Coherent Isin machine-based protein structure alignment method and device

Abstract

The invention provides a protein structure alignment method and device, and the method comprises the steps: firstly, obtaining two protein files which need to be aligned; then, for each protein file, determining a corresponding contact pair set; generating a two-dimensional grid chart according to the two contact pair sets, and establishing connecting edges between grid vertexes capable of forming effective alignment in the two-dimensional grid chart; searching a solution space represented by the two-dimensional grid diagram by using a coherent Isin machine system, and solving a ground state of the Hamiltonian H; and finally, generating a protein structure alignment graph according to the ground state of the Hamiltonian H. According to the protein structure alignment method provided by the invention, all possible alignment schemes can be systematically searched, and the found solution is ensured to be globally optimal. In addition, the method is based on a coherent Isin machine system, the calculation process has inherent parallelism, the solution space can be explored more quickly, and the method is particularly suitable for processing the large-scale parallel pairing problem in the protein alignment problem.

Description

Technical Field

[0001] The present invention relates to the field of computational biology, and particularly to a method and apparatus for protein structure alignment based on a coherent Ising machine. Background Art

[0002] In bioinformatics, protein structure alignment is a core task. The protein structure alignment problem can be formulated as: finding the optimal correspondence in the three-dimensional structures of two proteins so that their spatial structures match as closely as possible. Its purpose is to identify and compare the spatial structure similarities between different proteins, which is crucial for understanding protein functions, guiding drug design, and revealing biological evolutionary relationships. The protein structure alignment problem is a typical NP-hard problem, which means that as the protein size increases, the computational resources and time required to find the best or near-best structure alignment solution increase sharply.

[0003] Regarding the protein structure alignment problem, existing solutions include: 1. Distance matrix-based methods: Protein structure alignment is achieved by calculating and comparing the similarities of distance matrices between protein pairs. Although this method can theoretically provide accurate alignments, it has high computational costs when dealing with large protein datasets and is difficult to scale.

[0004] 2. Dynamic programming-based methods: Dynamic programming algorithms, such as the Smith-Waterman and Needleman-Wunsch algorithms, can be used to find local or global alignments between protein structures, but their time complexity and space complexity increase rapidly with the increase in protein sequence length, limiting their application in large-scale problems.

[0005] 3. Machine learning-based methods: With the development of machine learning technologies, neural networks and other machine learning models are used to predict the alignments between protein structures. Although machine learning shows potential in predicting protein structure alignments, they usually require a large amount of labeled data for training and may lack generalization ability when dealing with the complexity and diversity of protein structures.

[0006] 4. Branch and bound and branch and prune algorithms: Branch and bound and branch and prune algorithms are a method for solving integer programming problems and are also applied to the protein structure alignment problem. They find the optimal solution by systematically exploring the solution space and pruning infeasible branches. Although branch and bound and branch and prune algorithms can theoretically find the optimal solution, they may become very time-consuming in practice due to the huge solution space, especially in the NP-hard problem of protein structure alignment.

[0007] These methods have improved the efficiency and accuracy of protein structure alignment to varying degrees, but still face challenges posed by the NP-hard problem, especially when dealing with large protein datasets. These traditional methods have limitations in terms of computational resource consumption, alignment quality, and processing speed, especially in complex protein structures that require high-precision alignment. Therefore, a more efficient and accurate method that can be applied to large protein datasets is needed to solve the protein structure alignment problem. Summary of the Invention

[0008] To solve the above problems, the present invention provides a protein structure alignment method and device based on a coherent Ising machine.

[0009] According to a first aspect, there is provided a protein structure alignment method based on a coherent Ising machine, comprising the following steps: S1. Obtain two protein files to be aligned, each protein file including the spatial coordinates of multiple atoms in the protein, and the multiple atoms form multiple amino acid residues, and the amino acid residues are arranged in the order of the original sequence.

[0010] S2. For each protein file, determine a corresponding plurality of contact pairs, where each contact pair includes two amino acid residues.

[0011] S3. Generate a two-dimensional grid graph according to the corresponding plurality of contact pairs of the two protein files, where the rows represent the contact pairs corresponding to one protein file, and the columns represent the contact pairs corresponding to the other protein file.

