Protein conformation energy minimization methods, systems, devices, and media

Abstract

This application discloses a method, system, device, and medium for minimizing protein conformational energy. The method includes: determining the loop region and main chain region of the protein; obtaining the potential energy function of the loop region based on hydrogen bond energy and main chain dihedral angle deviation, and obtaining the potential energy function of the main chain region based on the main chain dihedral angle; obtaining a dual potential energy function based on the loop region potential energy function and the main chain region potential energy function; and optimizing the protein conformation by dynamically adjusting the constraint force constant until the minimum value of the dual potential energy function is found. Using the embodiments of this application, the accuracy of protein structure prediction can be effectively improved, especially the accuracy of loop conformation. By employing a dual resonant potential energy constraint system, which considers the conformational characteristics of both the loop and non-loop regions, greater conformational flexibility can be given to the loop region while maintaining overall structural stability, providing an important structural basis for drug screening and structure-function studies.

Description

Technical Field

[0001] This application relates to the field of biological technology, and in particular to a method, system, apparatus and medium for minimizing protein conformation energy. Background Technology

[0002] GPCRs (G protein-coupled receptors) are one of the largest superfamily of membrane proteins in living organisms, playing a central role in physiological processes such as cell signal transduction, hormone regulation, and neurotransmitter transmission. Because GPCRs are closely related to the occurrence and development of numerous diseases, they have become important targets in modern drug development, with approximately 40% of marketed drugs targeting GPCRs.

[0003] However, accurate prediction and optimization of GPCR structures still face significant challenges. This is mainly due to the unique structural features of GPCRs: seven transmembrane α-helices (TM1-TM7), three extracellular loops (ECL1-3), three intracellular loops (ICL1-3), and N-terminal and C-terminal domains. This complex structural composition makes GPCRs highly conformationally flexible in ligand binding and signal transduction. Currently, the main technical approaches for GPCR structure optimization and their limitations are as follows: Traditional energy minimization methods based on molecular dynamics: These methods use general force fields (such as AMBER and CHARMM) for structure optimization, but cannot effectively handle the high flexibility of loop regions. Artificial intelligence-based structure prediction methods: Deep learning models such as AlphaFold2 are relatively accurate in predicting transmembrane regions of GPCRs, but their accuracy drops significantly in predicting loop regions, often requiring further structural optimization. Hybrid modeling methods: These strategies combine homology modeling and de novo prediction, relying on known template structures. Their predictive effectiveness for novel GPCR families is limited, and the optimization process lacks consideration for GPCR-specific structural features. 1. Common technical problems with the methods in related technologies are: accuracy issues: insufficient accuracy in predicting the conformation of the loop region, inadequate consideration of the influence of the membrane environment, and insufficient specificity of the force field parameters; specificity issues: lack of specific optimization strategies for specific domains of GPCRs, insufficient consideration of the interactions between different domains, and difficulty in balancing the universality and specificity of the optimization methods. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, system, device, and medium for minimizing protein conformational energy to address the aforementioned technical problems. This method can effectively improve the accuracy of protein structure prediction, especially the accuracy of loop conformation. Furthermore, by employing a dual resonant potential energy constraint system, which considers the conformational characteristics of both loop and non-loop regions, the method can maintain overall structural stability while granting greater conformational flexibility to the loop region. This improves the biological rationality of the predicted structure and provides an important structural basis for drug screening and structure-function research.

[0005] Firstly, a method for minimizing protein conformational energy is provided, including:

[0006] Identify the loop region and main chain region of the protein;

[0007] The potential energy function of the Loop region is obtained based on the hydrogen bond energy and the dihedral angle deviation of the main bond, and the potential energy function of the main chain region is obtained based on the dihedral angle of the main chain.

[0008] Based on the potential energy function of the Loop region and the potential energy function of the main chain region, a dual potential energy function is obtained;

[0009] The conformation of the protein is optimized by dynamically adjusting the constraint constant until the minimum value of the dual potential function is found.

[0010] In some examples, determining the loop region and main chain region of a protein includes:

[0011] The hydrogen bond energy calculation function is obtained based on the atomic bias charge, the distance between atomic pairs, and the distance-dependent decay factor of the protein.

