CN114121148A - Method for calculating protein-ligand binding free energy based on cluster model - Google Patents

Method for calculating protein-ligand binding free energy based on cluster model Download PDF

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CN114121148A
CN114121148A CN202111333136.0A CN202111333136A CN114121148A CN 114121148 A CN114121148 A CN 114121148A CN 202111333136 A CN202111333136 A CN 202111333136A CN 114121148 A CN114121148 A CN 114121148A
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丁泓铭
陈远强
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Abstract

Compared with other methods for calculating the protein-ligand binding free energy, the GFN2-xTB method is easier to use, only initial coordinates and element composition are needed to form an input structure, and the time cost of calculation is effectively reduced on the premise of ensuring relatively good accuracy by combined application with the cluster model. This new method based on cluster models and GFN2-xTB should have great potential in future biomacromolecule-related binding free energy calculations.

Description

Method for calculating protein-ligand binding free energy based on cluster model
Technical Field
The invention relates to a method for calculating protein-ligand binding free energy based on a cluster model, belonging to the technical field of computational biology.
Background
Protein-ligand interactions play an important role in many biochemical processes and biomedical applications (e.g., immune responses, signal transduction, drug design). The research on the interaction affinity (i.e. binding free energy) of the protein and the ligand not only helps to understand the biological function of the protein, but also has important significance on the research and development of drugs and the research on the action mechanism of the drugs. For example, the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) virus causes the COVID-19 pandemic, and its major protease and spike protein can be used as a good drug target. The binding free energy of SARS-CoV-2 protease or spike protein and corresponding ligand can be accurately calculated, and high affinity ligand and inhibitor can be screened out, so that it can be effectively used for curing SARS-CoV-2 protease or spike protein. Protein-ligand binding is a complex process involving thousands or even tens of thousands of atoms, and many factors affect the calculation of the binding free energy. Therefore, how to accurately and efficiently calculate the free energy of protein-ligand binding is an extremely important topic in the field of computational biology at present.
In the past decades, in order to accurately calculate the binding free energy of protein-ligand, various methods have been proposed and developed, mainly molecular mechanics-based methods, such as the Free Energy Perturbation (FEP) method and the molecular mechanics/poisson boltzmann surface area (MM/PBSA) and molecular mechanics/generalized boltzmann surface area (MM/GBSA) methods. Free Energy Perturbation (FEP) is a widely used free energy method for metallurgy. Over the past few years, due to several improvements in computational power, classical force field accuracy, sampling methods and simulation settings, FEP has been made accurately and reliably applicable to the calculation of protein-ligand binding free energy. In detail, the FEP method relies on atomic analysis of the energy difference between two similar compounds and switches between the compounds through an alloying route. This is a strict statistical mechanical method, which requires the calculation of the free energy change during the gold extraction process by slowly changing the potential energy. MM/PBSA and MM/GBSA are methods for calculating the binding free energy of macromolecules by combining molecular mechanics calculation and a continuous solvation model, and are end-point free energy methods which are widely used for evaluating docking sites, determining structural stability and predicting binding affinity. The method comprises the steps of firstly, performing MD simulation of a protein-ligand complex under a display solvent model; then all solvent molecules and charged ions are deleted from each MD snapshot, and the solvation energy is evaluated using an implicit PBSA or GBSA solvent model; finally, the conformational entropy change of the solute is calculated from a selected set of snapshots, and the individual energy components are added to obtain the final binding free energy.
Recently, besides molecular mechanics methods, the semi-empirical quantum mechanics method GFN-xTB of "geometry, frequency, noncovalent, extended tight binding" has also gained widespread interest and application. The method is an extended semi-empirical tightly-bound model proposed by Grimme et al on the basis of Kohn-Sham Density Functional Theory (DFT). Compared with other traditional semi-empirical quantum mechanical methods, the GFN2-XTB method is high in calculation efficiency and accuracy, and parameterized elements of the GFN2-XTB method cover most of the periodic table of elements until Z is 86. The proposed GFN2-XTB method allows structural optimization, vibrational frequency and computational studies of non-covalent interactions on macromolecular systems of more than one thousand atoms. At present, the GFN2-xTB method is already applied to structure optimization screening and interaction energy calculation of some chemical macromolecules, and all the chemical macromolecules show good performance, so that the GFN2-xTB method is expected to be popularized and applied to free energy calculation of combination of a protein-ligand system.
