4.6 Article

Fast recovery of free energy landscapes via diffusion-map-directed molecular dynamics

期刊

PHYSICAL CHEMISTRY CHEMICAL PHYSICS
卷 16, 期 36, 页码 19181-19191

出版社

ROYAL SOC CHEMISTRY
DOI: 10.1039/c3cp54520b

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资金

  1. NSF [CHE-1152344, CHE-1265929, OCI-0959097, OCI-1053575]
  2. Welch Foundation [C-1570]
  3. NIH [NCRR S10RR02950]
  4. IBM Shared University Research (SUR) Award
  5. CISCO
  6. Qlogic
  7. Adaptive Computing
  8. Division Of Chemistry
  9. Direct For Mathematical & Physical Scien [1265929, 1152344] Funding Source: National Science Foundation
  10. Division Of Physics
  11. Direct For Mathematical & Physical Scien [1427654] Funding Source: National Science Foundation

向作者/读者索取更多资源

The reaction pathways characterizing macromolecular systems of biological interest are associated with high free energy barriers. Resorting to the standard all-atom molecular dynamics (MD) to explore such critical regions may be inappropriate as the time needed to observe the relevant transitions can be remarkably long. In this paper, we present a new method called Extended Diffusion-Map-directed Molecular Dynamics (extended DM-d-MD) used to enhance the sampling of MD trajectories in such a way as to rapidly cover all important regions of the free energy landscape including deep metastable states and critical transition paths. Moreover, extended DM-d-MD was combined with a reweighting scheme enabling to save on-the-fly information about the Boltzmann distribution. Our algorithm was successfully applied to two systems, alanine dipeptide and alanine-12. Due to the enhanced sampling, the Boltzmann distribution is recovered much faster than in plain MD simulations. For alanine dipeptide, we report a speedup of one order of magnitude with respect to Wain MD simulations. For alanine-12, our algorithm allows us to highlight all important unfolded basins in several days of computation when one single misfolded event is barely observable within the same amount of computational time by Wain MD simulations. Our method is reaction coordinate free, shows lithe dependence on the a priori knowledge of the system, and can be implemented in such a way that the biased steps are not computationay expensive with respect to MD simulations thus making our approach well adapted for larger complex systems from which lithe information is known.

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