[0012] Based on the hydrogen bond energy calculation function, the secondary structure determination function is obtained;

[0013] Obtain the Loop region determination criteria, and derive the Loop region determination function based on the Loop region determination criteria;

[0014] The Loop region and main chain region of the protein are determined based on the Loop region determination function and the number of residues in the Loop region.

[0015] In some examples, the step of obtaining the Loop region potential function based on the hydrogen bond energy and the main bond dihedral angle deviation, and obtaining the main chain region potential function based on the main chain dihedral angle, includes:

[0016] The adjustment coefficient is obtained, and the potential energy function of the Loop region is obtained based on the adjustment coefficient, the hydrogen bond energy, and the deviation between the main chain dihedral angle and the ideal dihedral angle.

[0017] The potential energy adjustment coefficient is obtained, and the potential energy function of the main chain region is obtained based on the potential energy adjustment coefficient, the main chain dihedral angle, and the ideal dihedral angle.

[0018] In some examples, the process of obtaining a dual potential function based on the Loop region potential function and the main chain region potential function includes:

[0019] Obtain the weighting coefficients for the relative contributions of the Loop region and the main chain region;

[0020] The dual potential function is obtained based on the weighting coefficients, the potential energy function of the Loop region, and the potential energy function of the main chain region.

[0021] In some examples, optimizing the protein conformation based on dynamically adjusting the constraint force constant until the minimum value of the dual potential function is found includes:

[0022] The conformation of the protein is progressively optimized by dynamically adjusting the constraint constant until the minimum value of the dual potential function is found.

[0023] In some examples, the progressive optimization of the protein conformation based on dynamically adjusting the constraint force constant until the minimum value of the dual potential function is found includes:

[0024] Initial optimization phase: Based on the initial dynamic adjustment constraint constant, the conformation of the protein is optimized until the first iteration number is reached;

[0025] Mid-term optimization phase: Based on the mid-term dynamic adjustment constraint constant, the conformation of the protein is optimized until the second iteration number is reached;

[0026] Final optimization stage: Based on the final dynamic adjustment constraint constant, the conformation of the protein is optimized until the third iteration is reached;

[0027] Based on the optimization results of the initial optimization stage, the intermediate optimization stage, and the final optimization stage, the minimum value of the dual potential energy function is found.

[0028] In some examples, after optimizing the protein conformation based on dynamically adjusted constraint constants until the minimum of the dual potential function is found, the process further includes:

[0029] The optimized protein conformation is subjected to structural quality assessment to obtain the final protein conformation.

[0030] Secondly, a protein conformation energy minimization system is provided, comprising:

[0031] The determination module is used to identify the loop region and main chain region of a protein;

[0032] The dual potential energy function acquisition module is used to obtain the potential energy function of the Loop region based on the hydrogen bond energy and the dihedral angle deviation of the main bond, and to obtain the potential energy function of the main chain region based on the dihedral angle of the main chain, and to obtain the dual potential energy function based on the potential energy function of the Loop region and the potential energy function of the main chain region.

[0033] The optimization module is used to optimize the conformation of the protein by dynamically adjusting the constraint force constant until the minimum value of the dual potential function is found.

[0034] Thirdly, a computing device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the protein conformation energy minimization method according to the first aspect above.

[0035] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the protein conformation energy minimization method according to the first aspect above.

[0036] In the embodiments of this application, after determining the loop region and main chain region of the protein, the potential energy function of the loop region is obtained based on the hydrogen bond energy and the dihedral deviation of the main chain, and the potential energy function of the main chain region is obtained based on the dihedral deviation of the main chain. Then, based on the potential energy functions of the loop region and the main chain region, a dual potential energy function is obtained. Finally, by dynamically adjusting the constraint force constant, the conformation of the protein is optimized to find the minimum value of the dual potential energy function, thereby completing the optimization of the protein conformation. This can effectively improve the accuracy of protein structure prediction, especially the accuracy of loop conformation. In addition, by using a dual resonant potential energy constraint system, considering the conformational characteristics of the loop region and the non-loop region respectively, it can give the loop region greater conformational flexibility while maintaining the overall structural stability, improving the biological rationality of the predicted structure, and providing an important structural basis for drug screening and structure-function research. Attached Figure Description

[0037] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0038] Figure 1 A flowchart of a protein conformation energy minimization method provided in the embodiments of this application;

[0039] Figure 2 A flowchart of a protein conformation energy minimization method provided in another embodiment of this application;

[0040] Figure 3 A structural block diagram of the protein conformation energy minimization system provided in the embodiments of this application;

[0041] Figure 4 This is a structural block diagram of a computing device provided in an embodiment of this application. Detailed Implementation

[0042] The present application will now be described in further detail with reference to the embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the application. Furthermore, it should be noted that, for ease of description, only the parts relevant to the application are shown in the accompanying drawings.

