Browsing by Author "Nam, Kwangho"

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    Accelerating ab initio QM/MM Molecular Dynamics Simulations with Multiple Time Step Integration and a Recalibrated Semi-empirical QM/MM Hamiltonian
    (American Chemical Society, 2022-06-02) Pan, Xiaoliang; Van, Richard; Epifanovsky, Evgeny; Liu, Jian; Pu, Jingzhi; Nam, Kwangho; Shao, Yihan; Chemistry and Chemical Biology, School of Science
    Molecular dynamics (MD) simulations employing ab initio quantum mechanical and molecular mechanical (ai-QM/MM) potentials are considered to be the state of the art, but the high computational cost associated with the ai-QM calculations remains a theoretical challenge for their routine application. Here, we present a modified protocol of the multiple time step (MTS) method for accelerating ai-QM/MM MD simulations of condensed-phase reactions. Within a previous MTS protocol [Nam J. Chem. Theory Comput. 2014, 10, 4175], reference forces are evaluated using a low-level (semiempirical QM/MM) Hamiltonian and employed at inner time steps to propagate the nuclear motions. Correction forces, which arise from the force differences between high-level (ai-QM/MM) and low-level Hamiltonians, are applied at outer time steps, where the MTS algorithm allows the time-reversible integration of the correction forces. To increase the outer step size, which is bound by the highest-frequency component in the correction forces, the semiempirical QM Hamiltonian is recalibrated in this work to minimize the magnitude of the correction forces. The remaining high-frequency modes, which are mainly bond stretches involving hydrogen atoms, are then removed from the correction forces. When combined with a Langevin or SIN(R) thermostat, the modified MTS-QM/MM scheme remains robust with an up to 8 (with Langevin) or 10 fs (with SIN(R)) outer time step (with 1 fs inner time steps) for the chorismate mutase system. This leads to an over 5-fold speedup over standard ai-QM/MM simulations, without sacrificing the accuracy in the predicted free energy profile of the reaction.
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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.
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    Trapping the ATP binding state leads to a detailed understanding of the F1-ATPase mechanism
    (PNAS, 2014-12-16) Nam, Kwangho; Pu, Jingzhi; Karplus, Martin; Department of Chemistry & Chemical Biology, School of Science
    The rotary motor enzyme FoF1-ATP synthase uses the proton-motive force across a membrane to synthesize ATP from ADP and Pi (H2PO4(-)) under cellular conditions that favor the hydrolysis reaction by a factor of 2 × 10(5). This remarkable ability to drive a reaction away from equilibrium by harnessing an external force differentiates it from an ordinary enzyme, which increases the rate of reaction without shifting the equilibrium. Hydrolysis takes place in the neighborhood of one conformation of the catalytic moiety F1-ATPase, whose structure is known from crystallography. By use of molecular dynamics simulations we trap a second structure, which is rotated by 40° from the catalytic dwell conformation and represents the state associated with ATP binding, in accord with single-molecule experiments. Using the two structures, we show why Pi is not released immediately after ATP hydrolysis, but only after a subsequent 120° rotation, in agreement with experiment. A concerted conformational change of the α3β3 crown is shown to induce the 40° rotation of the γ-subunit only when the βE subunit is empty, whereas with Pi bound, βE serves as a latch to prevent the rotation of γ. The present results provide a rationalization of how F1-ATPase achieves the coupling between the small changes in the active site of βDP and the 40° rotation of γ.