title: Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP.
abstract: BACKGROUND
  Membrane proteins are underrepresented in structural databases, which has led to a lack of computational tools and the corresponding inappropriate use of tools designed for soluble proteins. For membrane proteins, lipid accessibility is an essential property. Although programs are available for sequence-based prediction of lipid accessibility and structure-based identification of solvent-accessible surface area, the latter does not distinguish between water accessible and lipid accessible residues in membrane proteins.
  
  
  RESULTS
  Here we present mp_lipid_acc, the first method to identify lipid accessible residues from the protein structure, implemented in the RosettaMP framework and available as a webserver. Our method uses protein structures transformed in membrane coordinates, for instance from PDBTM or OPM databases, and a defined membrane thickness to classify lipid accessibility of residues. mp_lipid_acc is applicable to both α-helical and β-barrel membrane proteins of diverse architectures with or without water-filled pores and uses a concave hull algorithm for surface-residue classification. We further provide a manually curated benchmark dataset that can be used for further method development.
  
  
  CONCLUSIONS
  We present a novel tool to classify lipid accessibility from the protein structure, which is applicable to proteins of diverse architectures and achieves prediction accuracies of 90% on a manually curated database. mp_lipid_acc is part of the Rosetta software suite, available at www.rosettacommons.org . The webserver is available at http://rosie.graylab.jhu.edu/mp_lipid_acc/submit and the benchmark dataset is available at http://tinyurl.com/mp-lipid-acc-dataset .
authors.affiliation: Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY, 10010, USA. julia.koehler.leman@gmail.com.|Departments of Biology and Computer Science, Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA. julia.koehler.leman@gmail.com.
authors.forename: Julia
authors.initials: J
authors.lastname: Koehler Leman
authors.affiliation: Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
authors.forename: Sergey
authors.initials: S
authors.lastname: Lyskov
authors.affiliation: Center for Computational Biology, Flatiron Institute, 162 Fifth Avenue, New York, NY, 10010, USA.|Departments of Biology and Computer Science, Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: Bonneau
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008055:Lipids / Q000737:chemistry*; D008565:Membrane Proteins / Q000737:chemistry / Q000378:metabolism; D017433:Protein Structure, Secondary; D012984:Software; D012997:Solvents
chemical_list: D008055:Lipids; D008565:Membrane Proteins; D012997:Solvents
keywords: Accessible surface area; Lipid accessibility; Membrane proteins; Rosetta software; Structure prediction