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The Journal of Physical Chemistry B
Article . 2008 . Peer-reviewed
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Improved Sampling for Simulations of Interfacial Membrane Proteins: Application of Generalized Shadow Hybrid Monte Carlo to a Peptide Toxin/Bilayer System

Authors: Wee, C; Sansom, MS; Reich, S; Akhmatskaya, E;

Improved Sampling for Simulations of Interfacial Membrane Proteins: Application of Generalized Shadow Hybrid Monte Carlo to a Peptide Toxin/Bilayer System

Abstract

The computational costs associated with performing molecular dynamics (MD) simulations are still somewhat prohibitive and therefore limit the time and length scales that can be currently achieved. One approach to overcoming the limited size and duration of a simulation is to reduce the amount of detail when representing a system of interest, generally termed "coarse-graining". An alternative approach is via more efficient sampling methods that offer an enhanced search of a complex multidimensional energy landscape. One could also combine enhanced sampling methods with a coarse-grained (CG) force field. Here, we apply generalized shadow hybrid Monte Carlo (GSHMC), a recently proposed simulation protocol, to a biomolecular system of moderate size and show that GSHMC offers improved sampling compared to standard MD simulation. Our test system is a CG representation of a small peptide toxin interacting with a phospholipid bilayer. Specifically, we show that GSHMC allows for a quicker localization of the toxin to its equilibrium location of interaction at the headgroup/water interface of the bilayer. GSHMC therefore potentially allows for future exploration of larger and more complex systems over longer periods, which would otherwise be impractical to perform using conventional simulation methodology.

Country
United Kingdom
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Keywords

Models, Molecular, Lipid Bilayers, Membrane Proteins, Computer Simulation, Peptides, Monte Carlo Method, Toxins, Biological

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
22
Average
Top 10%
Top 10%
Green
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