Protein structure prediction method, system and device based on light quantum computer

Abstract

The invention belongs to the technical field of protein structure prediction, and relates to a protein structure prediction method, system and device based on a light quantum computer, and the method comprises the steps: 1) obtaining a multi-sequence alignment MSA matrix of a target sequence based on the target sequence alignment of a to-be-predicted protein; 2) encoding the obtained multi-sequence alignment MSA matrix into a {0, 1} matrix; 3) converting the amino acid interaction of the protein into an undirected graph model of {0, 1} state nodes, and training by adopting a Boltzmann machine training mechanism and using a light quantum computer to obtain a weight coefficient of a connecting edge between the nodes in the undirected graph model; and 4) obtaining an interaction coefficient between different amino acids of the protein based on the weight coefficient. According to the method, the calculation efficiency and the solving result of protein structure prediction are improved, and the problems that complex scheduling problems are difficult to calculate and accurate solutions cannot be obtained in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of protein structure prediction, and relates to a protein structure prediction method, system and device, and in particular to a protein structure prediction method, system and device based on an optical quantum computer. Background Art

[0002] Protein structure prediction is of great importance in life science research. In the field of basic biology, proteins are the most important class of molecules in biological molecules. They are transcribed from DNA sequences to generate mRNA, which is then translated into proteins to perform their main functions. In biology, structure often determines the function of biological molecules. When mRNA is translated into the corresponding amino acid chain, it folds in space to form a specific 3D structure. Therefore, obtaining accurate spatial structure is the basis for understanding protein function and developing drugs.

[0003] The existing methods for obtaining protein structures include experimental methods and AI prediction methods. The experimental methods mainly include X-ray crystallography, nuclear magnetic resonance (NMR) and cryo-electron microscopy (Cryo-EM). By purifying and crystallizing protein samples and then using the above experimental devices for structural analysis, this method can obtain accurate three-dimensional structures, but it consumes a lot of manpower and financial resources, and some protein purifications have uncertainties, making it difficult to obtain relatively pure protein samples. Compared with traditional experimental methods, AI-based structure prediction methods represented by AlphaFold and RoseTTAFold show higher efficiency and can obtain the three-dimensional structure of proteins in a short time. However, there are still many shortcomings, mainly reflected in: 1. Insufficient sensitivity to mutations. These AI tools are not very sensitive to point mutations (single amino acid changes); 2. It is more difficult to predict dynamic regions. These AI tools target regions with fixed structures in their natural state. Therefore, current AI tools cannot accurately predict the structure of dynamic and disordered regions. The disordered structure of proteins plays an important role in protein functions, such as phase separation processes. Abnormal protein phase separation processes are significantly related to Alzheimer's disease and cancer. 3. The prediction of some rare proteins is not accurate. These proteins have low similarity with currently known structural proteins, so the prediction results are not accurate; 4. These AI tools still have limitations in simulating the physical interaction between drugs and proteins.

[0004] Therefore, in view of the defects existing in the above-mentioned prior art, it is necessary to develop a new protein structure prediction method, system and device. Summary of the invention

[0005] In order to overcome the defects of the prior art, the present invention proposes a protein structure prediction method, system and device based on an optical quantum computer, which improves the computational efficiency and solution results of protein structure prediction, and solves the problem that the prior art is difficult to calculate and cannot obtain accurate solutions for complex scheduling problems.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A protein structure prediction method based on an optical quantum computer, characterized in that it comprises the following steps:

[0008] 1) Obtaining a multiple sequence alignment MSA matrix of the target sequence based on the target sequence alignment of the protein to be predicted;

[0009] 2) Encoding the obtained multiple sequence alignment MSA matrix into a {0,1} matrix;

[0010] 3) converting the amino acid interactions of the protein into an undirected graph model of {0,1} state nodes and training it using a Boltzmann machine training mechanism using an optical quantum computer to obtain the weight coefficients of the edges between the nodes in the undirected graph model;

[0011] 4) Obtaining the interaction coefficients between different amino acids in the protein based on the weight coefficients of the edges between the nodes in the undirected graph model.

[0012] Preferably, the step 1) specifically includes:

[0013] 11) Download the target sequence of the protein to be predicted from the protein sequence database;

[0014] 12) Using the downloaded target sequence as input, the multiple sequence alignment tool mmseqs2 and the protein database Swiss-Prot database were used to align the sequence to obtain the multiple sequence alignment MSA matrix.

[0015] Preferably, the step 2) is specifically as follows: one-hot encoding the multiple sequence alignment MSA matrix encoded by the string, there are 21 choices for the amino acid at each position, for matrix alignment, the space character is represented by “-”, and an L*21-dimensional {0,1} matrix is ​​used to represent the information of a row in the multiple sequence alignment MSA matrix, and the multiple sequence alignment MSA matrix is ​​converted to obtain M L*21-dimensional {0,1} matrices, wherein L is the sequence length of the amino acid and M is the number of protein strips obtained by alignment.

[0016] Preferably, in the step 3), when converting the amino acid interactions of the protein into an undirected graph model of {0,1} state nodes, first, the amino acids of the protein sequence of the protein to be predicted are taken as vertices, and the interaction relationships between the amino acids are taken as edges to establish a fully connected undirected graph; then, since each amino acid is a 21-dimensional one-hot vector, each amino acid is converted into 21 {0,1} state nodes, and the connection between any two {0,1} state nodes is taken as an edge to establish the undirected graph model of the {0,1} state nodes, wherein the state of each state node in the undirected graph model of the {0,1} state nodes is 0 or 1, and the weight is h. i , the weight coefficient of the edge between any two state nodes is J ij .

[0017] Preferably, in step 3), when the Boltzmann machine training mechanism is used to perform training using an optical quantum computer to obtain the weight coefficients of the edges between the nodes in the undirected graph model, the training loss function C is:

[0018]

[0019] Where D kl represents the KL divergence, N(θ) represents the negative logarithmic conditional likelihood, and N DS represents the number of {0,1}} state nodes in the undirected graph model, α is a coefficient and α∈[0,1].

[0020] Preferably, the step 4) specifically includes:

[0021] 41) Based on the weight coefficient J of the edge between nodes in the undirected graph model ij , establish the weight coefficient matrix J;

[0022] 42) extracting a submatrix J′=[i*21:i*21+21,j*21:j*21+21] from the weight coefficient matrix J, wherein the submatrix represents the coupling information between the 21*21 {0,1} state nodes corresponding to the i-th amino acid and the 21*21 {0,1} state nodes corresponding to the j-th amino acid;

[0023] 43) The submatrix is ​​mean-centered as follows to obtain a mean-centered matrix J″:

[0024] J″=J′-μ0-μ1+μ

[0025] Where μ0 is the column mean, μ1 is the row mean, and μ is the overall mean of the submatrix;

[0026] 44) Calculate the interaction coefficient S between the i-th amino acid and the j-th amino acid by using the obtained mean-centered matrix J″ ij :

[0027] S ij =||J″||2.

[0028] In addition, the present invention also provides a protein structure prediction system based on an optical quantum computer, characterized in that it includes:

[0029] An alignment module, which is used to obtain a multiple sequence alignment MSA matrix of the target sequence based on the target sequence of the protein to be predicted;

[0030] An encoding module, which is used to encode the obtained multiple sequence alignment MSA matrix into a {0,1} matrix;

[0031] A weight coefficient acquisition module, which is used to convert the amino acid interactions of the protein into an undirected graph model of {0,1} state nodes and use a Boltzmann machine training mechanism to train using an optical quantum computer to obtain the weight coefficients of the edges between the nodes in the undirected graph model;

[0032] The interaction coefficient acquisition module is used to obtain the interaction coefficients between different amino acids in the protein based on the weight coefficients of the edges between the nodes in the undirected graph model.

[0033] Moreover, the present invention also provides a protein structure prediction device based on an optical quantum computer, characterized in that it comprises:

[0034] one or more processors;

[0035] A memory for storing one or more programs;

[0036] When the one or more programs are executed by the one or more processors, the one or more processors implement the protein structure prediction method based on optical quantum computer as described above.

[0037] Finally, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps of the protein structure prediction method based on an optical quantum computer as described above are implemented.