Although different methods exist for the research and prediction of protein-ligand binding free energy, the methods all have some corresponding disadvantages and shortcomings. Firstly, the calculation of protein-ligand binding free energy by using methods such as FEP (fluorinated ethylene propylene) and MM/PBSA (MM/GBSA) is generally complicated, corresponding force field parameters need to be found, and the simulation setting and data analysis are also complicated; secondly, the accuracy of the FEP method in estimating protein-ligand binding capacity is high, with an error of about 2-3kcal/mol, but it is very time consuming and difficult to converge. In comparison, the MM/PBSA (MM/GBSA) method requires much lower computational cost, but its accuracy is general, with errors typically around 10kcal/mol, and in high charge systems, errors even greater than 20 kcal/mol.
In addition, the semi-empirical, tightly-bound GFN2-xTB method has shown some potential in calculating macromolecular interactions, but for large protein-ligand systems, the GFN2-xTB method is time-consuming to calculate, and cannot be used directly at present, due to the large amount of calculation.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for calculating the binding free energy of the protein-ligand by combining a cluster model with GFN2-xTB, compared with other methods for calculating the binding free energy of the protein-ligand, the GFN2-xTB method is easier to use, an input structure can be formed only by initial coordinates and element composition, and the time consumed in calculation can be effectively reduced on the premise of keeping good calculation accuracy by combined application with the cluster model.
The first object of the present invention is to provide a method for calculating protein-ligand binding free energy based on a cluster model, comprising the steps of:
s1, heavy atom information of protein and ligand structures is respectively obtained;
s2, opening the obtained heavy atom information of the protein and ligand structures in three-dimensional molecular model software, intercepting protein residues with the intercepting distance within a preset range, and storing the intercepted protein-ligand structures;
s3, carrying out hydrogenation saturation on the intercepted protein-ligand structure to obtain a cluster model after hydrogenation saturation;
s4, inputting the cluster model after hydrogenation saturation into a xtb program, setting a strong constraint on heavy atoms of truncated proteins in the cluster model after hydrogenation saturation, and setting an environment model of protein-ligand interaction;
s5, dividing the cluster model with strong constraint into three structures of a cluster model whole body, a truncated protein residue and a ligand, respectively carrying out Hessian calculation on the three structures by using a xtb program, and reading the free energy G of the cluster model whole body from an output filecomFree energy of truncated protein residue GproAnd free energy G of the ligandligThe binding free energy Δ G of the protein-ligand is calculated from the above three free energies.
Further, the protein-ligand binding free energy Δ G follows the protein and ligand binding free energy formula: g ═ Gcom-Gpro-GligCalculating;
wherein AG is the binding free energy of protein-ligand,
Gcomis the free energy of the cluster model as a whole,
Gproto truncate the free energy of the protein residue,
Gligis the free energy of the ligand.
Further, the preset range of the truncation distance is
Figure BDA0003349540270000031
And (3) a range.
Further, in the present invention, when Hessian calculation is performed by using the xtb program, the GFN2-xTB method is adopted, and the specific calculation process is described in c.bannwarth; s.ehlert, et al.j.chem.the theory company.2019, 15, 1652-; e.caldeweyher, et al wiley interdiscip.rev.: comput.mol.sci.2021,11, e1493.
Further, the three-dimensional molecular model software is one or two of PyMOL, VMD or GaussView.
Among these, VMD and PyMOL can be used to intercept proteins, GaussView and PyMOL can hydrogenate the structure because PyMOL can do both steps separately and the resulting PDB structure includes charge information for protein residues, preferably processed using PyMOL software.