[0043] It should be noted that, unless otherwise specified, the embodiments and features of the embodiments in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0044] The following describes in detail, with reference to the accompanying drawings, a method, system, apparatus, and medium for minimizing protein conformational energy according to embodiments of this application.

[0045] The implementation environment of the application embodiment can be determined by a personal computing device, such as a computer or mobile terminal, to identify the loop region and main chain region of the protein; the potential energy function of the loop region is obtained based on the hydrogen bond energy and the dihedral deviation of the main bond, and the potential energy function of the main chain region is obtained based on the dihedral deviation of the main chain; a dual potential energy function is obtained based on the potential energy function of the loop region and the potential energy function of the main chain region; the conformation of the protein is optimized by dynamically adjusting the constraint force constant until the minimum value of the dual potential energy function is found.

[0046] Alternatively, this can be implemented by a server. For example, a personal computing device sends a request to a server, which determines the loop region and main chain region of the protein; obtains the potential energy function of the loop region based on the hydrogen bond energy and the dihedral angle deviation of the main chain, and obtains the potential energy function of the main chain region based on the dihedral angle of the main chain; obtains a dual potential energy function based on the potential energy functions of the loop region and the main chain region; optimizes the conformation of the protein by dynamically adjusting the constraint constant until the minimum value of the dual potential energy function is found; finally, the result is returned to the personal computing device.

[0047] The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0048] Figure 1 This is a flowchart of a protein conformation energy minimization method according to an embodiment of this application. Figure 1 As shown, a protein conformation energy minimization method according to an embodiment of this application includes the following steps:

[0049] S101: Identify the loop region and main chain region of the protein.

[0050] Taking G protein-coupled receptors (GPCRs) as an example, the loop and non-loop regions of the GPCR are determined, where the non-loop region includes the main chain region. For example: based on the atomic bias charge, the distance between atomic pairs, and the distance-dependent decay factor of the protein, a hydrogen bond energy calculation function is obtained; based on the hydrogen bond energy calculation function, a secondary structure determination function is obtained; a loop region determination criterion is obtained, and a loop region determination function is obtained based on the loop region determination criterion; based on the loop region determination function and the number of residues in the loop region, the loop region and the main chain region of the protein are determined.

[0051] In other words, an improved DSSP (Define Secondary Structure of Proteins) algorithm and a loop region determination method are used to identify the loop region and main chain region of a protein. The improved DSSP algorithm enables intelligent identification of GPCR structures. The core idea of ​​the DSSP algorithm is to determine the secondary structure type by analyzing the hydrogen bond network pattern between atoms in the protein main chain. Then, a loop region determination method is used to identify the loop region and main chain region of the protein.

[0052] Specifically, we first need to define a hydrogen bond energy calculation function, which describes hydrogen bond interactions in the form of Coulomb potential energy:

[0053]

[0054] Where q1q2 represents the partial charge of the atoms, reflecting the polarity of the hydrogen bond; rON, rCH, rOH, and rCN represent the distances between the corresponding atom pairs, for example, rON represents the distance between the oxygen atom and the nitrogen atom; f is a distance-dependent attenuation factor, representing the characteristic that the strength of the hydrogen bond weakens with increasing distance; E HB This represents the hydrogen bond energy.

[0055] E HB It can accurately describe the formation and breakage of hydrogen bond networks in the protein backbone. As an important stabilizing factor in protein secondary structure, the introduction of energy terms into hydrogen bonds allows the model to better simulate the interactions between the loop region and adjacent secondary structure units.

[0056] Based on hydrogen bond energy, a secondary structure determination function is defined as follows:

[0057] S i =DSSP(R i ,{R i-4,...,R i+4}, E HB ), i∈[1,n] (2)

[0058] Among them, S i R represents the secondary structure type of the i-th residue. i For the i-th residue. R i-4 ,...,R i+4 Residue R i The size of the window, which consists of eight residues (e.g., four in front and four behind), can be chosen based on the typical periodicity of α-helices or β-sheets.

