Kumar Anmol, MacKerell Alexander D
Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland 21201, United States.
J Phys Chem B. 2024 May 9;128(18):4385-4395. doi: 10.1021/acs.jpcb.4c01314. Epub 2024 May 1.
Developing production quality CHARMM force-field (FF) parameters is a very detailed process involving a variety of calculations, many of which are specific for the molecule of interest. The first version of FFParam was developed as a standalone Python package designed for the optimization of electrostatic and bonded parameters of the CHARMM additive and polarizable Drude FFs by using quantum mechanical (QM) target data. The new version of FFParam has multiple new capabilities for FF parameter optimization and validation, with an emphasis on the ability to use condensed-phase target data in optimization. FFParam-v2 allows optimization of Lennard-Jones (LJ) parameters using potential energy scans of interactions between selected atoms in a molecule and noble gases, ., He and Ne, and through condensed-phase calculations, from which experimental observables such as heats of vaporization and free energies of solvation may be obtained. This functionality serves as a gold standard for both optimizing parameters and validating the performance of the final parameters. A new bonded parameter optimization algorithm has been introduced to account for simultaneously optimizing multiple molecules sharing parameters. FFParam-v2 also supports the comparison of normal modes and the potential energy distribution of internal coordinates towards each normal mode obtained from QM and molecular mechanics calculations. Such comparison capability is vital to validate the balance among various bonded parameters that contribute to the complex normal modes of molecules. User interaction has been extended beyond the original graphical user interface to include command-line interface capabilities that allow for integration of FFParam in workflows, thereby facilitating the automation of parameter optimization. With these new functionalities, FFParam is a more comprehensive parameter optimization tool for both beginners and advanced users.
开发生产质量的CHARMM力场（FF）参数是一个非常详细的过程，涉及各种计算，其中许多计算是针对目标分子特定的。FFParam的第一个版本是作为一个独立的Python包开发的，旨在通过使用量子力学（QM）目标数据来优化CHARMM添加剂和可极化德鲁德力场的静电和键合参数。FFParam的新版本具有多种用于FF参数优化和验证的新功能，重点是在优化中使用凝聚相目标数据的能力。FFParam-v2允许使用分子中选定原子与稀有气体（如氦气和氖气）之间相互作用的势能扫描来优化 Lennard-Jones（LJ）参数，并通过凝聚相计算来获得诸如汽化热和溶剂化自由能等实验可观测量。此功能既是优化参数的黄金标准，也是验证最终参数性能的黄金标准。引入了一种新的键合参数优化算法，以考虑同时优化共享参数的多个分子。FFParam-v2还支持比较正常模式以及从QM和分子力学计算获得的每个正常模式的内部坐标的势能分布。这种比较能力对于验证有助于分子复杂正常模式的各种键合参数之间的平衡至关重要。用户交互已从原始的图形用户界面扩展到包括命令行界面功能，允许将FFParam集成到工作流程中，从而促进参数优化的自动化。有了这些新功能，FFParam对于初学者和高级用户来说都是一个更全面的参数优化工具。
