DeepQA: improving the estimation of single protein model quality with deep belief networks

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Issue Date
2016-12-05
Authors
Cao, Renzhi
Bhattacharya, Debswapna
Hou, Jie
Cheng, Jianlin
Advisor
Citation

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin. 2016. DeepQA: improving the estimation of single protein model quality with deep belief networks. BMC Bioinformatics, vol. 17:495

Abstract

Background: Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem.

Results: We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods.

Conclusion: DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction.

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© The Author(s). 2016. This is an open access article, which may be freely copied and distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..
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