[0059] Compared to the traditional DSSP algorithm, this application adds a quantitative description of hydrogen bond energy and introduces local sequence information, thereby improving prediction accuracy. Furthermore, a continuous energy function replaces the discrete distance judgment criterion.

[0060] The criteria for determining the Loop area are as follows:

[0061] 1. Hydrogen bond energy threshold: E HB >0.5kcal / mol

[0062] 2. Main chain dihedral distribution: does not conform to the characteristic distribution of α-helix or β-sheet.

[0063] 3. Sequence Conservatism: Evaluated using sequence entropy, defined as: H(S)

[0064]

[0065] Where, p i Let be the probability of the i-th amino acid residue, and n be the number of amino acid types.

[0066] Define the Loop section determination function:

[0067]

[0068] Neighborhood expansion algorithm: To ensure the continuity and completeness of Loop region identification, a neighborhood expansion algorithm is introduced.

[0069] For any identified loop region residue i, its extended set E i Defined as:

[0070] E i ={j||ji|≤N neighbor ,1≤j≤n}(5)

[0071] Where, N neighbor To extend the domain parameters, their values ​​are determined based on:

[0072] For short loops (≤8 residues), N neighbor =2;

[0073] For medium loops (residues 9-15), N neighbor =3;

[0074] For long loops (>15 residues), N neighbor =4.

[0075] Among them, E i Represents an extended set centered at residue i, containing sets whose distance from i is no more than N. neighbor All residues j.

[0076] S102: Obtain the potential energy function of the Loop region based on the hydrogen bond energy and the dihedral deviation of the main bond, and obtain the potential energy function of the main chain region based on the dihedral of the main chain.

[0077] In one embodiment of this application, the potential energy function of the Loop region is obtained based on the hydrogen bond energy and the deviation of the main bond dihedral angle, and the potential energy function of the main chain region is obtained based on the main chain dihedral angle. This includes: obtaining an adjustment coefficient, and obtaining the potential energy function of the Loop region based on the adjustment coefficient, the hydrogen bond energy, and the deviation between the main chain dihedral angle and the ideal dihedral angle; obtaining a potential energy adjustment coefficient, and obtaining the potential energy function of the main chain region based on the potential energy adjustment coefficient, the main chain dihedral angle, and the ideal dihedral angle.

[0078] As a concrete example, the potential function of the Loop region is designed as follows:

[0079] In the Loop region, a composite potential energy function considering hydrogen bond energy and dihedral angle deviation is introduced, namely: Loop region potential energy function:

[0080] E loop =α1·E HB +α2·Δφ (6)

[0081] Here, α1 and α2 are adjustment coefficients that control the weights of hydrogen bond energy and dihedral angle matching degree. By adjusting these two parameters, a precise balance between the rigidity and flexibility of the Loop region can be achieved. Δφ is the deviation between the main chain dihedral angle and the ideal dihedral angle. The introduction of this deviation takes into account the conformational flexibility of the Loop region, allowing the model to maintain structural rationality while also permitting a certain degree of conformational change.

[0082] The potential function of the main chain region of the protein is designed as follows:

[0083] For the protein backbone region, considering the requirements for structural regularity and stability, a resonant potential energy function based on the dihedral angle of the backbone was calculated:

[0084]

[0085] Where, θ i and θ0 and θ1 represent the main chain dihedral angles of the i-th residue. The corresponding ideal dihedral angle values ​​ensure that the main chain conformation tends to remain within a reasonable conformational space. β1 and β2 are potential energy adjustment coefficients that control the contribution of the dihedral angle to the structure. This differentiation treatment is particularly suitable for describing the characteristic dihedral angle distributions of different secondary structural elements (such as α-helices and β-folds).

[0086] S103: Based on the potential energy function of the Loop region and the potential energy function of the main chain region, a dual potential energy function is obtained.

[0087] For example: obtain the weighting coefficients of the relative contributions of the Loop region and the main chain region; and obtain the dual potential function based on the weighting coefficients, the potential function of the Loop region, and the potential function of the main chain region.

[0088] The dual potential energy function conforms to the dual resonant potential energy confinement system, and the dual potential energy function is also called the total potential energy function. The synergistic effect of the dual potential energy functions can be described by a single total potential energy function.

