Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning.

ABSTRACT:

Motivation

Protein contact prediction is important for protein structure and functional study. Both evolutionary coupling (EC) analysis and supervised machine learning methods have been developed, making use of different information sources. However, contact prediction is still challenging especially for proteins without a large number of sequence homologs.

Results

This article presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning to improve accuracy on proteins without many sequence homologs. Different from existing single-family EC analysis that uses residue coevolution information in only the target protein family, our joint EC analysis uses residue coevolution in both the target family

SUBMITTER: Ma J 

PROVIDER: S-EPMC4838177 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature