CN114121148B - Method for calculating protein-ligand binding free energy based on cluster model - Google Patents
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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 combined application of the GFN2-xTB method and the cluster model can effectively reduce 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
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 particular, the FEP method relies on atomic analysis of the energy difference between two similar compounds and switches between compounds through the route of metallurgy. This is a strict statistical mechanical method, which requires calculation of the free energy change during the gold smelting 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 computationally efficient and accurate, and its parameterized elements cover a large portion of the periodic table up to Z =86. The proposal of the GFN2-XTB method makes it possible to carry out computational studies of structural optimization, vibration frequency and 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 20kcal/mol.
In addition, the semi-empirical tight-bound GFN2-xTB method has shown some potential in the calculation of macromolecular interactions, but for large protein-ligand systems, the calculation frequency of the GFN2-xTB method is very time-consuming and cannot be directly used at present due to the large calculation amount of the GFN2-xTB method.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for calculating the free energy of protein-ligand combination by combining a cluster model with a GFN2-xTB method, compared with other methods for calculating the free energy of protein-ligand combination, the GFN2-xTB method is easier to use, an input structure can be formed only by initial coordinates and element composition, and the combined application of the GFN2-xTB method and the cluster model can effectively reduce the consumed time in calculation on the premise of keeping good calculation accuracy.
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 an 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 an xtb program, and reading the free energy G of the cluster model whole body from an output file com Free energy of truncated protein residue G pro And free energy G of the ligand lig The 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: Δ G = G com -G pro -G lig Calculating;
wherein AG is the binding free energy of protein-ligand,
G com is the free energy of the cluster model as a whole,
G pro in order to truncate the free energy of the protein residue,
G lig is the free energy of the ligand.
Further, the preset range of the truncation distance is
And (3) a range.
Further, in the present invention, when the xTB program is used for Hessian calculation, the GFN2-xTB method is used, and the specific calculation process is described in c.bannwarth; s.ehlert, et al.j.chem.the theory company.2019, 15,1652-1671, and c.bannwarth; 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/Bohr 2 。
Further, the environment model is an implicit water solvent model, a vacuum model, a methanol solvent model, a toluene solvent model or an ethane model.
Further, in S1 step, heavy atom information of protein and ligand structures is 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, hydrogenation saturation is performed by using three-dimensional molecular model software.
The beneficial effects of the invention are:
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 GFN2-xTB method is combined with a cluster model to be applied, so that the calculation time cost is effectively reduced 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 flow chart of a calculation;
FIG. 2 is a cluster model (PDB: 1A 28) 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 value and calculated length of time of protein ligand binding free energy calculated using GFN2-xTB method in conjunction with a cluster model;
fig. 5 is an output file corresponding energy item.
Detailed Description
The present invention is further described below with reference to specific examples so that those skilled in the art can better understand the present invention and can practice the present invention, but the examples are not intended to limit the present invention.
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, firstly, selecting a protein-ligand complex to be calculated from a PDB-BIND website (http:// www.pdbbind-cn.org /), then downloading a corresponding PDB structure from a protein database website (http:// www.rcsb.org/PDB /), secondly, extracting heavy atoms of the protein and ligand structures from the PDB file, and opening in a PyMOL package to intercept a cluster model, i.e., the protein is truncated at a truncation distance different from that of the ligand; at the same time, the PyMOL software requires the addition of a hydrogen atom to saturate the selected residues. It is noteworthy that we have here as a truncation the residue, i.e. all atoms in the selected residue are retained, which is useful for determining the total charge of the truncated protein. This will generate the input structures required for the xtb (version 6.3.3) program, which is freely available on the https:// github. Com/grimme-lab/xtb website.
After obtaining the desired input structure, we initially optimized the initial structure using the GFN2-xTB method of the xTB program, and in order to calculate the binding free energy of protein-ligand interactions in the aqueous phase, 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 (GBSA (H) with Surface Area (SA) contribution 2 O)). 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 is determined based on the coordinates in the PDB, and 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 calculated 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
When there is only one residue in the protein, 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
The total number of atoms is close to 1500, approaching the upper limit of the GFN2-xTB method. To determine the accuracy and reliability of the cutoff values, we selected eight protein ligand systems and calculated the binding free energies at different cutoff distances using the GFN2-xTB method and evaluated the calculated dependence of the predicted binding free energies on the cutoff values. As shown in fig. 3, it was found that the calculated binding free energy tends to plateau with increasing truncation distance. When the cut-off distance is greater than
The calculated binding free energy change is small. Where the preset range of the cutoff distance can be judged to be
Example 2:
the present example calculates the binding free energies of 25 groups of protein-ligand complexes using the cluster model binding GFN2-xTB method with different cut-off values according to the above procedure, and further determines the cut-off range of the cluster model.