[0038] Compared with the prior art, the protein structure prediction method, system and device based on optical quantum computer of the present invention have one or more of the following beneficial technical effects:

[0039] 1. The present invention adopts a computational method to predict protein structure from sequence. Compared with traditional experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) and cryo-electron microscopy (Cryo-EM), it can obtain results in a short time, avoids the complicated steps of protein purification and expensive observation experimental equipment, and lowers the threshold for protein structure analysis.

[0040] 2. Compared with AI-based methods such as AlphaFold and RoseTTAFold, the present invention does not require a large number of high-quality training sets. Since some low-similarity proteins and high-complexity proteins do not have known structures that can be trained, these AI-based methods cannot accurately predict the structures of these proteins. The present invention does not rely on known structural data, but only needs to combine protein evolution-related information to train and obtain relevant parameters.

[0041] 3. All current technical methods, whether experimental or AI-based, only analyze protein structures for segments with fixed structures, but lack structural analysis of disordered segments of proteins. Disordered segments are crucial for proteins to function normally, and can promote processes such as protein phase separation, which is related to various diseases such as Alzheimer's disease, diabetes, and cardiovascular diseases. Therefore, the limitations of existing methods make it impossible to analyze the structure of disordered segments, so that no related drugs have been developed targeting disordered segments. The present invention can perform structural distribution sampling of disordered segments through energy minimization, thereby obtaining the structural state of the disordered segments.

[0042] 4. During the training process of the Boltzmann machine, the traditional method based on contrastive divergence uses other distributions to approximate the Boltzmann distribution. At the same time, due to the parameter complexity brought by full connection, the Boltzmann machine model has not been widely used. The present invention uses an optical quantum computer and directly performs energy sampling based on the quantum computer to obtain the true Boltzmann distribution, and increases the number of nodes of the Boltzmann machine model to thousands, while the traditional method is difficult to converge at twenty nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of the protein structure prediction method based on optical quantum computer of the present invention.

[0044] Figure 2 The following are some exemplary MSA matrices obtained by alignment.

[0045] Figure 3 A schematic diagram showing the encoding of the MSA matrix into a {0,1} matrix is ​​shown.

[0046] Figure 4 It is a schematic diagram of an exemplary undirected graph model of the present invention for converting amino acid interactions into {0, 1} state nodes.

[0047] Figure 5 This is a schematic diagram of extracting the interaction coefficients between amino acids from the weight coefficient matrix of the edges between nodes in the trained undirected graph model.

[0048] Figure 6 A schematic diagram of the composition of the protein structure prediction system based on an optical quantum computer of the present invention is shown. DETAILED DESCRIPTION

[0049] Before describing in detail any embodiment of the present invention, it should be understood that the present invention is not limited in its application to the construction and arrangement details of the components described below or illustrated in the following figures. The present invention can have other embodiments and can be practiced or carried out in various ways. In addition, it should be understood that the words and terms used here are for descriptive purposes and should not be considered restrictive. The use of "including" or "having" and its variations herein is intended to cover the items and their equivalents and additional items displayed below. Unless otherwise specified or limited, the terms "install", "connect", "support" and "couple" and their variations are widely used and cover direct installation and indirect installation, connection, support and connection. In addition, "connect" and "couple" are not limited to physical or mechanical connections or connections.

[0050] Furthermore, in the disclosure of the present invention, the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0051] During the evolution of proteins, amino acid mutations will occur, destroying the interactions between residues and causing protein structural instability. If the residues that interact with the mutated residues also mutate and form new interactions, the protein structure remains stable. This change is called protein coevolution. Therefore, by using protein evolutionary information and comparing the sequences of a large number of homologous proteins, we can find the common evolutionary amino acid pairs, and then infer the amino acid residues that are close to each other in the protein 3D structure. Based on the global amino acid interaction information, we can get the spatial structure of the protein.

[0052] Based on the above principles, the present invention provides a protein structure prediction method, system and device based on an optical quantum computer. The method binarizes the protein sequence alignment data and introduces a Boltzmann machine training mechanism, designs a mathematical model suitable for quantum computing training, and then uses a quantum computer to sample and solve the problem, thereby improving the computational efficiency and solution results of protein structure prediction, and solving the problem that the prior art is difficult to calculate and cannot obtain accurate solutions for complex scheduling problems.