[0012] Establish connection edges between the grid vertices that can form effective alignments in the two-dimensional grid graph, and the effective alignment needs to satisfy: after the alignment is completed, the amino acid residues are arranged in the order of the original sequence.

[0013] S4. Use a coherent Ising machine system to search the solution space represented by the two-dimensional grid graph and solve the ground state of the Hamiltonian H, where the Hamiltonian H is: Where represents whether the grid vertex in the th row and the th column is selected, is the weight of , represents whether there is a connection edge between the grid vertex in the th row and the th column and the grid vertex in the th row and the th column, and K1 is the coefficient of .

[0014] S5. Generate a protein structure alignment graph based on the ground state of the Hamiltonian H.

[0015] In some embodiments, the distance between the two amino acid residues is less than a specific threshold.

[0016] In some embodiments, the use of the coherent Ising machine system to search the solution space represented by the two-dimensional grid graph and solve the ground state of the Hamiltonian H specifically includes: Initialize the coherent Ising machine according to the Hamiltonian H and start the evolution process of the coherent Ising machine system; During the evolution process of the coherent Ising machine system, measure the phase and intensity of the optical field in the coherent Ising machine system multiple times, and adjust the parameters of the coherent Ising machine system in real time according to the measurement results. Finally, obtain a collective oscillation mode, and the quantum spin state in this mode corresponds to the ground state of the Hamiltonian H.

[0017] In some embodiments, the coherent Ising machine system includes: An optical parametric oscillator network for simulating the spin interaction in the Ising model.

[0018] A phase-sensitive amplifier for amplifying signals with a specific phase.

[0019] A field programmable gate array for controlling the optical parametric oscillator network.

[0020] A phase / intensity measurer for measuring the phase and intensity of the optical field in the cavity.

[0021] An optical modulator for changing the phase difference between light beams, thereby simulating the interaction strength between different spin states.

[0022] A beam splitter for generating an interference effect and simulating the connection relationship between spins in the Ising model.

[0023] An optical fiber used as a transmission medium for optical signals.

[0024] According to a second aspect, there is provided a protein structure alignment device, characterized by including: An acquisition module for acquiring two protein files to be aligned. Each protein file includes the spatial coordinates of multiple atoms in the protein, and the multiple atoms form multiple amino acid residues, and the amino acid residues are arranged in the order of the original sequence.

[0025] A contact pair generation module for determining respective multiple contact pairs for each protein file, where each contact pair includes two amino acid residues.

[0026] A graph generation module, configured to generate a two-dimensional grid graph according to two sets of contact pairs of two protein files, wherein rows represent contact pairs corresponding to one protein file, and columns represent contact pairs corresponding to the other protein file; connection edges are established between grid vertices in the two-dimensional grid graph that can form a valid alignment, and the valid alignment needs to satisfy: after the alignment is completed, the amino acid residues are arranged in the order of the original sequence.

[0027] A solving module, configured to use a coherent Ising machine system to search the solution space represented by the two-dimensional grid graph and solve the ground state of the Hamiltonian H, where the Hamiltonian H is: wherein, represents whether the grid vertex in the th row and the th column is selected, is the weight of , represents whether there is a connection edge between the grid vertex in the th row and the th column and the grid vertex in the th row and the th column, and K1 is the coefficient of .

[0028] An output module, configured to generate a protein structure alignment graph according to the ground state of the Hamiltonian H.

[0029] In some embodiments, the contact pairs include two amino acid residues that are non-adjacent and the distance therebetween is less than a first threshold.

[0030] In some embodiments, the using the coherent Ising machine system to search the solution space represented by the two-dimensional grid graph and solve the ground state of the Hamiltonian H specifically includes: Initializing a coherent Ising machine according to the Hamiltonian H and starting the evolution process of the coherent Ising machine system.

[0031] During the evolution process of the coherent Ising machine system, measuring the phase and intensity of the optical field in the coherent Ising machine system multiple times, and adjusting the parameters of the coherent Ising machine system in real time according to the measurement results, and finally obtaining a collective oscillation mode, and the quantum spin state in this mode corresponds to the ground state of the Hamiltonian H.

[0032] In some embodiments, the coherent Ising machine system includes: An optical parametric oscillator network, configured to simulate the spin interaction in the Ising model.

