850. The memory 864 may be implemented as one or more computer-readable media, one or more volatile memory units, or one or more non-volatile memory units. An expansion memory 874 may also be provided and connected to the mobile computing device 850 via an expansion interface 872, which may include, for example, a SIMM (single in-line memory module) card interface. The expansion memory 874 may provide additional storage space for the mobile computing device 850, and may also store applications or other information for the mobile computing device 850. Specifically, the expansion memory 874 may include instructions for executing or supplementing the above-mentioned processes, and may also include security information. Thus, for example, the expansion memory 874 may be provided as a security module for the mobile computing device 850, and may be programmed with instructions that allow the secure use of the mobile computing device 850. In addition, secure applications and other information may also be provided via a SIMM card, such as placing identification information on the SIMM card in an unbreakable manner.

[0160] The memory may include, for example, flash memory and / or NVRAM memory (non-volatile random access memory), as discussed below. In some embodiments, a computer program product is tangibly embodied in an information carrier. The computer program product includes instructions that, when executed, perform one or more methods, such as the methods described above. The computer program product may be a computer or machine readable medium, such as the memory 864, the expansion memory 874, or a memory on the processor 852. In some embodiments, the computer program product may be received, for example, via the transceiver 868 or the external interface 862 in the form of a propagated signal.

[0161] The mobile computing device 850 can communicate wirelessly via a communication interface 866, which may include digital signal processing circuitry when necessary. The communication interface 866 can provide communications in various modes or protocols, such as GSM voice calls (Global System for Mobile Communications), SMS (Short Message Service), EMS (Enhanced Short Message Service), or MMS messages (Multimedia Short Message Service), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), etc. Such communications can be performed, for example, using radio frequencies via a transceiver 868. In addition, short-range communications can also be performed, such as using Bluetooth, WiFi, or other such transceivers (not shown). In addition, a GPS (Global Positioning System) receiver module 870 can provide additional wireless data related to navigation and location to the mobile computing device 850, which can be used appropriately by applications running on the mobile computing device 850.

[0162] Mobile computing device 850 may also communicate audio using audio codec 860, which may receive spoken information from a user and convert it into usable digital information. Audio codec 860 may also generate audible sounds for the user, such as through a speaker (e.g., in a receiver of mobile computing device 850). Such sounds may include sounds from voice phone calls, may include recorded sounds (e.g., voice messages, music files, etc.), and may also include sounds generated by applications running on mobile computing device 850.

[0163] Mobile computing device 850 can be implemented in many different forms, as shown in the figure. For example, it can be implemented as a cellular phone 880. It can also be implemented as a part of a smart phone 882, a personal digital assistant or other similar mobile device.

[0164] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system that includes at least one programmable processor that can be special purpose or general purpose, coupled to receive data and instructions from a storage system, at least one input device, and at least one output device, and to transmit data and instructions to the storage system, at least one input device, and at least one output device.

[0165] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and may be implemented in high-level procedural and / or object-oriented programming languages ​​and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., magnetic disks, optical disks, memories, programmable logic devices (PLDs)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0166] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube), or LCD (liquid crystal display) monitor) for displaying information to the user, and a keyboard and pointing device (e.g., a mouse or trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including sound, voice, or tactile input.

[0167] The systems and techniques described herein can be implemented in a computing system that includes a back-end component (e.g., as a data server) or includes a middleware component (e.g., an application server), or includes a front-end component (e.g., a client computer with a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any digital data communication form or medium (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0168] A computing system may include clients and servers. Clients and servers are generally remote from each other and generally interact through a communication network. The relationship of client and server arises from computer programs running on the respective computers and having a client-server relationship to each other.

Claims

1. A dimensionality reduction method for generating an amino acid sequence, the method being performed by a system of one or more computers, and the method comprising: receiving one or more data objects defining a plurality of wild-type amino acid sequences; A plurality of reduced-dimensionality sequences are generated in the reduced-dimensionality space from the one or more data objects, wherein: Each reduced-dimensional sequence contains corresponding data for at least one of these wild-type amino acid sequences, The reduced dimensionality space has a lower dimension than the wild-type amino acid sequences, and The multiple reduced-dimensionality sequences define a distribution of values ​​along each dimension of the reduced-dimensionality space, and multiple candidate sequences are generated in the reduced-dimensionality space using the multiple reduced-dimensionality sequences; receiving one or more data objects defining an amino acid sequence of a virus; generating at least one dimension-reduced virus sequence in the dimension-reduced space; providing each of the candidate sequences and at least one of the reduced-dimensionality viral sequences as input to a potency predictor; receiving a candidate score for each of the candidate sequences as an output from the valence predictor; selecting at least one candidate sequence from the candidate sequences; generating at least one new amino acid sequence for each selected candidate sequence; and providing the generated at least one amino acid sequence; Wherein, each of these generated amino acid sequences is suitable for the manufacture of a corresponding vaccine, which comprises at least one of the group consisting of: i) a protein defined by these generated amino acid sequences, ii) a nucleic acid capable of producing the protein defined by these generated amino acid sequences, and iii) a delivery vehicle capable of producing the protein defined by these generated amino acid sequences.

2. The method of claim 1, wherein generating a plurality of reduced dimensionality sequences comprises creating representatives of the wild-type amino acid sequences using a variational autoencoder that predicts mean and variance values ​​of input data.

3. A method as claimed in the preceding claim, wherein each of the reduced dimensionality sequences comprises a corresponding set of values, and generating the plurality of candidate sequences in the reduced dimensionality space comprises sampling a distribution of values ​​of the plurality of reduced dimensionality sequences.

