• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

HessFit:从量子力学信息中推导出自动力场的工具包。

HessFit: A Toolkit to Derive Automated Force Fields from Quantum Mechanical Information.

机构信息

Department of Pharmacy, Drug Discovery Lab, University of Naples Federico II, Naples 80131, Italy.

出版信息

J Chem Inf Model. 2024 Jul 22;64(14):5634-5645. doi: 10.1021/acs.jcim.4c00540. Epub 2024 Jun 19.

DOI:10.1021/acs.jcim.4c00540
PMID:38897917
Abstract

In this study, we introduce a novel approach to enhance the accuracy of molecular dynamics simulations by refining the force fields (FFs) through a combination of transferable parameters and molecule-specific characteristics derived from quantum mechanical (QM) calculations. Traditional FFs often prioritize generality over precision, leading to limitations in the accuracy of accurately capturing intra- and intermolecular interactions. To address this, we present an open-source toolkit, called HessFit, designed to integrate QM-derived bonded parameters and atomic charges into existing FFs. In combination with bond, angle, torsional, and nonbonded parameters derivation, HessFit can easily extract multiple barrier terms of dihedrals from QM Hessian and gradient or return all terms through a fitting procedure scheme of QM potential energy surface. We showcase the effectiveness of HessFit through comprehensive evaluations of vibrational properties across a diverse set of small molecules and demonstrate that experimental results support its ability in predicting thermodynamic properties of organic molecules compared to previous state-of-the-art approaches. We further explore its application to Zn metalloprotein models, which are hard systems to treat with automatic approaches. Our results demonstrate that HessFit parameters compete with GAFF2 and OPLS parameters to describing small organic molecules, and its feasibility is also comparable to current FFs used to modeling nonstandard residues in Zn proteins for molecular dynamics simulations. The effectiveness of the HessFit protocol makes it a valuable tool for deriving or extending force field parameters for novel compounds in several molecular modeling applications.

摘要

在这项研究中,我们介绍了一种通过结合可转移参数和量子力学(QM)计算得出的分子特异性特征来改进力场(FF)以提高分子动力学模拟准确性的新方法。传统的 FF 通常更注重通用性而不是精度,这导致其在准确捕捉分子内和分子间相互作用方面存在局限性。为了解决这个问题,我们提出了一个名为 HessFit 的开源工具包,旨在将 QM 衍生的键参数和原子电荷集成到现有的 FF 中。结合键、角度、扭转和非键参数的推导,HessFit 可以轻松地从 QM Hessian 和梯度中提取多个二面角的势垒项,或者通过 QM 势能面拟合程序方案返回所有项。我们通过对各种小分子的振动性质进行全面评估展示了 HessFit 的有效性,并证明与以前的最先进方法相比,它能够预测有机分子的热力学性质。我们进一步探索了它在 Zn 金属蛋白模型中的应用,这些模型是用自动方法难以处理的硬系统。我们的结果表明,HessFit 参数在描述小分子方面与 GAFF2 和 OPLS 参数具有竞争力,其可行性也与用于 Zn 蛋白中非标准残基建模的当前 FF 相当。HessFit 方案的有效性使其成为几种分子建模应用中为新型化合物推导或扩展力场参数的有价值工具。

相似文献

1
HessFit: A Toolkit to Derive Automated Force Fields from Quantum Mechanical Information.HessFit:从量子力学信息中推导出自动力场的工具包。
J Chem Inf Model. 2024 Jul 22;64(14):5634-5645. doi: 10.1021/acs.jcim.4c00540. Epub 2024 Jun 19.
2
Biomolecular force fields: where have we been, where are we now, where do we need to go and how do we get there?生物分子力场:我们从哪里来,我们现在在哪里,我们需要去哪里以及我们如何到达那里?
J Comput Aided Mol Des. 2019 Feb;33(2):133-203. doi: 10.1007/s10822-018-0111-4. Epub 2018 Nov 30.
3
Q-Force: Quantum Mechanically Augmented Molecular Force Fields.Q-Force:量子力学增强分子力场。
J Chem Theory Comput. 2021 Aug 10;17(8):4946-4960. doi: 10.1021/acs.jctc.1c00195. Epub 2021 Jul 12.
4
QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics.QUBEKit:从量子力学中自动推导出力场参数。
J Chem Inf Model. 2019 Apr 22;59(4):1366-1381. doi: 10.1021/acs.jcim.8b00767. Epub 2019 Feb 22.
5
Parametrization of Force Field Bonded Terms under Structural Inconsistency.结构不一致下力场键合项的参数化
J Chem Inf Model. 2022 Oct 10;62(19):4771-4782. doi: 10.1021/acs.jcim.2c00950. Epub 2022 Sep 16.
6
Force field development phase II: Relaxation of physics-based criteria… or inclusion of more rigorous physics into the representation of molecular energetics.力场开发阶段 II:放松基于物理的标准……或者将更严格的物理纳入分子能量学的表示中。
J Comput Aided Mol Des. 2019 Feb;33(2):205-264. doi: 10.1007/s10822-018-0134-x. Epub 2018 Nov 30.
7
Accurate Quantum-Mechanically Derived Force-Fields through a Fragment-Based Approach: Balancing Specificity and Transferability in the Prediction of Self-Assembly in Soft Matter.基于片段方法的精确量子力学衍生力场:在软物质自组装预测中平衡特异性和可转移性。
J Chem Theory Comput. 2022 Nov 8;18(11):6905-6919. doi: 10.1021/acs.jctc.2c00747. Epub 2022 Oct 19.
8
Recent advances toward a general purpose linear-scaling quantum force field.通用线性标度量子力场的最新进展。
Acc Chem Res. 2014 Sep 16;47(9):2812-20. doi: 10.1021/ar500103g. Epub 2014 Jun 17.
9
Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.朝着蛋白质模拟的通用神经网络力场迈进:蛋白质中分子内相互作用的改进。
J Chem Phys. 2023 Jul 14;159(2). doi: 10.1063/5.0142280.
10
A new force field (ECEPP-05) for peptides, proteins, and organic molecules.一种用于肽、蛋白质和有机分子的新力场(ECEPP - 05)。
J Phys Chem B. 2006 Mar 16;110(10):5025-44. doi: 10.1021/jp054994x.

引用本文的文献

1
From Apo to Ligand-Bound: Unraveling PPARγ-LBD Conformational Shifts via Advanced Molecular Dynamics.从脱辅基状态到配体结合状态:通过高级分子动力学解析PPARγ配体结合域的构象转变
ACS Omega. 2025 Feb 17;10(13):13303-13318. doi: 10.1021/acsomega.4c11128. eCollection 2025 Apr 8.
2
easyPARM: Automated, Versatile, and Reliable Force Field Parameters for Metal-Containing Molecules with Unique Labeling of Coordinating Atoms.easyPARM:用于含金属分子的自动化、通用且可靠的力场参数,具有配位原子的独特标记。
J Chem Theory Comput. 2025 Feb 25;21(4):1817-1830. doi: 10.1021/acs.jctc.4c01272. Epub 2025 Feb 6.