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

A model calculation and ligand technology, applied in the field of computational biology, can solve problems such as difficulties, time-consuming convergence, and general accuracy, and achieve the effects of ease of use, reduced time cost, and good accuracy

Active Publication Date: 2022-03-01
SUZHOU UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Firstly, the calculation of protein-ligand binding free energy using FEP and MM/PBSA (MM/GBSA) methods is usually cumbersome, and the corresponding force field parameters need to be found, and the simulation settings and data analysis are also relatively complicated; secondly, the FEP method to estimate the protein-ligand The accuracy of the bulk binding energy is very high, the error is about 2-3kcal/mol, but it is extremely time-consuming and difficult to converge
In comparison, the calculation cost required by th...

Method used

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

Examples

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Effect test

Embodiment 1

[0038] Example 1: Optimal Cluster Model Scheme

[0039] In this example, the 1A28 protein is taken as an example to show its cluster models at different cut-off distances, such as figure 2 shown. When the cutoff value is , there is only one residue in the protein, which is obviously not enough. As the intercept increases, the number of residues / atoms increases and the ligand can be surrounded by residues. truncated to , the total number of atoms is close to 1500, which is close to the upper limit of the GFN2-xTB method. In order to determine the accuracy and reliability of the cutoff value, we selected eight protein ligand systems and used the GFN2-xTB method to calculate the binding free energy at different cutoff distances, and evaluated the dependence of the predicted binding free energy on the cutoff value. like image 3 As shown, it is found that the calculated binding free energy tends to plateau as the cut-off distance increases. When the cut-off distance is g...

Embodiment 2

[0041] In this example, cluster models with different cutoff values ​​combined with the GFN2-xTB method were used to calculate the binding free energies of 25 groups of protein-ligand complexes according to the above process, and further determine the cutoff range of the cluster model.

[0042] like Figure 4 As shown in a, we find that when the cutoff distance is or When using the GFN2-xTB method to calculate the protein-ligand binding free energy MAE is 10.4kcal / mol and 6.1kcal / mol, the errors of these two methods are comparable to the MM / PBSA (MM / GBSA) error mentioned above , and it also has the problem of not truncating residues that interact with the ligand, so this cutoff value is not suitable for cluster models. And when the cutoff value is When using GFN2-xTB method to calculate the MAE value of protein-ligand binding free energy, most of them are less than 10.0kcal / mol, and the average MAE value is about 5.0kcal / mol, which is close to the FEP method and has rela...

Embodiment 3

[0044] Taking the protein-ligand complex 1AU0 as an example, the binding free energy is calculated according to the method of the present invention, and compared with the experimental binding free energy calculated from the information published on the website.

[0045] (1) First select the protein-ligand complex 1AU0 from the PDB-BIND website. The basic information of the complex has been given on the page, including the protein name, corresponding ligand (SDK) and pK d value (7.66), etc. We can pass pK d =-logK d and ΔG=RTlnK d Find the corresponding experimental binding free energy value.

[0046] (2) After obtaining the relevant information of 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 structure from the PDB file. If you don’t do any processing and directly use PyMOL to hydrogenate and saturate, there will be 3329 atoms. Using GFN2-xTB to directly ...

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Abstract

The invention discloses a method for calculating protein-ligand binding free energy based on a cluster model, compared with other methods for calculating protein-ligand binding free energy, a GFN2-xTB method is easier to use, an input structure can be formed by only needing initial coordinates and element composition, and when the GFN2-xTB method is combined with the cluster model for application, the input structure is more stable. On the premise of ensuring relatively good accuracy, the time cost of calculation is reduced more effectively. The new method based on the cluster model and the GFN2-xTB should have great potential in calculation of combined free energy related to biomacromolecules in the future.

Description

technical field [0001] The invention relates to a method for calculating protein-ligand binding free energy based on a cluster model, which belongs to the technical field of computational biology. Background technique [0002] Protein-ligand interactions play an important role in many biochemical processes and biomedical applications (eg, immune response, signal transduction, drug design). The study of the affinity (binding free energy) between proteins and ligands not only helps to understand the biological functions of proteins, but also has very important significance for drug development and drug mechanism research. For example, the major protease and spike protein of the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus causing the COVID-19 pandemic could serve as a good drug target. Accurate calculation of the binding free energy between SARS-CoV-2 protease or spike protein and the corresponding ligand is expected to screen out high-affinity l...

Claims

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Application Information

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IPC IPC(8): G16B15/20G16B50/00
CPCG16B15/20G16B50/00
Inventor 丁泓铭陈远强
Owner SUZHOU UNIV
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