[0053] Figure 1 The flowchart of the protein structure prediction method based on optical quantum computer of the present invention is shown. Figure 1 As shown, the protein structure prediction method based on optical quantum computer of the present invention comprises the following steps:

[0054] 1. Based on the target sequence alignment of the protein to be predicted, a multiple sequence alignment MSA matrix of the target sequence is obtained.

[0055] In the present invention, the target sequence of the protein to be predicted is first obtained, for example, a sequence file in .fa format is downloaded from a protein sequence database such as SWISS-PROT, which contains the amino acid sequence information of the protein to be predicted. As an example, the specific format of the full-length amino acid sequence file of the human FIND3A protein with the Fibronection_III domain is shown below.

[0056] >sp|Q9Y2H6|FND3A_HUMAN Fibronectin type-III domain-containinggprotein 3A OS=Homo sapiens OX=9606 GN=FNDC3A PE=1 SV=4

[0057]

[0058] Next, the protein sequence obtained above was used as input, and the multiple sequence alignment tool mmseqs2s and the protein database Swiss-Prot database were used to align the multiple sequence alignment (MSA) matrix, as shown in Figure 2 As shown. Figure 2 In , each row represents the protein sequence of a species. Figure 2 The length of the amino acid sequence is L, that is, it has L amino acids, and the number of proteins obtained by alignment is M, that is, there are M homologous proteins.

[0059] Second, encode the obtained multiple sequence alignment MSA matrix into a {0,1} matrix.

[0060] The multiple sequence alignment MSA matrix is ​​a string code. In the present invention, the multiple sequence alignment MSA matrix of string code needs to be one-hot encoded. There are 21 choices of amino acids at each position (that is, there are 21 amino acid types to choose from). For matrix alignment, the space character is represented by "-". An L*21-dimensional {0,1} matrix is ​​used to represent the information of a row in the multiple sequence alignment MSA matrix. The multiple sequence alignment MSA matrix is ​​converted to obtain M L*21-dimensional {0,1} matrices, where L is the sequence length of the amino acid and M is the number of protein strips obtained by alignment. For example, Figure 3 In the left half of the MSA matrix, each row in the MSA matrix is ​​represented by an L*21 {0,1} matrix, and the one-hot matrix in the right half is obtained, that is, a {0,1} matrix.

[0061] Among them, in the L*21-dimensional {0,1} matrix, one dimension in each row represents an amino acid type. During the conversion, the type of amino acid corresponding to each row is determined, so the state of the dimension in the row corresponding to the amino acid type is 1, and the remaining 20 dimensions are 0.

[0062] 3. Convert the amino acid interactions of the protein into an undirected graph model with {0, 1} state nodes and use a Boltzmann machine training mechanism to train using an optical quantum computer to obtain the weight coefficients of the edges between the nodes in the undirected graph model.

[0063] For a stable protein, based on the thermodynamic statistical model, it can be assumed that the protein amino acid sequence obeys the Boltzmann distribution:

[0064]

[0065] Where P represents the global probability of amino acid distribution, a1,…,a L represents the specific types of amino acids from position 1 to position L, and Z represents the partition function.

[0066]

[0067] Where Z is the partition function; β is the inverse temperature; E i It is the energy of a system in a certain state. In the present invention, it refers to the energy corresponding to a certain amino acid distribution.

[0068] H(a1…a L ) represents the sum of the energies of all amino acids under the Boltzmann distribution, including the energy of monomeric amino acids and the interaction energy between two amino acids. The specific expression can be written as:

[0069]

[0070] In the formula, a i represents the amino acid at position i, a j represents the amino acid at position j, h i is the energy of the amino acid at position i, J ij For amino acid pairs {a i ,a j}, that is, the interaction energy between the amino acid at the i-th position and the amino acid at the j-th position.

