A Membrane Burial Potential with H-Bonds and Applications to Curved Membranes and Fast Simulations

Zongan Wang, John M. Jumper, Sheng Wang, Karl F. Freed, Tobin R. Sosnick

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

We use the statistics of a large and curated training set of transmembrane helical proteins to develop a knowledge-based potential that accounts for the dependence on both the depth of burial of the protein in the membrane and the degree of side-chain exposure. Additionally, the statistical potential includes depth-dependent energies for unsatisfied backbone hydrogen bond donors and acceptors, which are found to be relatively small, ∼2 RT. Our potential accurately places known proteins within the bilayer. The potential is applied to the mechanosensing MscL channel in membranes of varying thickness and curvature, as well as to the prediction of protein structure. The potential is incorporated into our new Upside molecular dynamics algorithm. Notably, we account for the exchange of protein-lipid interactions for protein-protein interactions as helices contact each other, thereby avoiding overestimating the energetics of helix association within the membrane. Simulations of most multimeric complexes find that isolated monomers and the oligomers retain the same orientation in the membrane, suggesting that the assembly of prepositioned monomers presents a viable mechanism of oligomerization.
Original languageEnglish (US)
Pages (from-to)1872-1884
Number of pages13
JournalBiophysical Journal
Volume115
Issue number10
DOIs
StatePublished - Oct 23 2018

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