A Novel Prediction Method for ATP-Binding Sites From Protein Primary Sequences Based on Fusion of Deep Convolutional Neural Network and Ensemble Learning

Abstract

Accurately identifying protein-ATP (Adenosine-5'-triphosphate) binding sites is significant for protein function annotation and new drug invention. Previous studies often utilize classical machine learning classification algorithms to predict protein-ATP binding sites based on protein primary sequence. However, deep learning as a newly developed technique has shown outstanding performance in various fields. In this work, we introduce the deep convolutional neural network for protein-ATP binding sites prediction based on sequence information. Two classification networks are developed including a residual-inception-based predictor and a multi-inception-based predictor, then the ensemble learning is applied by giving optimized weights to each network architecture to combine them for more superior performance. We examine the performance of our proposed method on two groups of independent testing sets including a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. As a result, our proposed method outperforms other state-of-art sequence-based predictors with the AUC of 0.922 and 0.896 respectively which illustrates the efficacy of deep learning technique in protein-ATP binding sites prediction. The source code and benchmark datasets can be downloaded at https://github.com/tlsjz/ATPbinding.

Keywords

• Protein-ATP binding sites prediction
• deep convolutional neural network
• ensemble learning
• inception neural network
• protein primary sequence