[0033] A phase-sensitive amplifier, configured to amplify signals with a specific phase.

[0034] A field-programmable gate array for controlling an optical parametric oscillator network.

[0035] A phase / intensity measurer for measuring the phase and intensity of the optical field in the cavity.

[0036] An optical modulator for changing the phase difference between light beams to simulate the interaction strength between different spin states.

[0037] A beam splitter for generating an interference effect to simulate the connection relationship between spins in the Ising model.

[0038] An optical fiber used as a transmission medium for optical signals.

[0039] The protein structure alignment method provided by the present invention can systematically search all possible alignment schemes to ensure that the solution found is globally optimal. In addition, based on the coherent Ising machine system, the calculation process of the present invention has inherent parallelism and can process the interactions of multiple spin states at one time, enabling it to explore the solution space more quickly, improve the solution speed, and is particularly suitable for dealing with large-scale parallel pairing problems in protein alignment problems. Therefore, the method provided by the present invention provides a faster and more accurate solution for the protein structure alignment problem. Description of the Drawings

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 Shows the flow of a protein structure alignment method provided by the present invention; Figure 2 Shows the structure of a coherent Ising machine system provided by an embodiment of the present invention; Figure 3 Shows the structure of a protein structure alignment device provided by the present invention. Detailed Embodiments

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will describe the technical solutions in the embodiments of the present application in conjunction with the drawings.

[0043] To quickly and accurately provide results when facing the structure alignment problem of large-scale or structurally complex proteins, the present invention provides a protein structure alignment method based on a coherent Ising machine (CIM).

[0044] A coherent Ising machine is a computing platform based on quantum optics to simulate the Ising model, which has the advantages of fast computing speed and strong scalability. It is suitable for processing various large-scale combinatorial optimization problems, especially outstanding in dealing with large-scale qubits and NP-hard problems. As a computing model, the unique parallelism of the coherent Ising machine provides potential advantages for solving NP-hard problems. The CIM can explore multiple possible solutions simultaneously during the coherent evolution of quantum states, thus showing the potential to outperform traditional algorithms in protein structure alignment problems. This method not only is expected to significantly improve the computational efficiency of alignment but also can process larger-scale protein datasets while maintaining high accuracy. Therefore, the technical solution of the present invention aims to utilize the computational advantages of the CIM to provide an innovative and efficient solution for the protein structure alignment problem, with the expectation of achieving breakthrough progress in the field of bioinformatics.

[0045] Based on this, the present invention first transforms the protein structure alignment problem into an Ising model optimization problem by designing the Hamiltonian of the coherent Ising machine, enabling efficient calculation; then uses the coherent Ising machine to solve the protein structure alignment problem to ensure that the solution found is globally optimal.

[0046] The following combines with the drawings to introduce the protein structure alignment method provided by the present invention. The flowchart of this method is as Figure 1 shown and includes the following steps: S1. Obtain two protein files to be aligned. Each protein file includes the spatial coordinates of multiple atoms in the protein, and the multiple atoms form multiple amino acid residues, which are arranged in the order of the original sequence.

[0047] It can be understood that a protein is a molecular chain formed by connecting multiple amino acid residues in a certain order through peptide bonds, and this order is the order of the original sequence of the protein, which is determined by genetic information. Each amino acid residue is formed by atomic arrangement and bonding, and atoms are the most basic units that make up proteins.

[0048] S2. For each protein file, determine the corresponding multiple contact pairs, where each contact pair includes two amino acid residues.

[0049] It can be understood that in the three-dimensional structure of a protein, if the distance between any atoms of two amino acid residues is less than a certain specific threshold (usually 5 to 10 angstroms), then these two residues are considered to form a contact pair. Contact pairs can be used to evaluate the spatial proximity between different amino acid residues in a protein molecule. Based on this, the similarity between the three-dimensional structures of two proteins can be determined, thereby solving the protein structure alignment problem.

[0050] In one embodiment of this step, for each protein file, all the amino acid residues contained therein can be traversed and paired. Thus, for any pair of amino acid residues, when it is determined that the spatial distance between the two amino acid residues is less than a specific threshold, this pair of amino acid residues is classified as a contact pair.

