Browsing by Author "Huang, Jing"

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    CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
    (American Chemical Society, 2024) Hwang, Wonmuk; Austin, Steven L.; Blondel, Arnaud; Boittier, Eric D.; Boresch, Stefan; Buck, Matthias; Buckner, Joshua; Caflisch, Amedeo; Chang, Hao-Ting; Cheng, Xi; Choi, Yeol Kyo; Chu, Jhih-Wei; Crowley, Michael F.; Cui, Qiang; Damjanovic, Ana; Deng, Yuqing; Devereux, Mike; Ding, Xinqiang; Feig, Michael F.; Gao, Jiali; Glowacki, David R.; Gonzales, James E., II; Hamaneh, Mehdi Bagerhi; Harder, Edward D.; Hayes, Ryan L.; Huang, Jing; Huang, Yandong; Hudson, Phillip S.; Im, Wonpil; Islam, Shahidul M.; Jiang, Wei; Jones, Michael R.; Käser, Silvan; Kearns, Fiona L.; Kern, Nathan R.; Klauda, Jeffery B.; Lazaridis, Themis; Lee, Jinhyuk; Lemkul, Justin A.; Liu, Xiaorong; Luo, Yun; MacKerell, Alexander D., Jr.; Major, Dan T.; Meuwly, Markus; Nam, Kwangho; Nilsson, Lennart; Ovchinnikov, Victor; Paci, Emanuele; Park, Soohyung; Pastor, Richard W.; Pittman, Amanda R.; Post, Carol Beth; Prasad, Samarjeet; Pu, Jingzhi; Qi, Yifei; Rathinavelan, Thenmalarchelvi; Roe, Daniel R.; Roux, Benoit; Rowley, Christopher N.; Shen, Jana; Simmonett, Andrew C.; Sodt, Alexander J.; Töpfer, Kai; Upadhyay, Meenu; van der Vaart, Arjan; Vazquez-Salazar, Luis Itza; Venable, Richard M.; Warrensford, Luke C.; Woodcock, H. Lee; Wu, Yujin; Brooks, Charles L., III; Brooks, Bernard R.; Karplus, Martin; Chemistry and Chemical Biology, School of Science
    Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.
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    Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions
    (ACS, 2021-09) Pan, Xiaoliang; Yang, Junjie; Van, Richard; Epifanovsky, Evgeny; Ho, Junming; Huang, Jing; Pu, Jingzhi; Mei, Ye; Nam, Kwangho; Shao, Yihan; Chemistry and Chemical Biology, School of Science
    Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the ai-QM/MM and semiempirical (se) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the ai-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol–1 Å–1, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results at only a fraction of the computational cost.