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

Julia Koehler Leman,Sergey Lyskov,Richard Bonneau

doi:10.1186/s12859-017-1541-z

Julia Koehler Leman, Sergey Lyskov + Show 1 more

Open Access

https://doi.org/10.1186/s12859-017-1541-z

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Abstract

BackgroundMembrane 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.ResultsHere 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.ConclusionsWe 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.

Highlights

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
Our algorithm uses protein structures transformed into membrane coordinates and a fixed membrane thickness and is applicable to a wide range of protein architectures
Classification is achieved through a 2D concave hull algorithm applied to the point cloud of membrane embedded Cα atoms projected onto the membrane plane. mp_lipid_acc creates a Protein Data Bank (PDB) structure file with modified B-factors that can be visualized by a provided PyMOL script

Summary

Introduction

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. 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. One important characteristic of membrane proteins is accessibility to the Prediction of lipid accessibility is not trivial: the protein ‘interior’ can either be water accessible, in the case of pores, or buried hydrophobic residues. For the latter, the hydrophobicity profile of buried residues is similar to lipid-facing residues, hydrophobicity as a solitary feature would be insufficient for classification. A combination of different features is required for accurate prediction of lipid accessible residues

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