Further, the force constant of the strong constraint is selected to be 0.5-1.0 Hartree/Bohr2。
Further, the environment model is an implicit hydrosolvent model, a vacuum model, a methanol solvent model, a toluene solvent model or an ethane model.
Further, in step S1, heavy atom information of protein and ligand structures was obtained from PDB-BIND website (http:// www.pdbbind-cn. org /) and protein database website (http:// www.rcsb.org/PDB /). For unknown protein-ligand, the possible binding sites need to be obtained by molecular docking method, and then the binding free energy is obtained by calculation according to the method.
Further, in the step S3, the hydrogenation saturation is performed using three-dimensional molecular modeling software.
The invention has the beneficial effects that:
compared with other methods for calculating protein-ligand binding free energy, the GFN2-xTB method is easier to use, only initial coordinates and element composition are needed to form an input structure, and the combined application of the GFN2-xTB method and a cluster model effectively reduces the calculation time cost on the premise of ensuring relatively good accuracy. This new method based on cluster models and GFN2-xTB should have great potential in future biomacromolecule-related binding free energy calculations.
Description of the drawings:
FIG. 1 is a computational flow diagram;
FIG. 2 is a cluster model (PDB:1A28) at different truncation distances;
FIG. 3 is a graph showing the dependence of the binding free energy on the truncation distance calculated by the GFN2-xTB method;
FIG. 4 is the MAE values and calculated durations of protein ligand binding free energy calculated using the GFN2-xTB method in conjunction with the cluster model;
fig. 5 is an output file corresponding energy item.
Detailed Description
The present invention is further described below in conjunction with specific examples to enable those skilled in the art to better understand the present invention and to practice it, but the examples are not intended to limit the present invention.
In order to accurately and efficiently calculate the free energy of protein-ligand binding using GFN2-xTB, a cluster model was used. FIG. 1 shows a specific calculation procedure, in detail, first selecting the protein-ligand complex to be calculated from the PDB-BIND website (http:// www.pdbbind-cn. org /), downloading the corresponding PDB structure from the protein database website (http:// www.rcsb.org/PDB /), and secondly, extracting heavy atoms of the protein and ligand structures from the PDB file and opening them in the PyMOL package to intercept the cluster model, i.e., the protein is truncated at a truncation distance different from the ligand; at the same time, it is necessary to saturate the selected residues with hydrogen atoms in PyMOL software. It is noteworthy that we have here as a truncation a residue, i.e. all atoms in the selected residue are retained, which is useful for determining the total charge of the truncated protein. This would generate the input structure required for the xtb (version 6.3.3) program, xtb packages are available for free at the https:// github. com/grimme-lab/xtb website.
After obtaining the desired input structure, we performed a preliminary optimization of the initial structure using the GFN2-xTB method of the program xtb, in order to calculate the binding of protein-ligand interactions in the aqueous phaseFree energy, we further divided the optimized structure into one complex and two monomers and performed Hessian calculations for GFN2-xTB, respectively. In the above two steps, we use the implicit hydrosolvent model calculation, i.e. the Generalized Born (GB) model with Surface Area (SA) contribution (GBSA (H)2O)). Furthermore, since we use the cluster model, we set a strong constraint on the heavy atoms of the truncated protein (force constant chosen to be 1.0 Hartree/Bohr)2)。
In the embodiment of the invention, the method for calculating the binding free energy of the protein and the ligand based on the existing coordinate determination in the PDB is used for verifying the accuracy of the method. For unknown protein-ligand, the possible binding sites need to be obtained by molecular docking method, and then the binding free energy is obtained by calculation according to the method.
