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Protein-ligand affinity predicting method based on molecule descriptors

A molecular descriptor and affinity technology, applied in the field of protein-ligand affinity prediction based on molecular descriptors, can solve the problems of large target dependence, poor sensitivity of homologues, poor correlation, etc., and achieve strong predictive ability Effect

Inactive Publication Date: 2013-02-13
SICHUAN UNIV
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Problems solved by technology

This method overcomes the disadvantages of poor correlation between predicted value and experimental value, large target dependence and poor sensitivity to homologues in the prior art.

Method used

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  • Protein-ligand affinity predicting method based on molecule descriptors
  • Protein-ligand affinity predicting method based on molecule descriptors
  • Protein-ligand affinity predicting method based on molecule descriptors

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Embodiment Construction

[0037] The method of the present invention belongs to the scoring method based on the empirical scoring function. By collecting 2278 diversified crystal structures of protein and ligand complexes and their binding affinity experimental values, 50 perfect and systematic molecules related to the interaction of proteins and ligands are constructed. Descriptors are used to reflect the affinity of the compound, and the relationship between the descriptor and the affinity of the compound is established by the method of support vector regression, so as to construct an empirical scoring function for predicting the affinity of a given compound.

[0038] Specific steps are as follows:

[0039] (1) Preparation of training set:

[0040] In total, the training set contains 2278 complex structures and their affinity data. The protein structure and the ligand structure in each complex are named with the PDB ID number and saved in the same folder. The protein structure is saved in the PDB fo...

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Abstract

Disclosed is a protein-ligand affinity predicting method based on molecule descriptors. The protein-ligand affinity is reflected through construction of perfect and systematic molecule descriptors, and the relation between the descriptors and the affinity is constructed through a supporting vector regression (SVR) mode. The method includes the steps of training set preparation: preparing a large amount of data containing the crystal structure and the affinity of a protein-ligand complex; construction and calculation of the molecule descriptors: constructing 50 kinds of molecule descriptors which belong to nine categories, and calculating concrete values of all the complex descriptors in the training set; regression model construction: fitting the relation between the descriptors and the affinity through the SVR mode, and introducing a conjugate gradient method to optimize a penalty factor C and a kernel function parameter; and novel scoring function building which is used for predicting the affinity of the complex. The method has the advantages of being high in prediction capacity, small in target dependence, high in homolog sensitivity and the like.

Description

1. Technical field [0001] The invention relates to the field of computer-aided drug molecule design, in particular to a method for predicting protein-ligand affinity based on molecular descriptors. 2. Background technology [0002] In structure-based drug design, such as molecular docking and de novo design, predicting the binding affinity between a protein target and its ligand is usually scored by a scoring function. So far, the scoring functions can be roughly divided into three categories: force field-based, knowledge-based and experience-based, and the scoring function based on experience is the most popular. Empirical scoring functions are usually composed of a variety of physicochemical terms related to protein-ligand interactions, such as van der Waals forces, hydrogen bonds, electrostatics, and metal-ligand bond energies. The coefficients of these terms are often fitted by multiple linear regression. It can be seen that the scoring function based on experience is ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/16
Inventor 杨胜勇李国菠李琳丽杨羚羚魏于全
Owner SICHUAN UNIV
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