title: Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane).
abstract: BACKGROUND
  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.
  
  
  METHODS
  Source code and datasets are available on request.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Biozentrum, University of Basel, Basel, 4056, Switzerland and SIB Swiss Institute of Bioinformatics, Basel, 4056, Switzerland Biozentrum, University of Basel, Basel, 4056, Switzerland and SIB Swiss Institute of Bioinformatics, Basel, 4056, Switzerland.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Studer
authors.affiliation: Biozentrum, University of Basel, Basel, 4056, Switzerland and SIB Swiss Institute of Bioinformatics, Basel, 4056, Switzerland Biozentrum, University of Basel, Basel, 4056, Switzerland and SIB Swiss Institute of Bioinformatics, Basel, 4056, Switzerland.
authors.forename: Marco
authors.initials: M
authors.lastname: Biasini
authors.affiliation: Biozentrum, University of Basel, Basel, 4056, Switzerland and SIB Swiss Institute of Bioinformatics, Basel, 4056, Switzerland Biozentrum, University of Basel, Basel, 4056, Switzerland and SIB Swiss Institute of Bioinformatics, Basel, 4056, Switzerland.
authors.forename: Torsten
authors.initials: T
authors.lastname: Schwede
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry*; D008958:Models, Molecular
chemical_list: D008565:Membrane Proteins