PT  - JOURNAL ARTICLE
AU  - Md Hossain Shuvo
AU  - Sutanu Bhattacharya
AU  - Debswapna Bhattacharya
TI  - QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks
AID  - 10.1101/2020.01.31.928622
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.01.31.928622
4099  - http://biorxiv.org/content/early/2020/02/02/2020.01.31.928622.short
4100  - http://biorxiv.org/content/early/2020/02/02/2020.01.31.928622.full
AB  - 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 out-performs 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 https://github.com/Bhattacharya-Lab/QDeepContact bhattacharyad{at}auburn.eduSupplementary information Supplementary data are available at Bioinformatics online.