  Protein quality assessment (QA) by 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.
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 experiment
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. DeepQA is a useful 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/.