As shown in FIG. 4aWe find that when the truncation distance is
Or
When the MAE for calculating the protein-ligand binding free energy using the GFN2-xTB method was 10.4kcal/mol and 6.1kcal/mol, the 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
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 length of time it takes to calculate the protein-ligand cluster model (the length of time spent includes initial structural optimization and calculation of the frequency of monomers and complexes, using 12 cores on the Intel Xeon platinum9242@2.3GHz cluster), we found that when the cutoff is at
The average calculation time is about 26h, the time cost is high, and the cutoff value is
The calculation cost is moderate. Thus, in the calculation of protein-ligand binding free energy by the cluster model binding GFN2-xTB method, the cutoff value is
Are all good choices. Also consider the use of a cutoff value of
When the cluster model of (A) is calculated by combining the GFN2-xTB method, the calculation of the cluster model of most protein-ligandsThe time is less than two hours, and the average calculation time is 4574s, so that the calculation time cost can be effectively reduced, and the protein residue interacted with the ligand can be more completely and reliably intercepted in individual cases. Therefore we consider the cutoff value to be
Is a safer and more effective choice.
Example 3:
using protein-ligand complex 1AU0 as an example, the binding free energy is calculated according to the method of the present invention and compared to the experimental binding free energy value calculated from the information published on the website.
(1) First, protein-ligand complex 1AU0 is selected from PDB-BIND website, and the basic information of the complex in the page is given, including protein name, corresponding ligand (SDK) and pK d Value (7.66), etc. We can pass pK d ＝-logK d And Δ G = RTlnK d The 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 GFN2-xTB binding method.
(3) Opening PDB structure containing protein and ligand heavy atom only in PyMOL, taking ligand (SDK) of 1AU0 as truncation point, and intercepting the PDB structure
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 and 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 required input structure, we first perform a restrictive optimization on it using the GFN2-xTB method of the xTB program.
(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 separately.
In the fifth and sixth steps, we use the implicit hydrosolvent model calculation, i.e. GBSA (H) 2 O). 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/Bohr 2 ). 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 component in the output file (as shown in figure 5) according to the free energy formula of protein and ligand combination, namely reading and calculating the corresponding energy of the complex and recording as G com (FIG. 5 a), the corresponding energies of the two monomers (protein residue and ligand) are denoted G pro (FIG. 5 b) and G lig (FIG. 5 c), calculating the corresponding Δ G as G com -G pro -G lig . Thereby obtaining the final free energy of binding.
The protein and ligand binding free energy formula is:
ΔG＝G com -G pro -G lig (1)
wherein G is com Being the free energy of the protein-ligand complex, G pro Being the free energy of the protein, G lig Is 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 calculation of the protein-ligand cluster model 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 | G com /Eh | G pro /Eh | G lig /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: 1Eh =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 (6)
1. A method for calculating protein-ligand binding free energy based on a cluster model, comprising the steps of:
s1, respectively obtaining heavy atom information of protein and ligand structures;
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 and the ligand together into a protein-ligand structure;
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 GFN2-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; wherein the strongly constrained force constant is selected to be 0.5-1.0 Hartree/Bohr 2 (ii) a The environment model is an implicit hydrosolvent model, a vacuum model, a methanol solvent model, a toluene solvent model or an ethane model;
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 performing Hessian calculation on the three structures by using a GFN2-xTB method, and reading the free energy G of the cluster model whole body from an output file com Free energy of truncated protein residue G pro And free energy G of the ligand lig And calculating the binding free energy delta G of the protein-ligand according to the three free energies, wherein the cluster model is a protein-ligand structure which is subjected to hydrogenation saturation and has strong constraint as a whole.
2. The method of claim 1, wherein the free energy of protein-ligand bindingΔ G according to the protein and ligand binding free energy formula: Δ G = G com -G pro -G lig Calculating;
wherein AG is the binding free energy of protein-ligand,
G com is the free energy of the cluster model as a whole,
G pro in order to truncate the free energy of the protein residue,
G lig is the free energy of the ligand.
3. The method of claim 1, wherein the predetermined range of the truncation distance is
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 in step S1, the heavy atom information of the protein and ligand structures is obtained from PDB-BIND website and protein database website.
6. The method of claim 1, wherein in the step S3, the hydrogenation saturation is performed by using three-dimensional molecular modeling software.
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