[0071] The goal of the present invention is to learn the parameter θ={h i ,J ij}, so that

[0072] arg maxp(x|θ)=arg maxΠp(x i |θ)=argmax∑logp(x i |θ)(4)

[0073] In the formula, p(x|θ) represents the value of i ,J ij}, that is, the joint probability distribution of M data; p(x i |θ) means that in the parameter θ={h i ,J ij}Next i The probability of occurrence, in the present invention, x i represents the state distribution corresponding to an amino acid data; in this parameter θ={h i ,J ij}H(a1,...,a L ) to obtain the minimum value.

[0074] In order to train the above parameters from the MSA matrix θ = {h i ,J ij}, the present invention uses a fully connected undirected graph model, that is, an undirected graph model with {0,1} state nodes, to represent the connection and interaction rules of protein amino acid residues, and maps the overall energy function of formula (3) into the exp space.

[0075] Specifically, when converting the amino acid interactions of proteins into an undirected graph model of {0,1} state nodes, first, the amino acids of the protein sequence of the protein to be predicted are taken as vertices, and the interaction relationships between the amino acids are taken as edges to establish a fully connected undirected graph; then, since each amino acid is a 21-dimensional one-hot vector, each amino acid is converted into 21 {0,1} state nodes, and the connection between any two {0,1} state nodes is taken as an edge to establish the undirected graph model of the {0,1} state nodes, wherein the state of each state node in the undirected graph model of the {0,1} state nodes is 0 or 1, and the weight is h. i , the weight coefficient of the edge between any two state nodes is J ij .

[0076] For example, assuming the protein sequence is ABCD, that is, there are four amino acids, A, B, C and D, it can be converted into a fully connected undirected graph, such as Figure 4 shown. Figure 4 The four vertices in the left graph correspond to the four amino acids in the protein sequence. Figure 4 The six edges between the vertices in the left figure represent the interaction between amino acids. The amino acid at each vertex is a 21-dimensional one-hot vector, which can be converted into Figure 4 The visible layer in the middle right figure (note, for simplicity, Figure 4 The middle right figure only shows some nodes in the visible layer). Figure 4 The value of each node in the visible layer of the middle right figure is {0,1}, for example, the node like but is 1, indicating that the type of the amino acid at the i-th node is α, otherwise it is 0. Then the interaction relationship of the four amino acids is finally converted into an undirected graph model with 4*21=84 {0,1} state nodes. The state of each node in the undirected graph model is 0 or 1, corresponding to the one-hot matrix encoded by each amino acid in the MSA matrix. The final parameter θ={h i ,J ij}. Among them, h i represents the weight of the i-th node in the undirected graph model, J ij Represents the edge weight, which corresponds to the interaction coefficient between the i-th node and the j-th node in the undirected graph model.

[0077] In order to solve the parameter θ = {h i ,J ij}, the present invention introduces the training mechanism of Boltzmann machine, that is, the undirected graph model of {0,1} state nodes is used as the Boltzmann machine model for training. During training, the nodes in the undirected graph model of {0,1} state nodes can be regarded as visible layer nodes, and a small number of hidden layer nodes are additionally introduced, such as Figure 4 As shown in the right figure. According to the characteristics of the converted one-hot encoded input data, the visible layer nodes are randomly divided into input nodes and output nodes in half. For example, if the number of nodes in the visible layer is 1000, 500 nodes can be used as visible layer input nodes, and another 500 nodes can be used as visible layer output nodes, and about 20 hidden layer nodes are introduced. Due to the increase in parameter complexity caused by full connection, the training effect of the Boltzmann machine model of the traditional method is not ideal. At the same time, because the partition function is difficult to handle, other methods are often used to make the node state value probability approach the Boltzmann distribution, resulting in unsatisfactory final network parameter training results.

[0078] To this end, the present invention proposes to perform parameter training of the Boltzmann machine model based on an optical quantum computer, and the training loss function C uses weighted KL divergence and negative log-likelihood, specifically:

[0079]

[0080] Where D kl represents the KL divergence, N(θ) represents the negative logarithmic conditional likelihood, and N DS represents the number of visible layer nodes, that is, the number of {0,1} state nodes in the undirected graph model, α is a coefficient and α∈[0,1], and its specific value can be set as needed.

