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

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    1-s2.0-S0006349518311597-main.pdf
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    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Wang, Zongan
    Jumper, John M.
    Wang, Sheng
    Freed, Karl F.
    Sosnick, Tobin R.
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computational Bioscience Research Center (CBRC)
    Date
    2018-10-23
    Online Publication Date
    2018-10-23
    Print Publication Date
    2018-11
    Embargo End Date
    2019-11-20
    Permanent link to this record
    http://hdl.handle.net/10754/630595
    
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    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.
    Citation
    Wang Z, Jumper JM, Wang S, Freed KF, Sosnick TR (2018) A Membrane Burial Potential with H-Bonds and Applications to Curved Membranes and Fast Simulations. Biophysical Journal 115: 1872–1884. Available: http://dx.doi.org/10.1016/j.bpj.2018.10.012.
    Sponsors
    We thank members in our group for helpful discussions. This work is supported by The National Institute of General Medical Sciences Grants GM055694 (to T.R.S. and K.F.F.), GM087519 (to E. Perozo), and T32GM008720 (to J. Piccirilli). Computations are produced using the Midway resources of the Research Computing Center (RCC) at the University of Chicago. A web server is available to insert helical transmembrane protein models into membranes and run Upside simulations of the inserted model as rigid body to generate an ensemble of orientations: http://sosnick.uchicago.edu/serverlinks.html.
    Publisher
    Elsevier BV
    Journal
    Biophysical Journal
    DOI
    10.1016/j.bpj.2018.10.012
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0006349518311597
    https://doi.org/10.1016/j.bpj.2018.10.012
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.bpj.2018.10.012
    Scopus Count
    Collections
    Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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