title: Sparsity-weighted outlier FLOODing (OFLOOD) method: Efficient rare event sampling method using sparsity of distribution.
abstract: As an extension of the Outlier FLOODing (OFLOOD) method [Harada et al., J. Comput. Chem. 2015, 36, 763], the sparsity of the outliers defined by a hierarchical clustering algorithm, FlexDice, was considered to achieve an efficient conformational search as sparsity-weighted "OFLOOD." In OFLOOD, FlexDice detects areas of sparse distribution as outliers. The outliers are regarded as candidates that have high potential to promote conformational transitions and are employed as initial structures for conformational resampling by restarting molecular dynamics simulations. When detecting outliers, FlexDice defines a rank in the hierarchy for each outlier, which relates to sparsity in the distribution. In this study, we define a lower rank (first ranked), a medium rank (second ranked), and the highest rank (third ranked) outliers, respectively. For instance, the first-ranked outliers are located in a given conformational space away from the clusters (highly sparse distribution), whereas those with the third-ranked outliers are nearby the clusters (a moderately sparse distribution). To achieve the conformational search efficiently, resampling from the outliers with a given rank is performed. As demonstrations, this method was applied to several model systems: Alanine dipeptide, Met-enkephalin, Trp-cage, T4 lysozyme, and glutamine binding protein. In each demonstration, the present method successfully reproduced transitions among metastable states. In particular, the first-ranked OFLOOD highly accelerated the exploration of conformational space by expanding the edges. In contrast, the third-ranked OFLOOD reproduced local transitions among neighboring metastable states intensively. For quantitatively evaluations of sampled snapshots, free energy calculations were performed with a combination of umbrella samplings, providing rigorous landscapes of the biomolecules.
authors.affiliation: Department of Physics Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8571, Japan.|Division of Life Sciences, Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8571, Japan.|Computational Engineering Application Unit, RIKEN Advanced Center for Computing and Communication, 2-1, Hirosawa, Wako, Saitama, 351-0198, Japan.
authors.forename: Ryuhei
authors.initials: R
authors.lastname: Harada
authors.affiliation: Fujitsu Limited, Numazu, Shizuoka, 411-0301, Japan.
authors.forename: Tomotake
authors.initials: T
authors.lastname: Nakamura
authors.affiliation: Department of Physics Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8571, Japan.|Division of Life Sciences, Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8571, Japan.|JST, CREST, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan.
authors.forename: Yasuteru
authors.initials: Y
authors.lastname: Shigeta
mesh_terms: D017122:Bacteriophage T4 / Q000201:enzymology; D016000:Cluster Analysis; D004151:Dipeptides / Q000737:chemistry; D056004:Molecular Dynamics Simulation; D009113:Muramidase / Q000737:chemistry; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D013816:Thermodynamics
chemical_list: D004151:Dipeptides; D011506:Proteins; C002956:alanylalanine; D009113:Muramidase
keywords: biologically rare event; conformational sampling; conformational transition; enhanced conformational sampling method; molecular dynamics simulation; protein folding; protein function