[0081] The specific training process is as follows: First, initialize the parameter θ = {h i ,J ij}, that is, manually given parameter h i ,J ij , and the {0,1} matrix obtained by converting the MSA matrix is ​​used as input, and the initial parameters θ={h i ,J ij The Ising matrix obtained by encoding the Ising model using the following formula (6) is sampled on an optical quantum computer to obtain the state of the {0,1} node with the lowest energy of the undirected graph model of the {0,1} state node and the probability of each state combination obtained through energy distribution.

[0082] H=min(-∑h i x i +∑J ij xi x j ) (6)

[0083] In the formula, H represents the energy state corresponding to the amino acid combination; x i represents the state of the amino acid at position i, x j represents the state of the amino acid at the jth position, with a value of {0,1}; h i is the energy of the amino acid at the ith position; J ij For amino acid pairs {a i ,a j}, that is, the interaction energy between the amino acid at the i-th position and the amino acid at the j-th position.

[0084] The Ising matrix is ​​obtained by encoding the Ising model mentioned above, and is input into the optical quantum computer for calculation and sampling. The states of different amino acid combinations are output, and the probabilities corresponding to different state combinations are obtained through energy distribution. The KL divergence is calculated by the state combination distribution output by the Ising model in formula (6) and the input data distribution. The calculation formula is as follows:

[0085]

[0086] Among them, D kl represents the KL divergence to be calculated, that is, the KL divergence of the probability distribution of the original data of the {0,1} matrix and the probability distribution of the data output by the optical quantum computer through the Ising model, p(x) is the probability distribution of the original data of the {0,1} matrix, q(x) is the probability distribution of the data output by the optical quantum computer through the Ising model, that is, the state combination distribution.

[0087] The negative logarithmic conditional likelihood N(θ) value can be obtained by the following method: iteratively calculate the {0,1} matrix obtained by the input M MSA matrices, and use the optical quantum computer to infer the output {0,1} state and the probability of different state combinations from the {0,1} state input in the visible layer, thereby calculating the negative logarithmic conditional likelihood N(θ) value. The calculation method is as follows:

[0088]

[0089] Where N(θ) is the value of i ,J ij The negative log-conditional likelihood value under}, v I ,v O Respectively represent the state of the visible layer input node and the state of the visible layer output node, p(v O |v O ; θ) means that the parameter θ={h i ,J ij}Under the undirected graph model, the input is v I When v appears O The conditional probability, v D Represents the state of the Dth input node of the visible layer.

[0090] The gradient is calculated by the state combination of the sampled nodes and their corresponding probabilities. The specific calculation formula is:

[0091]

[0092] Wherein, α is a coefficient and α∈[0,1], and its specific value can be set as needed, which is the same as α in formula (5); represents the expectation of the data distribution of all nodes (including visible layer nodes and hidden layer nodes) output by the optical quantum computer based on formula (6), which is the product of the data distribution of all nodes output and the probability of the state distribution; D represents the input data set, that is, M one-hot matrices; v represents a single input data, that is, a single one-hot matrix data; q(v) represents the probability distribution of a single one-hot input data; N DS Indicates the number of visible layer nodes; represents the expectation of the visible layer node data distribution output by the optical quantum computer based on formula (6), which is the product of the output visible layer node data distribution and the probability of the state distribution; Represents the expectation of the visible layer input node data distribution output by the optical quantum computer based on formula (6), which is the product of the output visible layer input node data distribution and the probability of the state distribution.

[0093] Based on the above calculated gradient update parameter θ = {h i ,J ij}:

[0094]

[0095] Among them, θ new is the updated parameter, and γ is the learning rate, which is an adjustable parameter.

[0096] Then, the training is iterated continuously through M one-hot data input until convergence, and the optimal parameter θ = {h i ,J ij}, thereby obtaining the weight coefficient J of the edge between nodes in the undirected graph model ij .

[0097] Fourth, based on the weight coefficients of the edges between the nodes in the undirected graph model, the interaction coefficients between different amino acids in the protein are obtained.