[0051] In another embodiment of this step, a corresponding structure diagram can be constructed according to each protein file, where each vertex represents an amino acid residue, and each edge connects the vertices corresponding to two amino acid residues whose distance is less than a specific threshold, that is, each edge represents a contact pair.

[0052] In some embodiments, the two amino acid residues are non - adjacent and the distance is less than a specific threshold.

[0053] Where adjacent means that two amino acid residues are connected by a peptide bond, hydrogen bond, etc. In some cases, researchers may only be interested in the contacts between non - adjacent amino acid residues, and only consider the pairs of non - adjacent amino acid residues whose distance is less than a specific threshold as contact pairs.

[0054] S3. Generate a two - dimensional grid graph based on the multiple contact pairs corresponding to the two protein files respectively, where the rows represent the contact pairs corresponding to one protein file, and the columns represent the contact pairs corresponding to the other protein file.

[0055] This step constructs a two - dimensional grid graph G. In graph G, the rows and columns of the grid represent the contact pairs in the two proteins respectively, and the grid vertices formed by the intersection of each row and each column represent a potential alignment of the contact pairs in the two proteins.

[0056] For the sake of distinction in description, the contact pairs corresponding to one protein file are called the first contact pairs, and the contact pairs corresponding to the other protein file are called the second contact pairs. Thus, the grid vertex located in the i - th row and j - th column of the two - dimensional grid graph corresponds to the potential alignment of the i - th first contact pair and the j - th second contact pair. It should be understood that the "first" in "first contact pair" and the "second" in "second contact pair" are only for distinguishing similar things and do not have other limiting effects such as sorting.

[0057] For example:

[0058] The i - th row in graph G corresponds to the contact pair (a1, a2) in protein A; the j - th column corresponds to the contact pair (b1, b2) in protein B, where a1, a2, b1, and b2 represent amino acid residues respectively. Then, the grid vertex formed by the intersection of the i - th row and j - th column in graph G represents the alignment of the two contact pairs (a1, a2) and (b1, b2).

[0059] The above examples explain the meaning of the grid vertices in graph G. Next, combined with examples, the establishment and meaning of the connecting edges in graph G will be further introduced. Connecting edges are established between the grid vertices that can form valid alignments in the two-dimensional grid graph, and the valid alignment needs to satisfy that after the alignment is completed, the amino acid residues are arranged in the order of the original sequence.

[0060] As mentioned above, each grid vertex in graph G represents a potential alignment of two protein contact pairs. By adding connecting edges between these grid vertices, it is possible to filter out the valid alignments that can still satisfy the amino acid alignment feasibility rules after two potential alignments take effect simultaneously. The condition of the valid alignment corresponds to the sequentiality in the amino acid alignment feasibility rules. On the premise that all amino acid alignments comply with sequentiality, the alignment naturally complies with exclusivity, that is, one amino acid will not align with multiple amino acids in another protein. For example:

[0061] The i-th row and the k-th row in graph G correspond to the contact pairs (a1, a2) and (a3, a4) in protein A respectively; the j-th column and the l-th column correspond to the contact pairs (b1, b2) and (b3, b4) in protein B respectively, where a1, a2, a3, a4, b1, b2, b3, b4 represent amino acid residues respectively. Then, the grid vertex 1 formed by the intersection of the i-th row and the j-th column in graph G represents the alignment of the two contact pairs (a1, a2) and (b1, b2), and the grid vertex 2 formed by the intersection of the k-th row and the l-th column in graph G represents the alignment of the two contact pairs (a3, a4) and (b3, b4).

[0062] If the amino acid residues are still arranged in the order of the original sequence after aligning grid vertex 1 with grid vertex 2, it indicates that these two grid vertices can form a valid alignment, and a connecting edge needs to be established between them. Otherwise, it indicates that these two grid vertices cannot form a valid alignment, and no connecting edge can be established between them.

[0063] The above examples explain the establishment and meaning of the connecting edges in graph G. As can be seen from the above, if there are connecting edges between any two grid vertices in a set of grid vertices in graph G, it means that the alignments represented by any pair of vertices in this set are feasible, and then the alignment represented by the entire vertex set is also feasible, and this set corresponds to the solution of the protein structure alignment problem. In this way, the protein structure alignment problem is transformed into the maximum clique problem of graph G.

