QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks
Md Hossain Shuvo, Sutanu Bhattacharya, Debswapna Bhattacharya
Abstract
MOTIVATION: Protein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction. RESULTS: We present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently outperforms existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep. AVAILABILITY AND IMPLEMENTATION: https://github.com/Bhattacharya-Lab/QDeep. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.