[0098] Through the training method in step 3, the weight coefficient J of the edge between 21*L state nodes in the undirected graph model is obtained ij ,Depend on Figure 5 As shown, finally the weight coefficient J between the edges of 21*L state nodes is ij Translated into interaction coefficients between L amino acids.

[0099] In the present invention, the specific conversion process is:

[0100] In order to get Figure 5 The interaction coefficients between amino acids in the right figure, such as the interaction coefficient S between the i-th amino acid and the j-th amino acid ij First, based on the weight coefficient J of the edge between nodes in the undirected graph model ij ,Establish Figure 5 The weight coefficient matrix J is shown in the left figure.

[0101] Secondly, from Figure 5 In the undirected graph model in the left figure, the submatrix J′=[i*21:i*21+21,j*21:j*21+21] is extracted from the weight coefficient matrix J of the edges between nodes. This submatrix represents the coupling information between the 21*21 one-hot encoded nodes corresponding to the i-th amino acid and the 21*21 one-hot encoded nodes corresponding to the j-th amino acid.

[0102] Then, the submatrix J′ is mean-centered as follows:

[0103] J″=J′-μ0-μ1+μ

[0104] Where μ0 is the column mean, μ1 is the row mean, and μ is the overall mean of the submatrix.

[0105] Finally, the interaction coefficient between the ith amino acid and the jth amino acid is calculated by the mean-centered matrix J″. The specific calculation method is:

[0106] S ij =||J″||2.

[0107] The present invention uses the co-evolution information of proteins, and through sequence alignment of a large number of homologous proteins, finds the common evolutionary amino acid pairing, which can infer the amino acid residues close to each other in the protein 3D structure, and the spatial structure of the protein can be obtained based on the global amino acid interaction information. At the same time, the present invention encodes the amino acid position and type selection into a {0,1} state, and converts it into a fully connected Boltzmann machine model (i.e., an undirected graph model). By establishing an Ising model and using an optical quantum computer for parameter training, the interaction relationship between amino acid residues is finally obtained. The present invention can sample the structural state of the disordered region of the protein without relying on the known protein structure, and at the same time increase the scale of nodes that can be trained by the Boltzmann machine model to thousands.

[0108] In addition, the present invention also provides a protein structure prediction system based on an optical quantum computer.

[0109] like Figure 6 As shown, the protein structure prediction system based on optical quantum computer of the present invention comprises:

[0110] 1. Comparison module.

[0111] The alignment module is used to obtain a multiple sequence alignment MSA matrix of the target sequence based on the target sequence alignment of the protein to be predicted.

[0112] 2. Encoding module.

[0113] The editing module is used to encode the obtained multiple sequence alignment MSA matrix into a {0,1} matrix.

[0114] 3. Weight coefficient acquisition module.

[0115] The weight coefficient acquisition module is used to convert the amino acid interactions of the protein into an undirected graph model of {0,1} state nodes and adopt a Boltzmann machine training mechanism to train using an optical quantum computer to obtain the weight coefficients of the edges between the nodes in the undirected graph model.

[0116] 4. Interaction coefficient acquisition module.

[0117] The interaction coefficient acquisition module is used to obtain the interaction coefficients between different amino acids in the protein based on the weight coefficients of the edges between the nodes in the undirected graph model.

[0118] Moreover, the present invention also provides a protein structure prediction device based on an optical quantum computer, which includes: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the protein structure prediction method based on an optical quantum computer as described above.

[0119] Finally, the present invention also provides a computer storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps of the protein structure prediction method based on an optical quantum computer as described above are implemented.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Those skilled in the art can modify or replace the technical solution of the present invention according to the idea of ​​the present invention without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A protein structure prediction method based on optical quantum computer, characterized in that: The following steps are involved: 1) Obtaining a multiple sequence alignment MSA matrix of the target sequence based on the target sequence alignment of the protein to be predicted; 2) Encoding the obtained multiple sequence alignment MSA matrix into a {0,1} matrix; 3) converting the amino acid interactions of the protein into an undirected graph model of {0,1} state nodes and training it using a Boltzmann machine training mechanism using an optical quantum computer to obtain the weight coefficients of the edges between the nodes in the undirected graph model; 4) Obtaining the interaction coefficients between different amino acids in the protein based on the weight coefficients of the edges between the nodes in the undirected graph model.

