TY - GEN
T1 - Think globally, move locally
T2 - International Research Workshop: Coping with Complexity: Model Reduction and Data Analysis
AU - Das, Payel
AU - Frewen, Thomas A.
AU - Kevrekidis, Ioannis G.
AU - Clementi, Cecilia
N1 - Funding Information:
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.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78651539735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78651539735&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-14941-2_6
DO - 10.1007/978-3-642-14941-2_6
M3 - Conference contribution
AN - SCOPUS:78651539735
SN - 9783642149405
T3 - Lecture Notes in Computational Science and Engineering
SP - 113
EP - 131
BT - Coping with Complexity
Y2 - 31 August 2009 through 4 September 2009
ER -