基于协同进化信息和深度学习的蛋白质功能预测.

The function of protein is crucial for understanding the mechanisms of cellular and biological activities, as well as for studying the mechanisms of diseases. Traditional experimental and sequence alignment methods are insufficient to support large-scale protein functional annotation when in the face of the rapid growth of sequence databases. For this situation, this paper proposed EGnet（Evolutionary Graph Network） model, which utilized the protein pre-training language model ESM2 and One-hot encoding to obtain the protein sequence encoding. The model integrated the coevolutionary information between residues, including PI（Paired Interaction） and SPI（Strong Paired Interaction）, through sequence self-attention and physical calculations. Subsequently, the two types of coevolutionary information and the sequence encoding used in inputs for a multi-layered cascaded graph convolutional network to learn the node features of the sequence encoding and achieve end-to-end protein function prediction. Compared with earlier methods, EGnet achieved better performance on the EC（Enzyme Commission） category labels in the ENZYME database, which reached 0.89 in the F-score and 0.91 in the AUPR. The results indicate that EGnet can achieve good performance by using only a single sequence to predict protein function, providing a rapid and effective method for protein function annotation. [ABSTRACT FROM AUTHOR]