Abstract
We present a multi-scale simulation methodology, based on data-mining tools for the extraction of low-dimensional reduction coordinates, to explore dynamically a protein model on its underlying effective folding free energy landscape. In practice, the averaged coarse-grained description of the local protein dynamics is extracted in terms of a few reduction coordinates from multiple, relatively short molecular dynamics trajectories. By exploiting the information collected from the fast relaxation dynamics of the system, the reduction coordinates are extrapolated “backward-in-time” to map globally the underlying low-dimensional free energy landscape. We demonstrate that the proposed method correctly identifies the transition state region on the reconstructed two-dimensional free energy surface of a model protein folding transition.
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Acknowledgements
The authors have planned and started their collaboration on this project during the program on “Bridging Time and Length Scales in Materials Science and Bio-Physics” that was held at the NSF-funded Institute for Pure and Applied Mathematics (IPAM) at UCLA in September-December 2005. This work has been supported in part by grants from NSF (C.C. Career CHE-0349303, CCF-0523908, and CNS-0454333), and the Robert A. Welch Foundation (C.C. Norman Hackermann Young Investigator award, and grant C-1570). The Rice Cray XD1 Cluster ADA used for the calculations is funded by NSF under grant CNS-0421109, and a partnership between Rice University, AMD and Cray. T.A.F and I.G.K are partially supported by NSF and DARPA.
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Das, P., Frewen, T.A., Kevrekidis, I.G., Clementi, C. (2011). Think Globally, Move Locally: Coarse Graining of Effective Free Energy Surfaces. In: Gorban, A., Roose, D. (eds) Coping with Complexity: Model Reduction and Data Analysis. Lecture Notes in Computational Science and Engineering, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14941-2_6
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DOI: https://doi.org/10.1007/978-3-642-14941-2_6
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