TY - JOUR
T1 - To milliseconds and beyond
T2 - Challenges in the simulation of protein folding
AU - Lane, Thomas J.
AU - Shukla, Diwakar
AU - Beauchamp, Kyle A.
AU - Pande, Vijay S.
N1 - Funding Information:
DS and VSP acknowledge support from the Simbios NIH Center for Biomedical Computation (NIH U54 Roadmap GM072970 ), VSP acknowledges NIH ( R01GM62828 ). TJL was supported by an NSF GRF , KAB was supported by a Stanford Graduate Fellowship .
PY - 2013/2
Y1 - 2013/2
N2 - Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.
AB - Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.
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U2 - 10.1016/j.sbi.2012.11.002
DO - 10.1016/j.sbi.2012.11.002
M3 - Review article
C2 - 23237705
AN - SCOPUS:84873526912
VL - 23
SP - 58
EP - 65
JO - Current Opinion in Structural Biology
JF - Current Opinion in Structural Biology
SN - 0959-440X
IS - 1
ER -