Example 1: optimal cluster model scheme
This example illustrates the 1A28 protein as an example, and shows its cluster model at different truncation distances, as shown in FIG. 2. When the cutoff value is
Figure BDA0003349540270000051
When the protein has only one residue, this is clearly insufficient. As the intercept increases, the number of residues/atom increases and the ligand can be surrounded by residues. Is cut off into
Figure BDA0003349540270000052
The total number of atoms is close to 1500, approaching the upper limit of the GFN2-xTB process. To determine the accuracy and reliability of the cut-off values, we selected eight protein ligand systems and calculated the binding free energies at different cut-off distances using the GFN2-xTB method, and evaluated the dependence of the calculated predicted binding free energies on the cut-off values. As shown in fig. 3, it was found that the calculated binding free energy tends to level off with increasing truncation distance. When the cut-off distance is greater than
Figure BDA0003349540270000053
The calculated binding free energy change is small. The preset range within which the cutoff distance can be judged is
Figure BDA0003349540270000054
Figure BDA0003349540270000055
Example 2:
the present example calculates the binding free energies of 25 groups of protein-ligand complexes using cluster models with different cut-off values in combination with the GFN2-xTB method according to the above-described scheme, and further determines the cut-off range of the cluster model.
As shown in FIG. 4a, we have found that when the truncation distance is
Figure BDA0003349540270000056
Or
Figure BDA0003349540270000057
Then, the MAE for calculating the protein-ligand binding free energy using the GFN2-xTB method was 10.4kcal/mol and 6.1kcal/mol, errors of both methods were comparable to the aforementioned MM/PBSA (MM/GBSA) errors, and there was also a problem that residues interacting with the ligand were not truncated, so that the truncation was not applicable to the cluster model. When the cutoff value is
Figure BDA0003349540270000058
Most of the MAE values for protein-ligand binding free energy calculated using the GFN2-xTB method were less than 10.0kcal/mol, and the average MAE value was about 5.0kcal/mol, close to the FEP method, with relatively good accuracy. Also shown in FIG. 4b is the elapsed time for calculation of the protein-ligand cluster model (elapsed time includes initial structure optimization and calculation of monomer and complex frequencies, 12 cores were used on the Intel Xeon Platinum9242@2.3GHz cluster), we found that when the cutoff value is
Figure BDA0003349540270000059
The average calculation time is about 26h, the time cost is high, and the cutoff value is
Figure BDA00033495402700000510
The calculation cost is moderate. Thus, in calculating the free energy of protein-ligand binding by the cluster model binding GFN2-xTB method, the cutoff value is
Figure BDA00033495402700000511
Are all good choices. Also consider the use of a cutoff value of
Figure BDA00033495402700000512
When the cluster model is calculated by combining the GFN2-xTB method, the calculation time of most of the protein-ligand cluster models is less than two hours, the average calculation time is 4574s, the calculation time cost can be effectively reduced, and simultaneously, the protein residues interacted with the ligand can be more completely and reliably intercepted under individual conditions. Therefore we consider the cutoff value to be
Figure BDA00033495402700000513
Is a safer and more effective choice.
Example 3:
using protein-ligand complex 1AU0 as an example, the binding free energy was calculated according to the method of the present invention and compared with experimental binding free energy values calculated from information published on the website.
(1) First, protein-ligand complex 1AU0 was selected from PDB-BIND website, and the essential information of the complex in the page is given, including protein name, corresponding ligand (SDK) and pKdValue (7.66), etc. We can pass pKd＝-logKdAnd Δ G ═ RTlnKdThe corresponding experimental binding free energy values were determined.
(2) After obtaining the information related to the protein 1AU0, we search and download the corresponding PDB structure from the protein database website, and need to extract the heavy atoms of the protein and ligand structures from the PDB file. If no treatment is carried out, PyMOL is directly used for hydrogenation saturation, 3329 atoms exist, GFN2-xTB is used for directly carrying out optimization and Hessian calculation, and the time cost is too high. We therefore used the cluster model to calculate the binding free energy of proteins and ligands using the method of GFN2-xTB binding.
(3) PyMOL opens a PDB structure containing only protein and ligand heavy atoms, and intercepts the protein and ligand heavy atoms with the ligand (SDK) of 1AU0 as a truncation point
Figure BDA0003349540270000061
Protein residues within the range and preserve the truncated protein-ligand structure. The method comprises the following specific operations: "select ligand, resn SDK" and "select 6A, byres ligand expanded 6" are entered in the command line PyMOL >. Then the processed structure is stored: file > Export Mobile > selection > 6A > PDB Options > Save.
