title: Toward canonical ensemble distribution from self-guided Langevin dynamics simulation.
abstract: This work derives a quantitative description of the conformational distribution in self-guided Langevin dynamics (SGLD) simulations. SGLD simulations employ guiding forces calculated from local average momentums to enhance low-frequency motion. This enhancement in low-frequency motion dramatically accelerates conformational search efficiency, but also induces certain perturbations in conformational distribution. Through the local averaging, we separate properties of molecular systems into low-frequency and high-frequency portions. The guiding force effect on the conformational distribution is quantitatively described using these low-frequency and high-frequency properties. This quantitative relation provides a way to convert between a canonical ensemble and a self-guided ensemble. Using example systems, we demonstrated how to utilize the relation to obtain canonical ensemble properties and conformational distributions from SGLD simulations. This development makes SGLD not only an efficient approach for conformational searching, but also an accurate means for conformational sampling.
authors.affiliation: Laboratory of Computational Biology, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland 20892, USA. wuxw@nhlbi.nih.gov
authors.forename: Xiongwu
authors.initials: X
authors.lastname: Wu
authors.forename: Bernard R
authors.initials: BR
authors.lastname: Brooks
mesh_terms: D000465:Algorithms; D001128:Argon / Q000737:chemistry*; D004151:Dipeptides / Q000737:chemistry*; D008968:Molecular Conformation; D056004:Molecular Dynamics Simulation; D011487:Protein Conformation; D013816:Thermodynamics
chemical_list: D004151:Dipeptides; C002956:alanylalanine; D001128:Argon