Ontology highlight
ABSTRACT: Motivation
Residue-residue contacts across the transmembrane helices dictate the three-dimensional topology of alpha-helical membrane proteins. However, contact determination through experiments is difficult because most transmembrane proteins are hard to crystallize.Results
We present a novel method (MemBrain) to derive transmembrane inter-helix contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers. Tested on 60 non-redundant polytopic proteins using a strict leave-one-out cross-validation protocol, MemBrain achieves an average accuracy of 62%, which is 12.5% higher than the current best method from the literature. When applied to 13 recently solved G protein-coupled receptors, the MemBrain contact predictions helped increase the TM-score of the I-TASSER models by 37% in the transmembrane region. The number of foldable cases (TM-score >0.5) increased by 100%, where all G protein-coupled receptor templates and homologous templates with sequence identity >30% were excluded. These results demonstrate significant progress in contact prediction and a potential for contact-driven structure modeling of transmembrane proteins.Availability
www.csbio.sjtu.edu.cn/bioinf/MemBrain/
SUBMITTER: Yang J
PROVIDER: S-EPMC3789543 | biostudies-literature | 2013 Oct
REPOSITORIES: biostudies-literature
Bioinformatics (Oxford, England) 20130814 20
<h4>Motivation</h4>Residue-residue contacts across the transmembrane helices dictate the three-dimensional topology of alpha-helical membrane proteins. However, contact determination through experiments is difficult because most transmembrane proteins are hard to crystallize.<h4>Results</h4>We present a novel method (MemBrain) to derive transmembrane inter-helix contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers. Tested on 60 non-redund ...[more]