structure aware protein interaction site prediction based on depth map convolution network

This paper is published in… By Zhou Yaoqi research group of Shenzhen Graduate School of Peking University and Yang Yuedong research group of Sun Yat sen University Bioinformatics Articles on .

Address of thesis :


protein – Protein interactions (PPI) It plays a vital role in many biological processes , distinguish PPI Site is an important step in understanding disease mechanism and designing new drugs . because PPI The experimental method of site recognition is expensive and time-consuming , Many computational methods have been developed as screening tools . However , Most of these methods are based on the adjacent features in the sequence, and are limited to obtaining spatial information .

The author proposes a method for PPI A depth map based framework for site prediction GraphPPIS( Depth map convolution network for protein interaction site prediction ), among PPI The locus prediction problem is transformed into a graph node classification task , And by using the initial residual (initial residual) Identity mapping (identity mapping) Deep learning of technology to solve . The author shows , Compared with other sequence based and structure based methods , stay AUPRC and MCC On , Deeper Architecture ( As many as 8 layer ) The performance can be improved respectively 12.5% and 10.5% above . Further analysis shows that , Even if a false positive prediction is made ,GraphPPIS The predicted interaction sites are more spatially clustered , Closer to the native site . The results highlight the importance of capturing spatially adjacent residues for the prediction of interaction sites .

GraphPPIS Network framework of model

Read more here: Source link