New Deep Neural Networks for Protein Model Evaluation

Computational protein structure prediction is one of the most important problems in bioinformatics. In the process of protein three-dimensional structure prediction, assessing the quality of generated models accurately is crucial. In recent years, deep neural networks have obtained good results for various structure prediction related problems. In this paper, two new deep neural network structures are developed, trained and tested for single-model quality assessment. These two networks are based on a comprehensive set of model structure features, such as predicted and real secondary structure and solvent accessibility, distance matrix and φ, ψ angle. For each model, using these features as inputs, trained deep neural networks are able to predict a quality score in the range of [0, 1]. Experimental results show that the deep neural networks obtained excellent results, comparable with existing state of art single-model QA methods on CASP 12 dataset.