Protein threading using residue co-variation and deep learning.

ABSTRACT:

Motivation

Template-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain very challenging.

Results

We present a new method called DeepThreader to improve protein threading, including both alignment generation and template selection, by making use of deep learning (DL) and residue co-variation information. Our method first employs DL to predict inter-residue distance distribution from residue co-variation and sequential information (e.g. sequence profile and predicted secondary structure), and then builds sequence-template alignment by integrating predicted distance information and sequential features

SUBMITTER: Zhu J 

PROVIDER: S-EPMC6022550 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature