Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane)
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 2689815
Author(s) Studer, Gabriel; Biasini, Marco; Schwede, Torsten
Author(s) at UniBasel Studer, Gabriel
Biasini, Marco
Schwede, Torsten
Year 2014
Title Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane)
Journal Bioinformatics
Volume 30
Number 17
Pages / Article-Number i505-11
Abstract

Motivation: Membrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and >50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited.

Results: We developed a local model quality estimation method for membrane proteins (‘QMEANBrane’) by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules.

Availability and implementation: Source code and datasets are available on request.

Publisher Oxford University Press
ISSN/ISBN 1367-4803
URL http://bioinformatics.oxfordjournals.org/content/30/17/i505.abstract
edoc-URL http://edoc.unibas.ch/dok/A6289229
Full Text on edoc No
Digital Object Identifier DOI 10.1093/bioinformatics/btu457
PubMed ID http://www.ncbi.nlm.nih.gov/pubmed/25161240
ISI-Number WOS:000342912400023
Document type (ISI) Journal Article
 
   

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