[0089] E total =w1·E loop +w2·E main (8)

[0090] Among them, w1 and w2 are weighting coefficients that balance the relative contributions of the Loop region and the main chain region.

[0091] In the embodiments of this application, a dual resonant potential energy confinement system is designed, which can achieve precise control of different structural domains through differentiated potential energy functions.

[0092] S104: Optimize the conformation of the protein by dynamically adjusting the constraint constant until the minimum value of the dual potential function is found.

[0093] In one embodiment of this application, optimizing the conformation of a protein based on dynamically adjusting the constraint force constant until the minimum value of the dual potential function is found includes: progressively optimizing the conformation of the protein based on dynamically adjusting the constraint force constant until the minimum value of the dual potential function is found.

[0094] In this example, the progressive optimization of the protein conformation based on the dynamically adjusted constraint constant until the minimum value of the dual potential function is found includes: an initial optimization stage: optimizing the protein conformation based on the initial dynamically adjusted constraint constant until the first iteration number is reached; an intermediate optimization stage: optimizing the protein conformation based on the intermediate dynamically adjusted constraint constant until the second iteration number is reached; a final optimization stage: optimizing the protein conformation based on the final dynamically adjusted constraint constant until the third iteration number is reached; and finding the minimum value of the dual potential function based on the optimization results of the initial optimization stage, the intermediate optimization stage, and the final optimization stage.

[0095] Specifically, in order to make the total potential energy function E total To reach the minimum value, in the embodiments of this application, for example, a progressive three-stage optimization method is adopted, which dynamically adjusts the constraint force constant (k). loop k main This is done by gradually guiding the energy to its minimum. Dynamically adjusting the constraint force constant directly affects each term in the total potential energy function. For example: k loop The potential energy term E affecting the loop region loop And k main The potential energy term E acting on the main chain region main .

[0096] Initial optimization phase:

[0097]

[0098]

[0099]

[0100] In this stage, the constraint terms are given greater weight in the total potential energy function to ensure that the basic geometric requirements are met first. This stage involves approximately 50 iterations.

[0101] Mid-term optimization phase:

[0102]

[0103]

[0104]

[0105] The adjustments made at this stage are reflected in the total potential function, manifested as changes in the relative weights of the terms, resulting in greater freedom in conformational exploration. In this stage, the number of iterations increases to, for example, 200, providing ample time to explore a broader conformational space.

[0106] Final optimization phase:

[0107]

[0108]

[0109]

[0110] At this point, the influence of the constraint terms in the total potential energy function is reduced, allowing for more freedom in adjusting the conformation to reach the energy minimum. The number of iterations in this stage, such as 500, ensures sufficient relaxation to the optimal conformation.

[0111] Energy minimization process:

[0112]

[0113] Where, N iter This represents the maximum number of iterations at each stage. Simultaneously, each stage minimizes the total potential energy V. total (r) is used to optimize the structure, with the constraint that the number of iterations does not exceed the maximum value of each stage.

[0114] After optimizing the protein conformation based on the dynamically adjusted constraint constant until the minimum value of the dual potential function is found, the method further includes: evaluating the structural quality of the optimized protein conformation to obtain the final protein conformation.

[0115] Specifically, the process involves constructing a molecular dynamics system and optimizing its structure using the AMBER force field system, which includes:

[0116] Force field configuration:

[0117] (1) Main field: AMBER ff19SB.

[0118] (2) Solvent model: TIP3P.

[0119] (3) Temperature: 300K.

[0120] (4) Langevin integrator parameters:

[0121] τ = 1.0ps -1 (13)

[0122] Δt=2.0fs (14)

[0123] Where τ is the damping coefficient in the Langevin integrator, in ps. -1 Δt is the time step, measured in femtoseconds (fs).

[0124] Constraints:

[0125] (1) H-bond constraint: Hydrogen bond constraint is usually implemented using the SHAKE or RATTLE algorithm, which allows for the use of larger time steps while maintaining system stability.

[0126] (2) Rigid water molecule treatment: ensure that water molecules maintain their geometric configuration.

[0127] (3) Removal of the center of mass motion: prevents the overall translation and rotation of the system, which helps to analyze the internal motion of molecules.

[0128] System boundary conditions:

[0129] Using aperiodic boundary conditions (NoCutoff) means that no distance cutoff is set when calculating nonbonded interactions, which can yield more accurate results.

