title: DeepQA: improving the estimation of single protein model quality with deep belief networks.
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.
  
  
  CONCLUSIONS
  DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .
authors.affiliation: Department of Computer Science, Pacific Lutheran University, Tacoma, WA, 98447, USA.
authors.forename: Renzhi
authors.initials: R
authors.lastname: Cao
authors.affiliation: Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS, 67260, USA.
authors.forename: Debswapna
authors.initials: D
authors.lastname: Bhattacharya
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Hou
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO, 65211, USA. chengji@missouri.edu.|Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. chengji@missouri.edu.
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D000068598:Data Accuracy; D000069550:Machine Learning; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D011506:Proteins
keywords: Deep belief network; Machine learning; Protein model quality assessment; Protein structure prediction