Browsing by Subject "Quantum theory"

Now showing 1 - 3 of 3
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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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    Facilitating Ab Initio QM/MM Free Energy Simulations by Gaussian Process Regression with Derivative Observations
    (Royal Society of Chemistry, 2022-10-27) Snyder, Ryan; Kim, Bryant; Pan, Xiaoliang; Shao, Yihan; Pu, Jingzhi; Chemistry and Chemical Biology, School of Science
    In combined quantum mechanical and molecular mechanical (QM/MM) free energy simulations, how to synthesize the accuracy of ab initio (AI) methods with the speed of semiempirical (SE) methods for a cost-effective QM treatment remains a long-standing challenge. In this work, we present a machine-learning-facilitated method for obtaining AI/MM-quality free energy profiles through efficient SE/MM simulations. In particular, we use Gaussian process regression (GPR) to learn the energy and force corrections needed for SE/MM to match with AI/MM results during molecular dynamics simulations. Force matching is enabled in our model by including energy derivatives into the observational targets through the extended-kernel formalism. We demonstrate the effectiveness of this method on the solution-phase SN2 Menshutkin reaction using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. Trained on only 80 configurations sampled along the minimum free energy path (MFEP), the resulting GPR model reduces the average energy error in AM1/MM from 18.2 to 5.8 kcal mol-1 for the 4000-sample testing set with the average force error on the QM atoms decreased from 14.6 to 3.7 kcal mol-1 Å-1. Free energy sampling with the GPR corrections applied (AM1-GPR/MM) produces a free energy barrier of 14.4 kcal mol-1 and a reaction free energy of -34.1 kcal mol-1, in closer agreement with the AI/MM benchmarks and experimental results.
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    Free Energy Profile Decomposition Analysis for QM/MM Simulations of Enzymatic Reactions
    (American Chemical Society, 2023) Pan, Xiaoliang; Van, Richard; Pu, Jingzhi; Nam, Kwangho; Mao, Yuezhi; Shao, Yihan; Chemistry and Chemical Biology, School of Science
    In enzyme mechanistic studies and mutant design, it is highly desirable to know the individual residue contributions to the reaction free energy and barrier. In this work, we show that such free energy contributions from each residue can be readily obtained by postprocessing ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulation trajectories. Specifically, through a mean force integration along the minimum free energy pathway, one can obtain the electrostatic, polarization, and van der Waals contributions from each residue to the free energy barrier. Separately, a similar analysis procedure allows us to assess the contribution from different collective variables along the reaction coordinate. The chorismate mutase reaction is used to demonstrate the utilization of these two trajectory analysis tools.