Abstract
BackgroundRecent increases in the number of deposited membrane protein crystal structures necessitate the use of automated computational tools to position them within the lipid bilayer. Identifying the correct orientation allows us to study the complex relationship between sequence, structure and the lipid environment, which is otherwise challenging to investigate using experimental techniques due to the difficulty in crystallising membrane proteins embedded within intact membranes.ResultsWe have developed a knowledge-based membrane potential, calculated by the statistical analysis of transmembrane protein structures, coupled with a combination of genetic and direct search algorithms, and demonstrate its use in positioning proteins in membranes, refinement of membrane protein models and in decoy discrimination.ConclusionsOur method is able to quickly and accurately orientate both alpha-helical and beta-barrel membrane proteins within the lipid bilayer, showing closer agreement with experimentally determined values than existing approaches. We also demonstrate both consistent and significant refinement of membrane protein models and the effective discrimination between native and decoy structures. Source code is available under an open source license from http://bioinf.cs.ucl.ac.uk/downloads/memembed/.
Highlights
Recent increases in the number of deposited membrane protein crystal structures necessitate the use of automated computational tools to position them within the lipid bilayer
Background transmembrane proteins are encoded by approximately 30% of a typical genome and play vital roles in a diverse range of essential biological processes, they constitute only about 2% of structures deposited into the Protein Data Bank (PDB) [1,2]
This paucity of structures has meant that the majority of computational tools developed to analyse transmembrane proteins have focused on topology prediction [3,4,5,6,7] and de novo structure prediction [8,9,10,11,12,13,14]
Summary
Recent increases in the number of deposited membrane protein crystal structures necessitate the use of automated computational tools to position them within the lipid bilayer. Transmembrane proteins are encoded by approximately 30% of a typical genome and play vital roles in a diverse range of essential biological processes, they constitute only about 2% of structures deposited into the Protein Data Bank (PDB) [1,2] This paucity of structures has meant that the majority of computational tools developed to analyse transmembrane proteins have focused on topology prediction [3,4,5,6,7] and de novo structure prediction [8,9,10,11,12,13,14]. Transmembrane proteins differ from globular proteins in that they are embedded in the anisotropic environment of the lipid bilayer, composed of a heterogeneous mixture of lipid types with a central hydrocarbon core and a steep polarity gradient Their positioning within the membrane is crucial to their folding, stability and activity yet the difficulties associated with crystallising transmembrane proteins in intact membranes mean that experimental orientation data is extremely scarce. Results are comparable to OPM and enable the generation of complete pseudo-energy topological landscapes that underscores positional stability, the method is slower with a computation time of approximately 1 second per 5 residues
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