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AUTOMATED FORCE FIELD PARAMETERIZATION FOR NON-POLARIZABLE AND POLARIZABLE ATOMIC MODELS BASED ON AB INITIO TARGET DATA

机译:基于AB初始目标数据的不可极化和可极化原子模型的自动力场参数化

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摘要

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 ab initio 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.
机译: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广泛的生物系统。从此类模拟中获得有意义结果的前提是精确的分子机械力场。目前,大多数生物分子模拟都基于广泛使用的AMBER和CHARMM力场,这些场已进行参数化和优化以涵盖一小部分与天然氨基酸和核酸碱基相对应的碱性化合物。通常通过类似于给定力场的参数集来生成其他化合物的原子模型。虽然此过程产生的模型在内部是一致的,但是所产生模型的准确性可能受到限制。在这项工作中,我们提出了一种通用的自动原子模型参数化(GAAMP)方法,该方法可以使用从头算量子力学(QM)计算得出的结果作为目标数据自动生成小分子的原子模型参数。尽管可以优化任何最终参数,但先前为各种模型化合物开发的力场可作为初始猜测。静电参数(部分电荷,极化率和屏蔽度)基于QM静电势(ESP)以及化合物(如适用)与水分子之间的相互作用能进行优化。通过针对QM二面体扫描以及稳定构象异构体的能量,可以自动识别和参数化软二面体。为了验证该方法,计算了200多个小分子的溶剂化自由能,并进行了3种不同蛋白质的MD模拟。

著录项

  • 期刊名称 other
  • 作者

    Lei Huang; Benoît Roux;

  • 作者单位
  • 年(卷),期 -1(9),8
  • 年度 -1
  • 页码 1021/ct4003477
  • 总页数 31
  • 原文格式 PDF
  • 正文语种
  • 中图分类
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  • 入库时间 2022-08-21 11:21:30

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