DL-PRO: A Novel Deep Learning Method for Protein Model Quality Assessment.
Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not high enough for most real applications. In this paper, a new approach based on C-α atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues' C-α atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW.
Department of Computer Science, University of Missouri, Columbia, MO 65211 USA.
Son P
SP
Nguyen
Department of Computer Science, University of Missouri, Columbia, MO 65211 USA.
Yi
Y
Shang
Department of Computer Science, University of Missouri, Columbia, MO 65211 USA. Christopher S. Bond Life Science Center, University of Missouri at Columbia.
Dong
D
Xu
Critical Assessment of Structure Prediction (CASP); classification; deep learning; energy and scoring function; protein model quality assessment; stacked autoencoder