NeBcon: protein contact map prediction using neural network training coupled with naive Bayes classifiers.

ABSTRACT:

Motivation

Recent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact prediction methods for the high volume of sequence homologs that are not available to most of the non-humongous protein targets. Development of efficient methods that can generate balanced and reliable contact maps for different type of protein targets is essential to enhance the success rate of the ab initio protein structure prediction.

Results

We developed a new pipeline, NeBcon, which uses the naïve Bayes classifier (NBC) theorem to combine eight state of the art contact methods that are built from co-evolution and machine learn

SUBMITTER: He B 

PROVIDER: S-EPMC5860114 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature