ATPensemble

Accurately identifying protein-ATP binding residues is significant for protein function annotation and new drug design. Previous studies often use classic machine learning algorithms like SVMs and random forests for protein-ATP binding residues prediction, however, as some new machine learning techniques are developed, the prediction performance could be further improved. In this study, an ensemble predictor which combines deep convolutional neural network and LightGBM with ensemble learning algorithm is proposed. Three sub-classifiers are developed including a multiincepresnet-based predictor, a multixception-based predictor and a LightGBM predictor. The final prediction result is the combination of outputs from three sub-classifiers with optimized weight distribution. We examine the performance of our proposed predictor on two datasets: a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. Our predictor can achieve AUCs of 0.925 and 0.902, MCCs of 0.639 and 0.642 respectively which are both better than other state-of-art prediction methods. The full source code and benchmark datasets can be freely available at https://github.com/tlsjz/ATPensemble.