2. The protein structure prediction method based on optical quantum computer according to claim 1, characterized in that: The step 1) specifically includes: 11) Download the target sequence of the protein to be predicted from the protein sequence database; 12) Using the downloaded target sequence as input, multiple sequence alignment tool mmseqs2 and protein database Swiss-Protdatabase were used to obtain the multiple sequence alignment MSA matrix.

3. The protein structure prediction method based on optical quantum computer according to claim 1, characterized in that: The step 2) is specifically as follows: one-hot encoding the multiple sequence alignment MSA matrix encoded by the string, there are 21 choices for the amino acid at each position, for matrix alignment, the space character is represented by "-", and an L*21-dimensional {0,1} matrix is ​​used to represent the information of a row in the multiple sequence alignment MSA matrix, and the multiple sequence alignment MSA matrix is ​​converted to obtain M L*21-dimensional {0,1} matrices, wherein L is the sequence length of the amino acid and M is the number of protein strips obtained by alignment.

4. The protein structure prediction method based on optical quantum computer according to claim 1, characterized in that: In the step 3), when converting the amino acid interactions of the protein into an undirected graph model of {0,1} state nodes, first, the amino acids of the protein sequence of the protein to be predicted are taken as vertices, and the interaction relationships between the amino acids are taken as edges to establish a fully connected undirected graph; then, since each amino acid is a 21-dimensional one-hot vector, each amino acid is converted into 21 {0,1} state nodes, and the connection between any two {0,1} state nodes is taken as an edge to establish the undirected graph model of the {0,1} state nodes, wherein the state of each state node in the undirected graph model of the {0,1} state nodes is 0 or 1, and the weight is h. i , the weight coefficient of the edge between any two state nodes is J ij .

5. The protein structure prediction method based on optical quantum computer according to claim 4, characterized in that: In the step 3), when the Boltzmann machine training mechanism is used to train using an optical quantum computer to obtain the weight coefficients of the edges between the nodes in the undirected graph model, the training loss function C is: Where D kl represents the KL divergence, N(θ) represents the negative logarithmic conditional likelihood, and N DS represents the number of {0, 1}} state nodes in the undirected graph model, α is a coefficient and α∈[0, 1].

6. The protein structure prediction method based on optical quantum computer according to claim 5, characterized in that: The step 4) specifically includes: 41) Based on the weight coefficient J of the edge between nodes in the undirected graph model ij , establish the weight coefficient matrix J; 42) extracting a submatrix J′=[i*21:i*21+21, j*21:j*21+21] from the weight coefficient matrix J, wherein the submatrix represents the coupling information between the 21*21 {0,1} state nodes corresponding to the ith amino acid and the 21*21 {0,1} state nodes corresponding to the jth amino acid; 43) The submatrix is ​​mean-centered as follows to obtain a mean-centered matrix J″: J″=J′-μ0-μ1+μ Where μ0 is the column mean, μ1 is the row mean, and μ is the overall mean of the submatrix; 44) Calculate the interaction coefficient S between the i-th amino acid and the j-th amino acid by using the obtained mean-centered matrix J″ ij : S ij =||J″||2。 7. A protein structure prediction system based on optical quantum computers, characterized in that: include: An alignment module, which is used to obtain a multiple sequence alignment MSA matrix of the target sequence based on the target sequence of the protein to be predicted; An encoding module, which is used to encode the obtained multiple sequence alignment MSA matrix into a {0,1} matrix; A weight coefficient acquisition module, which is used to convert the amino acid interactions of the protein into an undirected graph model of {0,1} state nodes and use a Boltzmann machine training mechanism to train using an optical quantum computer to obtain the weight coefficients of the edges between the nodes in the undirected graph model; The interaction coefficient acquisition module is used to obtain the interaction coefficients between different amino acids in the protein based on the weight coefficients of the edges between the nodes in the undirected graph model.

8. A protein structure prediction device based on an optical quantum computer, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the protein structure prediction method based on an optical quantum computer as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the protein structure prediction method based on an optical quantum computer as described in any one of claims 1 to 6 are implemented.

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