In this article, we present a novel adaptive enhanced sampling molecular dynamics (MD) method for the accelerated simulation of protein folding and aggregation. We introduce a path-variable L based on the un-biased momenta p and displacements dq for the definition of the bias s applied to the system and derive 3 algorithms: general adaptive bias MD, adaptive path-sampling, and a hybrid method which combines the first 2 methodologies. Through the analysis of the correlations between the bias and the un-biased gradient in the system, we find that the hybrid methodology leads to an improved force correlation and acceleration in the sampling of the phase space. We apply our method on SPC/E water, where we find a conservation of the average water structure. We then use our method to sample dialanine and the folding of TrpCage, where we find a good agreement with simulation data reported in the literature. Finally, we apply our methodologies on the initial stages of aggregation of a hexamer of Alzheimer’s amyloid β fragment 25-35 (Aβ 25-35) and find that transitions within the hexameric aggregate are dominated by entropic barriers, while we speculate that especially the conformation entropy plays a major role in the formation of the fibril as a rate limiting factor.
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Research Article| December 05 2017
Adaptive enhanced sampling with a path-variable for the simulation of protein folding and aggregation
Emanuel K. Peter
Emanuel K. Peter a)
Department of Pharmacy and Chemistry, Institute of Physical and Theoretical Chemistry, University of Regensburg, Regensburg,
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J. Chem. Phys. 147, 214902 (2017)
August 20 2017
November 09 2017
Emanuel K. Peter; Adaptive enhanced sampling with a path-variable for the simulation of protein folding and aggregation. J. Chem. Phys. 7 December 2017; 147 (21): 214902. https://doi.org/10.1063/1.5000930
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