Huang Lei, Roux Benoît
Department of Biochemistry and Molecular Biology University of Chicago 929 East 57th Street, Chicago, IL 60637.
J Chem Theory Comput. 2013 Aug 13;9(8). doi: 10.1021/ct4003477.
Classical molecular dynamics (MD) simulations based on atomistic models are increasingly used to study a wide range of biological systems. A prerequisite for meaningful results from such simulations is an accurate molecular mechanical force field. Most biomolecular simulations are currently based on the widely used AMBER and CHARMM force fields, which were parameterized and optimized to cover a small set of basic compounds corresponding to the natural amino acids and nucleic acid bases. Atomic models of additional compounds are commonly generated by analogy to the parameter set of a given force field. While this procedure yields models that are internally consistent, the accuracy of the resulting models can be limited. In this work, we propose a method, General Automated Atomic Model Parameterization (GAAMP), for generating automatically the parameters of atomic models of small molecules using the results from quantum mechanical (QM) calculations as target data. Force fields that were previously developed for a wide range of model compounds serve as initial guess, although any of the final parameter can be optimized. The electrostatic parameters (partial charges, polarizabilities and shielding) are optimized on the basis of QM electrostatic potential (ESP) and, if applicable, the interaction energies between the compound and water molecules. The soft dihedrals are automatically identified and parameterized by targeting QM dihedral scans as well as the energies of stable conformers. To validate the approach, the solvation free energy is calculated for more than 200 small molecules and MD simulations of 3 different proteins are carried out.
基于原子模型的经典分子动力学(MD)模拟越来越多地用于研究各种生物系统。此类模拟得出有意义结果的一个前提是要有精确的分子力学力场。目前大多数生物分子模拟都基于广泛使用的AMBER和CHARMM力场,这些力场经过参数化和优化,以涵盖与天然氨基酸和核酸碱基相对应的一小部分基本化合物。其他化合物的原子模型通常通过类比给定力场的参数集来生成。虽然这个过程产生的模型在内部是一致的,但所得模型的准确性可能会受到限制。在这项工作中,我们提出了一种方法,即通用自动原子模型参数化(GAAMP),用于使用量子力学(QM)计算结果作为目标数据自动生成小分子原子模型的参数。以前为广泛的模型化合物开发的力场用作初始猜测,不过最终的任何参数都可以优化。静电参数(部分电荷、极化率和屏蔽)基于QM静电势(ESP)进行优化,并且在适用时,基于化合物与水分子之间的相互作用能进行优化。通过以QM二面角扫描以及稳定构象体的能量为目标,自动识别并参数化软二面角。为了验证该方法,计算了200多种小分子的溶剂化自由能,并对3种不同的蛋白质进行了MD模拟。