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应用于含显式水分子溶剂的丙氨酸二肽的部分多重正则分子动力学模拟的优化。

Optimization of partial multicanonical molecular dynamics simulations applied to an alanine dipeptide in explicit water solvent.

机构信息

Research Center for Computational Science Institute for Molecular Science Okazaki, Aichi 444-8585, Japan.

出版信息

Phys Chem Chem Phys. 2011 Jan 7;13(1):114-26. doi: 10.1039/c0cp00371a. Epub 2010 Oct 29.

Abstract

The partial multicanonical algorithm for molecular dynamics and Monte Carlo simulations samples a wide range of an important part of the potential energy. Although it is a strong technique for structure prediction of biomolecules, the choice of the partial potential energy has not been optimized. In order to find the best choice, partial multicanonical molecular dynamics simulations of an alanine dipeptide in explicit water solvent were performed with 15 trial choices for the partial potential energy. The best choice was found to be the sum of the electrostatic, Lennard-Jones, and torsion-angle potential energies between solute atoms. In this case, the partial multicanonical simulation sampled all of the local-minimum free-energy states of the P(II), C(5), α(R), α(P), α(L), and C states and visited these states most frequently. Furthermore, backbone dihedral angles ϕ and ψ rotated very well. It is also found that the most important term among these three terms is the electrostatic potential energy and that the Lennard-Jones term also helps the simulation to overcome the steric restrictions. On the other hand, multicanonical simulation sampled all of the six states, but visited these states fewer times. Conventional canonical simulation sampled only four of the six states: The P(II), C(5), α(R), and α(P) states.

摘要

部分多重正则化算法可用于分子动力学和蒙特卡罗模拟,以采样重要的一部分势能。虽然它是生物分子结构预测的强大技术,但部分势能的选择尚未得到优化。为了找到最佳选择,对在显式水中的丙氨酸二肽进行了 15 种部分势能的尝试选择的部分多重正则化分子动力学模拟。发现最佳选择是溶质原子之间的静电、 Lennard-Jones 和扭转角势能的总和。在这种情况下,部分多重正则化模拟采样了 P(II)、C(5)、α(R)、α(P)、α(L)和 C 状态的所有局部最小自由能状态,并最频繁地访问这些状态。此外,主链二面角 ϕ 和 ψ 旋转得很好。还发现,这三个项中最重要的项是静电势能,Lennard-Jones 项也有助于模拟克服空间限制。另一方面,多重正则化模拟采样了所有六个状态,但访问这些状态的次数较少。传统的正则化模拟仅采样了六个状态中的四个:P(II)、C(5)、α(R)和α(P)状态。

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