Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets.

Zengyan Xie,Kunxian Shu,Xiaoya Deng

doi:10.3390/ijms21020467

Abstract

Protein–protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.

Highlights

Proteins play key roles in various aspects of life [1] by physically interacting with other proteins [2,3]
In order to demonstrate the distribution tendency of residues, we first compared the abundance of residues (AR) between the protein surface (ARs) and whole protein (ARw) and used ARw/ARs as the indicator of the tendency of a residue to be inside of proteins (Table 1, Section 4.3)
Charged and hydrophilic residues tend to appear on the protein surface with the exception of histidine, which shows no tendency towards protein inside or surface

Summary

Introduction

Proteins play key roles in various aspects of life [1] by physically interacting with other proteins [2,3]. Protein-binding interfaces are heterogeneous and some interface residues contribute more to binding than the others. These residues are called “hotspots” [6,7,8,9,10]. Hotspots are often pre-organized in the unbound protein state. It is suggested that much of the protein surface does not accommodate binding and the potential binding sites of a protein are already imprinted in its unbound state [8]

Methods

Results

Conclusion