Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP

Julia Koehler Leman, Sergey Lyskov, Richard Bonneau

Research output: Contribution to journalArticlepeer-review


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 The webserver is available at the benchmark dataset is available at

Original languageEnglish (US)
Article number115
JournalBMC bioinformatics
Issue number1
StatePublished - Feb 20 2017


  • Accessible surface area
  • Lipid accessibility
  • Membrane proteins
  • Rosetta software
  • Structure prediction

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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