Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder NTL9(1-39)
Department of Chemistry, Stanford University, Stanford, California 94305, USA
Journal of the American Chemical Society, Vol. 132, No. 5. (10 February 2010), pp. 1526-1528.
@article{voelz2010molecular,
title={Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder NTL9 (1- 39)},
author={Voelz, V.A. and Bowman, G.R. and Beauchamp, K. and Pande, V.S.},
journal={Journal of the American Chemical Society},
volume={132},
number={5},
pages={1526–1528},
issn={0002-7863},
year={2010},
publisher={ACS Publications}
}
To date, the slowest-folding proteins folded ab initio by all-atom molecular dynamics simulations have had folding times in the range of nanoseconds to microseconds. We report simulations of several folding trajectories of NTL9(1-39), a protein which has a folding time of ~1.5 ms. Distributed molecular dynamics simulations in implicit solvent on GPU processors were used to generate ensembles of trajectories out to ~40micros for several temperatures and starting states. At a temperature less than the melting point of the force field, we observe a small number of productive folding events, consistent with predictions from a model of parallel uncoupled two-state simulations. The posterior distribution of the folding rate predicted from the data agrees well with the experimental folding rate (~640/s). Markov State Models (MSMs) built from the data show a gap in the implied time scales indicative of two-state folding and heterogeneous pathways connecting diffuse mesoscopic substates. Structural analysis of the 14 out of 2000 macrostates transited by the top 10 folding pathways reveals that native-like pairing between strands 1 and 2 only occurs for macrostates with p(fold)>0.5, suggesting B12 hairpin formation may be rate-limiting. We believe that using simulation data such as these to seed adaptive resampling simulations will be a promising new method for achieving statistically converged descriptions of folding landscapes at longer time scales than ever before.
November 4, 2010 by hgpu