Detecting distant-homology protein structures by aligning deep neural-network based contact maps.

ABSTRACT: Accurate prediction of atomic-level protein structure is important for annotating the biological functions of protein molecules and for designing new compounds to regulate the functions. Template-based modeling (TBM), which aims to construct structural models by copying and refining the structural frameworks of other known proteins, remains the most accurate method for protein structure prediction. Due to the difficulty in recognizing distant-homology templates, however, the accuracy of TBM decreases rapidly when the evolutionary relationship between the query and template vanishes. In this study, we propose a new method, CEthreader, which first predicts residue-residue contacts by coupling evolutionary precision matrices with deep residual convolutional neural-networks. The predicted cont

SUBMITTER: Zheng W 

PROVIDER: S-EPMC6818797 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature