Protein Structure Analysis Model Based On Graph Attention Network

Protein is the basic component of tissues and cells in living organisms on Earth,and plays an important role in metabolic,movement,defense,cell communication,molecular recognition and many other biochemical reactions.Proteins consist of twenty amino acids,each with a unique side chain and different chemical properties.The interactions between amino acids drive protein folding and internal molecular bonding.These interactions ultimately make the protein form a unique three-dimensional shape.Different amino acids have their corresponding amino acid microenvironment.The amino acid microenvironment is determined by its 3D position and local neighborhood with related structure or function.Understanding the amino acid environment affinity is essential for additional protein structural studies and protein engineering.In recent years,with the rapid development of deep learning,researchers have applied deep learning method to study the relationship between amino acids and the microenvironment.Convolution Neural Network is one of the mainstream methods,but it can only deal with problems represented by Euclidean data.However,many practical applications are non-Euclidean problems,such as social networks,protein-protein interaction networks and knowledge graphs.Graph Neural Network(GNN)is a new type of deep learning method that directly operates on the graph structure.To tackle the challenges faced by graph learning methods,researchers applied the attention mechanism to Graph Neural Network to improve its performance.Since the amino acid microenvironment is highly dependent on the neighborhood around its threedimensional position,the attention mechanism is very suitable for amino acid environmental analysis.It can be used to focus on related amino acids while ignoring the others.This paper proposes a protein structure analysis framework based on Graph Attention Network.On this basis,for the two tasks of amino acid microenvironment analysis and protein structure evaluation,an amino acid microenvironment model based on graph attention and a protein structure evaluation model based on graph attention were established respectively.The former is based on the analysis of protein structure,and predicts the type of amino acid at a specific location in the protein structure through the microenvironment at that location.The latter is to use the model to evaluate the quality of the predicted protein structure,which is divided into global scoring evaluation and local scoring evaluation.The purpose of the amino acid microenvironment model based on graph attention is to study how amino acids interact with their surrounding environment and predict which amino acids are most compatible in a specific environment.The input of the model is a graph representing the protein,and each amino acid in the protein is represented as a node in the graph.If the distance between two ?-carbon atoms of two amino acids is less than 9(?),a connection is set between these two nodes.For each node,extract its feature set from protein sequence and structure information,and exclude features related to amino acid type.The experimental results show that our proposed method greatly improves the prediction accuracy compared with existing methods.A protein structure evaluation model based on graph attention is used to perform the task of protein structure evaluation.The method of constructing and extracting the feature of the protein graph is the same as before.The homologous protein structure in Rosetta-300 k and the predicted protein structure in the CASP13 competition were used to train the models respectively.The experimental results show that the global score and the local score are improved compared with the existing methods,and there is a strong correlation between the predicted score and the real score,which can provide guidance information for protein modeling.This paper proposes a protein structure analysis framework based on graph attention network.Two models are established for the two tasks of amino acid microenvironment analysis and protein structure evaluation,and a series of experiments are carried out.Compared with the original method,our method has improved the accuracy and can correctly capture the local score trend of protein structure,indicating that our method has a certain significance in protein structure evaluation.