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 (Impact Factor: 12.11). 02/2010; 132(5):1526-8. DOI: 10.1021/ja9090353
Source: PubMed


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 approximately 1.5 ms. Distributed molecular dynamics simulations in implicit solvent on GPU processors were used to generate ensembles of trajectories out to approximately 40 micros 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 (approximately 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 beta(12) 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.

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Available from: Vincent A Voelz, Oct 01, 2015
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    • "A promising approach to model such an intricate coupling is Markov state modeling of molecular dynamics simulations [55] [56] [57] [58] [59] [60]. Such Markov state modeling has been previously applied to obtain pathways for the conformational changes of proteins [61] [62] [63] [64]. Other methods to explore rare conformational transitions of proteins with atomistic molecular dynamic simulations identify reaction coordinates or collective variables for such transitions [65] [66] [67] [68] [69]. "
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    Protein Science 11/2014; 23(11). DOI:10.1002/pro.2539 · 2.85 Impact Factor
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