(4) At the same time, it needs to be saturated and stored by hydrogen in PyMOL, and the protein-ligand complex cluster model only has 489 atoms. This will also generate the input structure required for the xtb program, i.e., the cluster model after hydrosaturation.
(5) After obtaining the desired input structure, we first restrictively optimize it using the GFN2-xTB method of the program xtb.
(6) To calculate the binding free energy of protein-ligand interactions in the aqueous phase, we further divided the optimized structure into one complex (cluster model ensemble) and two monomers (truncated protein residue and ligand) and performed Hessian calculations for GFN2-xTB, respectively.
In the fifth and sixth steps, we use the implicit hydrosolvent model calculation, i.e. GBSA (H)2O). Since we use the cluster model, we have additionally set an input file md. inp, where a strong constraint can be placed on the heavy atom of the truncated protein (force constant chosen to be 1.0Hartree/Bohr2). Note that Hessian calculations on the ligands do not require reading this input file. After the calculation is completed, finally adding or subtracting the corresponding energy components in the output file (as shown in FIG. 5) according to the protein and ligand binding free energy formula, namely reading the corresponding energy of the calculation complex and recording as Gcom(FIG. 5a), the corresponding energies of the two monomers (protein residue and ligand) are denoted Gpro(FIG. 5b) and Glig(FIG. 5c), calculating the corresponding Δ G as Gcom-Gpro-Glig. Thereby obtaining the final free energy of binding。
The protein and ligand binding free energy formula is:
ΔG＝Gcom-Gpro-Glig (1)
wherein G iscomBeing the free energy of the protein-ligand complex, GproBeing the free energy of the protein, GligIs the free energy of the ligand.
The corresponding calculations for protein 1AU0 are given in table one. The table gives not only the binding free energy and the long time it takes for our protein-ligand cluster model to calculate using the GFN2-xTB method, but also other energy contributions to the free energy. It can be seen that the binding cluster model uses the GFN2-xTB method to calculate the protein-ligand binding free energy, which can effectively reduce the time cost, and also maintains relatively good accuracy.
TABLE-protein 1AU0 binding free energy results calculated using the GFN2-XTB method
PDB Gcom/Eh Gpro/Eh Glig/Eh ΔG(kcal/mol) Experiment Δ G (kcal/mol) WALL Time/s
1AU0 -776.618 -649.127 -127.480 -6.90 -10.45 4955
Note: 627.51kcal/mol
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.

Claims (8)

1. A method for calculating protein-ligand binding free energy based on a cluster model, comprising the steps of:
s1, heavy atom information of protein and ligand structures is respectively obtained;
s2, opening the obtained heavy atom information of the protein and ligand structures in three-dimensional molecular model software, intercepting protein residues with the intercepting distance within a preset range, and storing the intercepted protein-ligand structures;
s3, carrying out hydrogenation saturation on the intercepted protein-ligand structure to obtain a cluster model after hydrogenation saturation;
s4, inputting the cluster model after hydrogenation saturation into a xtb program, setting a strong constraint on heavy atoms of truncated proteins in the cluster model after hydrogenation saturation, and setting an environment model of protein-ligand interaction;
s5, dividing the cluster model with strong constraint into three structures of a cluster model whole body, a truncated protein residue and a ligand, respectively carrying out Hessian calculation on the three structures by using a xtb program, and reading the free energy G of the cluster model whole body from an output filecomFree energy of truncated protein residue GproAnd free energy G of the ligandligFromThe three free energies described above were calculated to yield the protein-ligand binding free energy Δ G.
2. The method of claim 1, wherein the protein-ligand binding free energy Δ G follows the protein and ligand binding free energy formula: g ═ Gcom-Gpro-GligCalculating;
wherein AG is the binding free energy of protein-ligand,
Gcomis the free energy of the cluster model as a whole,
Gproto truncate the free energy of the protein residue,
Gligis the free energy of the ligand.
