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. 2020 Jul 1;36(Suppl_1):i285-i291.
doi: 10.1093/bioinformatics/btaa455.

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

Md Hossain Shuvo  1 , Sutanu Bhattacharya  1 , Debswapna Bhattacharya  1   2
Affiliations

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

Md Hossain Shuvo et al. Bioinformatics. .

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.

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Figures

Fig. 1.
Fig. 1.
Flowchart of QDeep. (A) Multiple sequence alignment generation. (B) Distance-based, sequence versus structure consistency-based and ROSETTA centroid energy terms-based features collection. (C) Architecture of stacked deep ResNet classifiers at 1, 2, 4, and 8Å error thresholds. (D) Residue-level ensemble error classifications and their combination for model quality estimation
Fig. 2.
Fig. 2.
Accuracy of the individual residue-level classifiers at 1, 2, 4, and 8Å error thresholds on the validation set of 82 CASP11 targets
Fig. 3.
Fig. 3.
The ability of single-model quality estimation methods to distinguish good and bad models in (A) CASP12 and (B) CASP13 stage 2 datasets. A cutoff of GDT-TS = 0.4 is used to separate good and bad models
See this image and copyright information in PMC

References

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