[0064] Next, by constructing the Hamiltonian H of graph G and using CIM to solve for the ground state of the Hamiltonian H to solve this maximum clique problem, that is:

[0065] S4. Using the coherent Ising machine system, search the solution space represented by the two-dimensional grid graph to solve the ground state of the Hamiltonian H, where the Hamiltonian H is:

[0066] (1)

[0067] The meanings and possible values of each parameter are as follows:

[0068] represents whether the grid vertex at the th row and the th column is selected. Whether the grid vertex is selected corresponds to the node spin state in the coherent Ising machine. For example, it can be set that when the spin is +1, it means this grid vertex is selected.

[0069] is the weight of and can take the value of 1.

[0070] represents whether there is a connecting edge between the grid vertex at the th row and the th column and the grid vertex at the th row and the th column. For example, it takes 1 when there is a connecting edge and 0 when there is no connecting edge.

[0071] K1 is the coefficient of and can be set to any positive number, such as 1.

[0072] In some embodiments, using the coherent Ising machine system to search the solution space represented by the two-dimensional grid graph and solve the ground state of the Hamiltonian H specifically includes:

[0073] Initializing the coherent Ising machine according to the Hamiltonian H and starting the evolution process of the coherent Ising machine system;

[0074] During the evolution process of the coherent Ising machine system, measuring the phase and intensity of the optical field in the coherent Ising machine system multiple times, and adjusting the parameters of the coherent Ising machine system in real time according to the measurement results, and finally obtaining the collective oscillation mode. The quantum spin state in this mode corresponds to the ground state of the Hamiltonian H.

[0075] From the above, the ground state of the Hamiltonian H can be obtained using the coherent Ising machine system.

[0076] S5. Generating a protein structure alignment graph according to the ground state of the Hamiltonian H.

[0077] The ground state of the Hamiltonian H corresponds to the maximum clique of the graph G. The alignment graph obtained by inverse encoding the grid vertices in the maximum clique into amino acid residues is the final result of the alignment of the two protein structures.

[0078] The above is the protein structure alignment method based on the coherent Ising machine provided by the present invention. Next, the coherent Ising machine system used in this method will be briefly introduced.

[0079] This coherent Ising machine system uses a doubly resonant optical parametric oscillator (DOPO) to implement artificial spins, and enhances optical signals with specific phases by placing a phase sensitive amplifier (PSA) in the optical cavity. The PSA is an optical amplifier based on optical parametric amplification, which can effectively amplify the phase components of 0 and π relative to the pump phase. Therefore, the DOPO only adopts phases of 0 or π higher than the oscillation threshold; thus, discrete phase states can be used to represent Ising spin states. The interaction between DOPO pulses is realized using measurement feedback technology. By repeatedly measuring and feedback modulating the process in the cavity multiple times while increasing the pump amplitude from 0, the "strongest" collective oscillation mode far above the threshold will ultimately be obtained, which corresponds to the optimal solution to a given Ising problem.

[0080] The schematic diagram of this coherent Ising machine system is as Figure 2 shown and includes:

[0081] (1) An optical parametric oscillator network for simulating spin interactions in the Ising model.

[0082] (2) A phase sensitive amplifier for amplifying signals with specific phases.

[0083] (3) A field-programmable gate array (FPGA) for controlling the optical parametric oscillator network.

[0084] The FPGA can be programmed to configure its internal circuit, and can be used to adjust the parameters of the coherent Ising machine system in real time according to the measurement results during the evolution of the coherent Ising machine system.

[0085] (4) A phase / intensity measurer for measuring the phase and intensity of the optical field in the cavity.

[0086] (5) An optical modulator for changing the phase difference between light beams, thereby simulating the interaction intensity between different spin states.

[0087] (6) A beam splitter for generating an interference effect and simulating the connection relationship between spins in the Ising model.

[0088] (7) Optical fibers used as the transmission medium for optical signals.

[0089] As can be seen from the above, compared with the prior art, the protein structure alignment method provided by the present invention has the following beneficial effects:

[0090] 1. Guarantee of global optimal solution: The technology of this method can systematically search all possible alignment schemes to ensure that the solution found is globally optimal. This is particularly important in the NP-hard protein alignment problem because although many heuristic algorithms can quickly find approximate solutions, they cannot guarantee the optimality of the solutions.

