Protein and nucleic acid binding site prediction method based on graph neural network characterization

Abstract

A protein and nucleic acid binding site prediction method based on graph neural network characterization comprises the following steps: constructing a protein and nucleic acid interaction data set, extracting the position and feature information of each residue in the protein and the structural context thereof after sample fusion processing, and constructing graph representation of the structural context of the residues according to the graph representation. And predicting the graph representation of the to-be-predicted protein through the hierarchical graph neural network to obtain the probability that each residue is combined with DNA/RNA, thereby realizing prediction of the binding site of the protein and the nucleic acid. Key structures and feature patterns of binding sites are learned from graph representations through graph representations based on residues of structural context and a hierarchical graph neural network model.

Description

technical field [0001] The present invention relates to a technology in the field of bioengineering, in particular to a method for predicting binding sites between proteins and nucleic acids based on graph neural network representations of protein local structure contexts. Background technique [0002] Protein-nucleic acid interactions play an important role in a variety of life activities, such as DNA replication, transcription, translation, gene expression, signal transduction and recognition, etc. Learning protein-nucleic acid interactions is important for analyzing genes, protein functions and drugs There are significant implications in terms of design. Due to the disadvantages of expensive and time-consuming analysis of protein-nucleic acid interactions by experimental methods, which cannot meet the current needs of massive protein analysis, computational-based methods have become more and more important. Current computational-based methods can be divided into protein ...

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Problems solved by technology

[0003] Aiming at the problem that the existing recognition accuracy of protein-nucleic acid binding sites is not high, the present invention proposes a protein-nucleic acid binding site prediction method based on graph neural network representation, through the graph representation and hierarchical graph of residues based on structural context A neural network model to learn key structures and characteristic patterns of binding sites from graph representations

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