4. A method as claimed in any preceding claim, wherein the valence predictor is configured to: receiving as input i) a first sequence in the reduced dimensionality space and ii) a second sequence in the reduced dimensionality space; and A valence score is provided as an output as the candidate score, the valence score defining a measure of a biological reaction between the first sequence and the second sequence.

5. The method of any preceding claim, wherein selecting the at least one candidate sequence as the selected candidate sequence comprises selecting N candidate sequences having the highest candidate scores.

6. The method of claim 5, wherein the value of N is 1, thereby selecting a single candidate sequence.

7. The method of claim 5, wherein the value of N is greater than 1, thereby selecting a plurality of candidate sequences.

8. The method of any preceding claim, wherein selecting the at least one candidate sequence as the selected candidate sequence comprises selecting a candidate sequence having a corresponding candidate score greater than a threshold.

9. The method of any preceding claim, wherein each of the generated amino acid sequences differs from any of the wild-type amino acid sequences.

10. A method as claimed in any preceding claim, wherein at least one of the candidate sequences is in the plurality of reduced dimensionality sequences.

11. The method of any preceding claim, wherein the corresponding vaccine is against one of the group consisting of: i) influenza, ii) human rhinovirus, iii) HIV and iv) coronavirus disease.

12. A system for generating an amino acid sequence, the system comprising: one or more processors; as well as Computer memory storing instructions that, when executed by the processors, cause the processors to perform operations including: receiving one or more data objects defining a plurality of wild-type amino acid sequences; A plurality of reduced-dimensionality sequences are generated in the reduced-dimensionality space from the one or more data objects, wherein: Each reduced-dimensional sequence contains corresponding data for at least one of these wild-type amino acid sequences, The reduced dimensionality space has a lower dimension than the wild-type amino acid sequences, and The plurality of reduced dimensionality sequences define a distribution of values ​​along each dimension of the reduced dimensionality space, Using the multiple reduced-dimensionality sequences to generate multiple candidate sequences in the reduced-dimensionality space; receiving one or more data objects defining an amino acid sequence of a virus; generating at least one dimension-reduced virus sequence in the dimension-reduced space; providing each of the candidate sequences and at least one of the reduced-dimensionality viral sequences as input to a potency predictor; receiving a candidate score for each of the candidate sequences as an output from the valence predictor; selecting at least one candidate sequence from the candidate sequences; generating at least one new amino acid sequence for each selected candidate sequence; and providing at least one amino acid sequence generated, Wherein, each of these generated amino acid sequences is suitable for the manufacture of a corresponding vaccine, which comprises at least one of the group consisting of: i) a protein defined by these generated amino acid sequences, ii) a nucleic acid capable of producing the protein defined by these generated amino acid sequences, and iii) a delivery vehicle capable of producing the protein defined by these generated amino acid sequences.

13. The system of claim 12, wherein generating the plurality of reduced dimensionality sequences comprises creating representatives of the wild-type amino acid sequences using a variational autoencoder that predicts mean and variance values ​​of input data.

14. The system of any one of claims 12-13, wherein each of the reduced dimensionality sequences comprises a corresponding set of values, and generating the plurality of candidate sequences in the reduced dimensionality space comprises sampling a distribution of values ​​of the plurality of reduced dimensionality sequences.

15. The system of any one of claims 12-14, wherein the valence predictor is configured to: receiving as input i) a first sequence in the reduced dimensionality space and ii) a second sequence in the reduced dimensionality space; and A valence score is provided as an output as the candidate score, the valence score defining a measure of a biological reaction between the first sequence and the second sequence.

16. The system of any one of claims 12-15, wherein selecting the at least one candidate sequence as the selected candidate sequence comprises selecting N candidate sequences having the highest candidate scores.

17. A non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving one or more data objects defining a plurality of wild-type amino acid sequences; A plurality of reduced-dimensionality sequences are generated in the reduced-dimensionality space from the one or more data objects, wherein: Each reduced-dimensional sequence contains corresponding data for at least one of these wild-type amino acid sequences, The reduced dimensionality space has a lower dimension than the wild-type amino acid sequences, and The multiple reduced-dimensionality sequences define a distribution of values ​​along each dimension of the reduced-dimensionality space, and multiple candidate sequences are generated in the reduced-dimensionality space using the multiple reduced-dimensionality sequences; receiving one or more data objects defining an amino acid sequence of a virus; generating at least one dimension-reduced virus sequence in the dimension-reduced space; providing each of the candidate sequences and at least one of the reduced-dimensionality viral sequences as input to a potency predictor; receiving a candidate score for each of the candidate sequences as an output from the valence predictor; selecting at least one candidate sequence from the candidate sequences; generating at least one new amino acid sequence for each selected candidate sequence; and providing at least one amino acid sequence generated, Wherein, each of these generated amino acid sequences is suitable for the manufacture of a corresponding vaccine, which comprises at least one of the group consisting of: i) a protein defined by these generated amino acid sequences, ii) a nucleic acid capable of producing the protein defined by these generated amino acid sequences, and iii) a delivery vehicle capable of producing the protein defined by these generated amino acid sequences.

18. The medium of claim 17, wherein generating the plurality of reduced dimensionality sequences comprises creating representatives of the wild-type amino acid sequences using a variational autoencoder that predicts mean and variance values ​​of input data.

19. The medium of any of claims 17-18, wherein each of the reduced dimensionality sequences comprises a corresponding set of values, and generating the plurality of candidate sequences in the reduced dimensionality space comprises sampling a distribution of values ​​of the plurality of reduced dimensionality sequences.

20. The medium of any one of claims 17-19, wherein the valence predictor is configured to: receiving as input i) a first sequence in the reduced dimensionality space and ii) a second sequence in the reduced dimensionality space; and A valence score is provided as an output as the candidate score, the valence score defining a measure of a biological reaction between the first sequence and the second sequence.

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