[0091] 2. Acceleration potential: Due to the utilization of coherent effects, the coherent Ising machine can explore the solution space more quickly, improving the solution speed, and is particularly suitable for the clique problem of large graphs. Traditional protein alignment algorithms, such as dynamic programming, heuristic algorithms, etc., usually take a long time to solve large-scale graphs. In addition, the calculation process of the coherent Ising machine has inherent parallelism and can handle the interactions of multiple spin states at one time. Therefore, it is suitable for handling large-scale parallel pairing in protein alignment problems, further improving the calculation efficiency.

[0092] In summary, compared with traditional protein alignment solution methods, the protein structure alignment method based on the coherent Ising machine of the present invention solves the problem that heuristic algorithms may consume a long time but cannot obtain a standard solution, and provides a faster and more accurate solution for protein structure alignment problems, especially for the structure alignment of large proteins.

[0093] The present invention also provides a protein structure alignment device 300, the schematic diagram of which is as Figure 3 shown, including:

[0094] An acquisition module 301, configured to acquire two protein files to be aligned. Each protein file includes the spatial coordinates of multiple atoms in the protein, and the multiple atoms form multiple amino acid residues, and the amino acid residues are arranged in the order of the original sequence;

[0095] A contact pair generation module 302, configured to determine a corresponding contact pair set for each protein file, where each contact pair includes two amino acid residues;

[0096] A graph generation module 303, configured to generate a two-dimensional grid graph according to the two contact pair sets of the two protein files. Among them, the rows represent the contact pairs in the contact pair set corresponding to one protein file, and the columns represent the contact pairs in the contact pair set corresponding to the other protein file; the connecting edges are established between the grid vertices that can form an effective alignment in the two-dimensional grid graph, and the effective alignment needs to meet: after the alignment is completed, the amino acid residues are arranged in the order of the original sequence;

[0097] A solution module 304, configured to use the coherent Ising machine system to search the solution space represented by the two-dimensional grid graph and solve the ground state of the Hamiltonian H, where the Hamiltonian H is:

[0098]

[0099] Among them, represents whether the grid vertex at the th row and the th column is selected. is the weight of . represents whether there is a connecting edge between the grid vertex at the th row and the th column and the grid vertex at the th row and the th column. K1 is the coefficient of .

[0100] The output module 305 is configured to generate a protein structure alignment graph according to the ground state of the Hamiltonian H.

[0101] It should be noted that the above device can execute the aforementioned protein structure alignment method. For the functions of each module, reference can also be made to the foregoing introduction to the method, which will not be elaborated here.

[0102] In the description of the embodiments of the present application, words such as "exemplary", "for example" or "for instance" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "exemplary", "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0103] In the description of the embodiments of the present application, the term "and / or" merely describes the association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, B exists alone, and both A and B exist simultaneously. In addition, unless otherwise specified, the meaning of the term "plurality" refers to two or more.

[0104] In addition, the terms "comprise", "include", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0105] The specific embodiments described above further elaborate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above is only the specific embodiment of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the present invention shall be included in the protection scope of the present invention.

Claims

1. A protein structure alignment method, characterized in that, Including the following steps: Obtain two protein files to be aligned. Each protein file includes the spatial coordinates of multiple atoms in the protein, and the multiple atoms form multiple amino acid residues, which are arranged in the order of the original sequence; For each protein file, determine the corresponding multiple contact pairs, where each contact pair includes two amino acid residues; Generate a two-dimensional grid graph according to the multiple contact pairs corresponding to the two protein files respectively. Among them, the rows represent the contact pairs corresponding to one protein file, and the columns represent the contact pairs corresponding to the other protein file; Establish connection edges between the grid vertices that can form an effective alignment in the two-dimensional grid graph. The effective alignment needs to satisfy that after the alignment is completed, the amino acid residues are arranged in the order of the original sequence; Use a coherent Ising machine system to search the solution space represented by the two-dimensional grid graph and solve the ground state of the Hamiltonian H, where the Hamiltonian H is: Among them, represents whether the grid vertex at the -th row and -th column is selected. is the weight of . represents whether there is a connecting edge between the grid vertex at the -th row and -th column and the grid vertex at the -th row and -th column. K1 is the coefficient of . Generate a protein structure alignment graph according to the ground state of the Hamiltonian H.

