Speed of Conformational Change: Comparing Explicit and Implicit Solvent Molecular Dynamics Simulations

被引:149
|
作者
Anandakrishnan, Ramu [1 ]
Drozdetski, Aleksander [2 ]
Walker, Ross C. [3 ,4 ]
Onufriev, Alexey V. [1 ,2 ]
机构
[1] Virginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USA
[2] Virginia Tech, Dept Phys, Blacksburg, VA USA
[3] Univ Calif San Diego, San Diego Supercomp Ctr, San Diego, CA 92103 USA
[4] Univ Calif San Diego, Dept Chem & Biochem, San Diego, CA 92103 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
GENERALIZED-BORN MODEL; PARTICLE MESH EWALD; REPLICA-EXCHANGE SIMULATIONS; LONG-RANGE INTERACTIONS; BIOMOLECULAR SIMULATIONS; NUCLEIC-ACIDS; FREE-ENERGIES; FORCE-FIELDS; DIELECTRIC ENVIRONMENTS; VISCOSITY DEPENDENCE;
D O I
10.1016/j.bpj.2014.12.047
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Adequate sampling of conformation space remains challenging in atomistic simulations, especially if the solvent is treated explicitly. Implicit-solvent simulations can speed up conformational sampling significantly. We compare the speed of conformational sampling between two commonly used methods of each class: the explicit-solvent particle mesh Ewald (PME) with TIP3P water model and a popular generalized Born (GB) implicit-solvent model, as implemented in the AMBER package. We systematically investigate small (dihedral angle flips in a protein), large (nucleosome tail collapse and DNA unwrapping), and mixed (folding of a miniprotein) conformational changes, with nominal simulation times ranging from nanoseconds to microseconds depending on system size. The speedups in conformational sampling for GB relative to PME simulations, are highly system-and problem-dependent. Where the simulation temperatures for PME and GB are the same, the corresponding speedups are approximately onefold (small conformational changes), between similar to 1- and similar to 100-fold (large changes), and approximately sevenfold (mixed case). The effects of temperature on speedup and free-energy landscapes, which may differ substantially between the solvent models, are discussed in detail for the case of miniprotein folding. In addition to speeding up conformational sampling, due to algorithmic differences, the implicit solvent model can be computationally faster for small systems or slower for large systems, depending on the number of solute and solvent atoms. For the conformational changes considered here, the combined speedups are approximately twofold, similar to 1- to 60-fold, and similar to 50-fold, respectively, in the low solvent viscosity regime afforded by the implicit solvent. For all the systems studied, 1) conformational sampling speedup increases as Langevin collision frequency (effective viscosity) decreases; and 2) conformational sampling speedup is mainly due to reduction in solvent viscosity rather than possible differences in free-energy landscapes between the solvent models.
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页码:1153 / 1164
页数:12
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