3. The method of claim 1, wherein the predetermined range of the truncation distance is
Figure FDA0003349540260000011
Figure FDA0003349540260000012
And (3) a range.
4. The method according to claim 1, wherein the three-dimensional molecular modeling software is one or two of PyMOL, VMD, or GaussView.
5. The method of claim 1, wherein the strongly constrained force constant is selected from 0.5 to 1.0Hartree/Bohr2。
6. The method of claim 1, wherein the environmental model is an implicit water solvent model, a vacuum model, a methanol solvent model, a toluene solvent model, or an ethane model.
7. The method as claimed in claim 1, wherein in the step of S1, the heavy atom information of the protein and ligand structure is obtained from PDB-BIND website and protein database website.
8. The method of claim 1, wherein the hydrogenation saturation in the step of S3 is performed by using three-dimensional molecular modeling software.
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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105683379A (en) * 2013-06-17 2016-06-15 布罗德研究所有限公司 Delivery, engineering and optimization of systems, methods and compositions for targeting and modeling diseases and disorders of post mitotic cells
EP3460689A1 (en) * 2016-05-16 2019-03-27 Fujitsu Limited Method and device for calculating binding free energy, and program
CN110851617A (en) * 2019-10-10 2020-02-28 中国海洋大学 Knowledge graph-based multi-source information drug screening method
CN110910951A (en) * 2019-11-19 2020-03-24 江苏理工学院 Method for predicting protein and ligand binding free energy based on progressive neural network
CN111816250A (en) * 2020-06-17 2020-10-23 华中科技大学 Method for mapping macromolecular complex structures to genomic and mutation databases
CN111951884A (en) * 2020-07-10 2020-11-17 中南大学 Identification method of flexible amino acid key on protein small molecule binding pocket
BE1027448A1 (en) * 2020-11-15 2021-02-12 Institute Of Food Science And Tech Chinese Academy Of Agricultural Sciences A method for evaluating the structure-activity of polypeptides with osteogenic activity
CN113402509A (en) * 2021-06-17 2021-09-17 山东大学 Meishadazole compounds and preparation method and application thereof
CN113593633A (en) * 2021-08-02 2021-11-02 中国石油大学(华东) Drug-protein interaction prediction model based on convolutional neural network

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105683379A (en) * 2013-06-17 2016-06-15 布罗德研究所有限公司 Delivery, engineering and optimization of systems, methods and compositions for targeting and modeling diseases and disorders of post mitotic cells
EP3460689A1 (en) * 2016-05-16 2019-03-27 Fujitsu Limited Method and device for calculating binding free energy, and program
CN110851617A (en) * 2019-10-10 2020-02-28 中国海洋大学 Knowledge graph-based multi-source information drug screening method
CN110910951A (en) * 2019-11-19 2020-03-24 江苏理工学院 Method for predicting protein and ligand binding free energy based on progressive neural network
CN111816250A (en) * 2020-06-17 2020-10-23 华中科技大学 Method for mapping macromolecular complex structures to genomic and mutation databases
CN111951884A (en) * 2020-07-10 2020-11-17 中南大学 Identification method of flexible amino acid key on protein small molecule binding pocket
BE1027448A1 (en) * 2020-11-15 2021-02-12 Institute Of Food Science And Tech Chinese Academy Of Agricultural Sciences A method for evaluating the structure-activity of polypeptides with osteogenic activity
CN113402509A (en) * 2021-06-17 2021-09-17 山东大学 Meishadazole compounds and preparation method and application thereof
CN113593633A (en) * 2021-08-02 2021-11-02 中国石油大学(华东) Drug-protein interaction prediction model based on convolutional neural network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
YAN-JING SHENG 等: "Improving the Performance of MM/PBSA in Protein-Protein Interactions via the Screening Electrostatic Energy", 《ACS PUBLICATIONS》 *
闫东 等: "基于网络药理学及分子对接技术分析康艾注射液治疗肝细胞癌的物质基础与分子机制", 《肝癌电子杂志》 *

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