2. The method according to claim 1, characterized in that, The two amino acid residues are non-adjacent and the distance is less than a specific threshold.

3. The method according to claim 1, wherein The using of the coherent Ising machine system to search the solution space represented by the two-dimensional grid graph and solve the ground state of the Hamiltonian H specifically includes: Initialize the coherent Ising machine according to the Hamiltonian H and start the evolution process of the coherent Ising machine system; During the evolution process of the coherent Ising machine system, measure the phase and intensity of the optical field in the coherent Ising machine system multiple times, and adjust the parameters of the coherent Ising machine system in real time according to the measurement results. Finally, obtain a collective oscillation mode, and the quantum spin state in this mode corresponds to the ground state of the Hamiltonian H.

4. The method according to claim 1, characterized in that The coherent Ising machine system includes: An optical parametric oscillator network for simulating the spin interaction in the Ising model; A phase-sensitive amplifier for amplifying signals with specific phases; A field programmable gate array for controlling the optical parametric oscillator network; A phase / intensity measurer for measuring the phase and intensity of the intracavity optical field; An optical modulator for changing the phase difference between light beams to simulate the interaction strength between different spin states; A beam splitter for generating an interference effect and simulating the connection relationship between spins in the Ising model; Optical fibers used as the transmission medium for optical signals.

5. A protein structure alignment device, characterized in that, Including: An acquisition module for obtaining two protein files to be aligned. Each protein file includes the spatial coordinates of multiple atoms in the protein, and the multiple atoms form multiple amino acid residues, which are arranged in the order of the original sequence; A contact pair generation module for determining the corresponding multiple contact pairs for each protein file, where each contact pair includes two amino acid residues; A graph generation module for generating a two-dimensional grid graph according to the multiple contact pairs corresponding to the two protein files respectively. Among them, the rows represent the contact pairs corresponding to one protein file, and the columns represent the contact pairs corresponding to the other protein file; connection edges are established between the grid vertices that can form an effective alignment in the two-dimensional grid graph. The effective alignment needs to satisfy that after the alignment is completed, the amino acid residues are arranged in the order of the original sequence; A solution module, which is used to search the solution space represented by the two-dimensional grid graph by using a coherent Ising machine system, and solve the ground state of the Hamiltonian H, where the Hamiltonian H is: Among them, represents whether the grid vertex at the -th row and the -th column is selected. is the weight of ; represents whether there is a connecting edge between the grid vertex at the -th row and the -th column and the grid vertex at the -th row and the -th column. K1 is the coefficient of ; An output module for generating a protein structure alignment graph according to the ground state of the Hamiltonian H.

6. The device according to claim 5, characterized in that The two amino acid residues are non-adjacent and the distance is less than a specific threshold.

7. The device according to claim 5, characterized in that, The method of using the coherent Ising machine system to search the solution space represented by the two-dimensional grid graph and solve the ground state of the Hamiltonian H specifically includes: Initializing the coherent Ising machine according to the Hamiltonian H and starting the evolution process of the coherent Ising machine system; During the evolution process of the coherent Ising machine system, measuring the phases and intensities of the optical fields in the coherent Ising machine system multiple times, and adjusting the parameters of the coherent Ising machine system in real time according to the measurement results, and finally obtaining a collective oscillation mode, and the quantum spin state in this mode corresponds to the ground state of the Hamiltonian H.

8. The device according to claim 5, characterized in that, The coherent Ising machine system includes: An optical parametric oscillator network, which is used to simulate the spin interaction in the Ising model; A phase-sensitive amplifier, which is used to amplify signals with specific phases; A field programmable gate array, which is used to control the optical parametric oscillator network; A phase / intensity measurer, which is used to measure the phases and intensities of the optical fields in the cavity; An optical modulator, which is used to change the phase difference between the light beams, so as to simulate the interaction intensity between different spin states; A beam splitter, which is used to generate an interference effect and simulate the connection relationship between the spins in the Ising model; Optical fibers, which are used as the transmission medium for optical signals.

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