[0130] In summary, the protein conformation energy minimization method of this application is specifically implemented as follows: Figure 2 As shown, the initial structure is first preprocessed, then the improved DSSP algorithm is used to identify the secondary structure, then the loop region and non-loop region are determined, and the potential energy function is constructed based on the dual-image potential energy constraint system. Then, the minimum value of the potential energy function is found based on the constraint force constant in a multi-stage optimization. Finally, the structural quality is evaluated and the final structure is output.

[0131] According to the protein conformation energy minimization method of this application, after determining the loop region and main chain region of the protein, the potential energy function of the loop region is obtained based on the hydrogen bond energy and the dihedral deviation of the main chain, and the potential energy function of the main chain region is obtained based on the dihedral deviation of the main chain. Then, based on the potential energy functions of the loop region and the main chain region, a dual potential energy function is obtained. Finally, the conformation of the protein is optimized by dynamically adjusting the constraint constant to find the minimum value of the dual potential energy function, thereby completing the optimization of the protein conformation. This method can effectively improve the accuracy of protein structure prediction, especially the accuracy of loop conformation. In addition, by adopting a dual resonant potential energy constraint system, which considers the conformational characteristics of the loop region and the non-loop region respectively, it can give the loop region greater conformational flexibility while maintaining the overall structural stability, improving the biological rationality of the predicted structure and providing an important structural basis for drug screening and structure-function research.

[0132] Furthermore, an intelligent region identification strategy based on an improved DSSP is introduced, which can adaptively identify the structural features of different regions in proteins such as GPCRs, providing a basis for region-specific optimization. A multi-stage optimization method is also designed, which dynamically adjusts the constraint force constant to achieve progressive optimization of GPCR structures from coarse to fine, thereby improving computational efficiency and optimization results.

[0133] Figure 3 This is a structural block diagram of a protein conformation energy minimization system according to an embodiment of this application. Figure 3 As shown, a protein conformation energy minimization system according to an embodiment of this application includes: a determination module 310, a dual potential function acquisition module 320, and an optimization module 330, wherein:

[0134] The determination module 310 is used to determine the Loop region and the main chain region of the protein; the dual potential function acquisition module 320 is used to obtain the potential function of the Loop region based on the hydrogen bond energy and the dihedral deviation of the main chain, and to obtain the potential function of the main chain region based on the dihedral deviation of the main chain, and to obtain the dual potential function based on the potential function of the Loop region and the potential function of the main chain region; the optimization module 330 is used to optimize the conformation of the protein by dynamically adjusting the constraint force constant until the minimum value of the dual potential function is found.

[0135] According to the protein conformation energy minimization method of this application, after determining the loop region and main chain region of the protein, the potential energy function of the loop region is obtained based on the hydrogen bond energy and the dihedral deviation of the main chain, and the potential energy function of the main chain region is obtained based on the dihedral deviation of the main chain. Then, based on the potential energy functions of the loop region and the main chain region, a dual potential energy function is obtained. Finally, the conformation of the protein is optimized by dynamically adjusting the constraint constant to find the minimum value of the dual potential energy function, thereby completing the optimization of the protein conformation. This method can effectively improve the accuracy of protein structure prediction, especially the accuracy of loop conformation. In addition, by adopting a dual resonant potential energy constraint system, which considers the conformational characteristics of the loop region and the non-loop region respectively, it can give the loop region greater conformational flexibility while maintaining the overall structural stability, improving the biological rationality of the predicted structure and providing an important structural basis for drug screening and structure-function research.

[0136] Specific limitations regarding the protein conformation energy minimization system can be found in the limitations of the protein conformation energy minimization method described above, and will not be repeated here. Each module of the aforementioned protein conformation energy minimization system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computing device in hardware form, or stored in the memory of the computing device in software form, so that the processor can call and execute the corresponding operations of each module.

[0137] The following is for reference. Figure 4 , Figure 4 A schematic diagram of a computing device structure suitable for implementing embodiments of this application is shown.

[0138] like Figure 4 As shown, the computing device 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 1002 or programs loaded from storage section 1008 into random access memory (RAM) 1003. The RAM 1003 also stores various programs and data required for the system's operating instructions. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0139] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 1010 as needed so that computer programs read from it can be installed into storage section 1008 as needed.

[0140] Specifically, according to embodiments of this application, the flowchart above refers to... Figure 1 The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program contains program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs the functions defined in the system of this application.

