Introduction

Membrane proteins are the most important drug target accounting for around 30% proteins in genomes of living organisms. One of the important roles of this protein type is to bind with adenosine triphosphate (ATP). This facilitates some crucial biological processes such as metabolism and cell signalling. Many researchers have paid significant attention to elucidating the localization of ATP-binding sites with much progress. However, such researchs on membrane proteins were limited. We are one of the pioneers in using deep learning approach for identifying ATP-binding sites. Our predictor, DeepATP, conbined evolutionary information in the form of Position-Specific Scoring Matrix and 2D Convolutional Neural Network to predict these interacting sites in membrane proteins. Random over-sampling was used to solved the imbalanced data learning problem.

On the independent test, DeepATP can obtain a MCC of 0.79 and an AUC of 93%. Compared to some existing sequence-based predictors and some traditional machine learning algorithms, our approach can improve the performance significantly. We suggest this method as a reliable tools for biologists in predicting these binding site in membrane proteins.