QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

Shuvo, M. H.; Bhattacharya, S.; Bhattacharya, D.

doi:10.1101/2020.01.31.928622

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bioRxiv · 10.1101/2020.01.31.928622

QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

Shuvo, M. H.·Bhattacharya, S.·Bhattacharya, D.

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Abstract

MotivationProtein 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. ResultsWe 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. Availabilityhttps://github.com/Bhattacharya-Lab/QDeep Contactbhattacharyad@auburn.edu

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Shuvo, M. H., Bhattacharya, S., Bhattacharya, D.. 2020-02-02. QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks. https://doi.org/10.1101/2020.01.31.928622

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