[0141] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0142] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operational instructions of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the above figures. For example, two connected blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified functions or operational instructions, or using a combination of dedicated hardware and computer instructions.

[0143] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be located in a processor. The names of these units or modules do not, in certain circumstances, constitute a limitation on the unit or module itself.

[0144] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for minimizing protein conformation energy, characterized in that, include: Identify the loop region and main chain region of the protein; The potential energy function of the Loop region is obtained based on the hydrogen bond energy and the main chain dihedral angle deviation, and the potential energy function of the main chain region is obtained based on the main chain dihedral angle. Based on the potential energy function of the Loop region and the potential energy function of the main chain region, a dual potential energy function is obtained; The conformation of the protein is optimized by dynamically adjusting the constraint constant until the minimum value of the dual potential function is found.

2. The protein conformation energy minimization method according to claim 1, characterized in that, The determination of the protein's loop region and main chain region includes: The hydrogen bond energy calculation function is obtained based on the atomic bias charge, the distance between atomic pairs, and the distance-dependent decay factor of the protein. Based on the hydrogen bond energy calculation function, the secondary structure determination function is obtained; Obtain the Loop region determination criteria, and derive the Loop region determination function based on the Loop region determination criteria; The Loop region and main chain region of the protein are determined based on the Loop region determination function and the number of residues in the Loop region.

3. The protein conformation energy minimization method according to claim 1, characterized in that, The process of obtaining the Loop region potential function based on hydrogen bond energy and main chain dihedral angle deviation, and obtaining the main chain region potential function based on the main chain dihedral angle, includes: The adjustment coefficient is obtained, and the potential energy function of the Loop region is obtained based on the adjustment coefficient, the hydrogen bond energy, and the deviation between the main chain dihedral angle and the ideal dihedral angle. The potential energy adjustment coefficient is obtained, and the potential energy function of the main chain region is obtained based on the potential energy adjustment coefficient, the main chain dihedral angle, and the ideal dihedral angle.

4. The protein conformation energy minimization method according to claim 1, characterized in that, The process of obtaining the dual potential function based on the Loop region potential function and the main chain region potential function includes: Obtain the weighting coefficients for the relative contributions of the Loop region and the main chain region; The dual potential function is obtained based on the weighting coefficients, the potential energy function of the Loop region, and the potential energy function of the main chain region.

5. The protein conformation energy minimization method according to any one of claims 1-4, characterized in that, The optimization of protein conformation based on dynamically adjusting the constraint force constant until the minimum value of the dual potential function is found includes: The conformation of the protein is progressively optimized by dynamically adjusting the constraint constant until the minimum value of the dual potential function is found.

6. The protein conformation energy minimization method according to claim 5, characterized in that, The stepwise optimization of the protein conformation based on dynamically adjusting the constraint force constant until the minimum value of the dual potential function is found includes: Initial optimization phase: Based on the initial dynamic adjustment constraint constant, the conformation of the protein is optimized until the first iteration number is reached; Mid-term optimization phase: Based on the mid-term dynamic adjustment constraint constant, the conformation of the protein is optimized until the second iteration number is reached; Final optimization stage: Based on the final dynamic adjustment constraint constant, the conformation of the protein is optimized until the third iteration is reached; Based on the optimization results of the initial optimization stage, the intermediate optimization stage, and the final optimization stage, the minimum value of the dual potential energy function is found.

7. The protein conformation energy minimization method according to claim 1, characterized in that, After optimizing the protein conformation by dynamically adjusting the constraint force constant until the minimum value of the dual potential function is found, the process further includes: The optimized protein conformation is subjected to structural quality assessment to obtain the final protein conformation.

8. A protein conformation energy minimization system, characterized in that, include: The determination module is used to identify the loop region and main chain region of a protein; The dual potential energy function acquisition module is used to obtain the potential energy function of the Loop region based on the hydrogen bond energy and the main chain dihedral angle deviation, obtain the potential energy function of the main chain region based on the main chain dihedral angle, and obtain the dual potential energy function based on the Loop region potential energy function and the main chain region potential energy function. The optimization module is used to optimize the conformation of the protein by dynamically adjusting the constraint force constant until the minimum value of the dual potential function is found.

9. A computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the protein conformation energy minimization method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the protein conformation energy minimization method according to any one of claims 1-7.

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