Explicit polarization: a quantum mechanical framework for developing next generation force fields.
Explicit polarization: a quantum mechanical framework for developing next generation force fields.
复制标题
显式极化：用于开发下一代力场的量子机械框架。
DOI：
10.1021/ar5002186
复制
发表时间：
2014-09-16
影响因子：
18.3
通讯作者：
Rehak, Pavel
中科院分区：
文献类型：
--
作者：
Gao, Jiali;Truhlar, Donald G.;Wang, Yingjie;Mazack, Michael J. M.;Loeffler, Patrick;Provorse, Makenzie R.;Rehak, Pavel
半个世纪以来，分子力学力场已被成功地用于模拟凝聚相和生物系统。通过仔细的参数化，这样的经典力场可以用来提供有用的解释实验结果和某些性质的预测。然而，有必要进一步提高生物分子相互作用的定量预测的计算精度，并依赖于波函数，而不仅仅是能量项的模型属性。一种新的策略，称为显式极化（X-Pol）已被开发来构建势能面和波函数的基础上的量子力学，而不是仅仅使用量子力学的结果来拟合解析力场的大分子和液相模拟。在这种精神下，这种方法被称为量子力学力场（QMFF）。X-Pol是一种用于电子结构计算的通用片段方法，其基于将凝聚相或大分子系统划分为子系统（“片段”）以实现计算效率。在这里，intrafragment能量和相互电子极化的interfragment相互作用明确使用量子力学处理。X-Pol可以作为一种通用的、多层次的大分子电子结构模型，也可以作为新一代的力场。作为一种量子化学模型，变分多体（VMB）展开方法被用来系统地改善碎片间的相互作用，包括交换排斥，电荷离域，色散和其他相关能。作为一个量子力学力场，这些能量项近似的经验函数的精神，传统的分子力学。本帐户首先审查的X-Pol的制定，在完全变分正确的版本，在更快的嵌入式版本，并与系统的多体改进。我们讨论说明性的例子涉及水集群（其显示了两体校正的能力）、乙基甲基咪唑乙酸盐离子液体（这表明阴离子和阳离子之间的电荷转移量比一些经典模拟中假设的要小得多），和水溶液中的溶剂化蛋白质（这表明羰基沿着多肽链的平均电荷分布强烈地依赖于它们在序列中的位置，而它们在大多数经典力场中是固定的）。QMFF的发展也提供了一个机会，将生化模拟的准确性扩展到经典力场往往不足的领域，特别是在光谱学、反应性和酶催化领域。
Molecular mechanical force fields have been successfully used to model condensed-phase and biological systems for a half century. By means of careful parametrization, such classical force fields can be used to provide useful interpretations of experimental findings and predictions of certain properties. Yet, there is a need to further improve computational accuracy for the quantitative prediction of biomolecular interactions and to model properties that depend on the wave functions and not just the energy terms. A new strategy called explicit polarization (X-Pol) has been developed to construct the potential energy surface and wave functions for macromolecular and liquid-phase simulations on the basis of quantum mechanics rather than only using quantum mechanical results to fit analytic force fields. In this spirit, this approach is called a quantum mechanical force field (QMFF). X-Pol is a general fragment method for electronic structure calculations based on the partition of a condensed-phase or macromolecular system into subsystems (“fragments”) to achieve computational efficiency. Here, intrafragment energy and the mutual electronic polarization of interfragment interactions are treated explicitly using quantum mechanics. X-Pol can be used as a general, multilevel electronic structure model for macromolecular systems, and it can also serve as a new-generation force field. As a quantum chemical model, a variational many-body (VMB) expansion approach is used to systematically improve interfragment interactions, including exchange repulsion, charge delocalization, dispersion, and other correlation energies. As a quantum mechanical force field, these energy terms are approximated by empirical functions in the spirit of conventional molecular mechanics. This Account first reviews the formulation of X-Pol, in the full variationally correct version, in the faster embedded version, and with systematic many-body improvements. We discuss illustrative examples involving water clusters (which show the power of two-body corrections), ethylmethylimidazolium acetate ionic liquids (which reveal that the amount of charge transfer between anion and cation is much smaller than what has been assumed in some classical simulations), and a solvated protein in aqueous solution (which shows that the average charge distribution of carbonyl groups along the polypeptide chain depends strongly on their position in the sequence, whereas they are fixed in most classical force fields). The development of QMFFs also offers an opportunity to extend the accuracy of biochemical simulations to areas where classical force fields are often insufficient, especially in the areas of spectroscopy, reactivity, and enzyme catalysis.
登录
查看更多内容
影响因子：
3.3
作者：
Gao, JL
通讯作者：
Gao, JL
影响因子：
5.5
作者：
Isegawa, Miho;Fiedler, Luke;Truhlar, Donald G.
通讯作者：
Truhlar, Donald G.
影响因子：
5.5
作者：
Giese, Timothy J.;Chen, Haoyuan;Dissanayake, Thakshila;Giambasu, George M.;Heldenbrand, Hugh;Huang, Ming;Kuechler, Erich R.;Lee, Tai-Sung;Panteva, Maria T.;Radak, Brian K.;York, Darrin M.
通讯作者：
York, Darrin M.
影响因子：
5.5
作者：
Cembran, Alessandro;Bao, Peng;Wang, Yingjie;Song, Lingchun;Truhlar, Donald G.;Gao, Jiali
通讯作者：
Gao, Jiali
影响因子：
2.9
作者：
Fedorov, Dmitri G.;Ishida, Toyokazu;Kitaura, Kazuo
通讯作者：
Kitaura, Kazuo