Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers.

ABSTRACT:

Background

Prediction of long-range inter-residue contacts is an important topic in bioinformatics research. It is helpful for determining protein structures, understanding protein foldings, and therefore advancing the annotation of protein functions.

Results

In this paper, we propose a novel ensemble of genetic algorithm classifiers (GaCs) to address the long-range contact prediction problem. Our method is based on the key idea called sequence profile centers (SPCs). Each SPC is the average sequence profiles of residue pairs belonging to the same contact class or non-contact class. GaCs train on multiple but different pairs of long-range contact data (positive data) and long-range non-contact data (negative data). The negative data sets, having roughly the same sizes as the

SUBMITTER: Chen P 

PROVIDER: S-EPMC2873825 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature