Literature DB >> 34283604

Reaction Path-Force Matching in Collective Variables: Determining Ab Initio QM/MM Free Energy Profiles by Fitting Mean Force.

Bryant Kim1, Ryan Snyder1, Mulpuri Nagaraju1, Yan Zhou1, Pedro Ojeda-May1, Seth Keeton1, Mellisa Hege1, Yihan Shao2, Jingzhi Pu1.   

Abstract

First-principles determination of free energy profiles for condensed-phase chemical reactions is hampered by the daunting costs associated with configurational sampling on ab initio quantum mechanical/molecular mechanical (AI/MM) potential energy surfaces. Here, we report a new method that enables efficient AI/MM free energy simulations through mean force fitting. In this method, a free energy path in collective variables (CVs) is first determined on an efficient reactive aiding potential. Based on the configurations sampled along the free energy path, correcting forces to reproduce the AI/MM forces on the CVs are determined through force matching. The AI/MM free energy profile is then predicted from simulations on the aiding potential in conjunction with the correcting forces. Such cycles of correction-prediction are repeated until convergence is established. As the instantaneous forces on the CVs sampled in equilibrium ensembles along the free energy path are fitted, this procedure faithfully restores the target free energy profile by reproducing the free energy mean forces. Due to its close connection with the reaction path-force matching (RP-FM) framework recently introduced by us, we designate the new method as RP-FM in collective variables (RP-FM-CV). We demonstrate the effectiveness of this method on a type-II solution-phase SN2 reaction, NH3 + CH3Cl (the Menshutkin reaction), simulated with an explicit water solvent. To obtain the AI/MM free energy profiles, we employed the semiempirical AM1/MM Hamiltonian as the base level for determining the string minimum free energy pathway, along which the free energy mean forces are fitted to various target AI/MM levels using the Hartree-Fock (HF) theory, density functional theory (DFT), and the second-order Møller-Plesset perturbation (MP2) theory as the AI method. The forces on the bond-breaking and bond-forming CVs at both the base and target levels are obtained by force transformation from Cartesian to redundant internal coordinates under the Wilson B-matrix formalism, where the linearized FM is facilitated by the use of spline functions. For the Menshutkin reaction tested, our FM treatment greatly reduces the deviations on the CV forces, originally in the range of 12-33 to ∼2 kcal/mol/Å. Comparisons with the experimental and benchmark AI/MM results, tests of the new method under a variety of simulation protocols, and analyses of the solute-solvent radial distribution functions suggest that RP-FM-CV can be used as an efficient, accurate, and robust method for simulating solution-phase chemical reactions.

Entities:  

Year:  2021        PMID: 34283604      PMCID: PMC9064116          DOI: 10.1021/acs.jctc.1c00245

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.578


  32 in total

1.  Accurate and Efficient Treatment of Continuous Solute Charge Density in the Mean-Field QM/MM Free Energy Calculation.

Authors:  Hiroshi Nakano; Takeshi Yamamoto
Journal:  J Chem Theory Comput       Date:  2012-12-13       Impact factor: 6.006

2.  Using force-matched potentials to improve the accuracy of density functional tight binding for reactive conditions.

Authors:  Nir Goldman; Laurence E Fried; Lucas Koziol
Journal:  J Chem Theory Comput       Date:  2015-09-18       Impact factor: 6.006

3.  Hybrid quantum mechanics/molecular mechanics simulations with two-dimensional interpolated corrections: application to enzymatic processes.

Authors:  J Javier Ruiz-Pernía; Estanislao Silla; Iñaki Tuñón; Sergio Martí
Journal:  J Phys Chem B       Date:  2006-09-07       Impact factor: 2.991

Review 4.  CHARMM: the biomolecular simulation program.

Authors:  B R Brooks; C L Brooks; A D Mackerell; L Nilsson; R J Petrella; B Roux; Y Won; G Archontis; C Bartels; S Boresch; A Caflisch; L Caves; Q Cui; A R Dinner; M Feig; S Fischer; J Gao; M Hodoscek; W Im; K Kuczera; T Lazaridis; J Ma; V Ovchinnikov; E Paci; R W Pastor; C B Post; J Z Pu; M Schaefer; B Tidor; R M Venable; H L Woodcock; X Wu; W Yang; D M York; M Karplus
Journal:  J Comput Chem       Date:  2009-07-30       Impact factor: 3.376

5.  Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density.

Authors: 
Journal:  Phys Rev B Condens Matter       Date:  1988-01-15

6.  Multiscale reactive molecular dynamics.

Authors:  Chris Knight; Gerrick E Lindberg; Gregory A Voth
Journal:  J Chem Phys       Date:  2012-12-14       Impact factor: 3.488

7.  Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks.

Authors:  Lin Shen; Jingheng Wu; Weitao Yang
Journal:  J Chem Theory Comput       Date:  2016-09-06       Impact factor: 6.006

8.  Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems.

Authors:  Lennard Böselt; Moritz Thürlemann; Sereina Riniker
Journal:  J Chem Theory Comput       Date:  2021-04-05       Impact factor: 6.006

9.  Fragment Molecular Orbital method-based Molecular Dynamics (FMO-MD) as a simulator for chemical reactions in explicit solvation.

Authors:  Yuto Komeiji; Takeshi Ishikawa; Yuji Mochizuki; Hiroshi Yamataka; Tatsuya Nakano
Journal:  J Comput Chem       Date:  2009-01-15       Impact factor: 3.376

10.  Acceleration of Ab Initio QM/MM Calculations under Periodic Boundary Conditions by Multiscale and Multiple Time Step Approaches.

Authors:  Kwangho Nam
Journal:  J Chem Theory Comput       Date:  2014-09-15       Impact factor: 6.006

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  2 in total

1.  Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions.

Authors:  Xiaoliang Pan; Junjie Yang; Richard Van; Evgeny Epifanovsky; Junming Ho; Jing Huang; Jingzhi Pu; Ye Mei; Kwangho Nam; Yihan Shao
Journal:  J Chem Theory Comput       Date:  2021-09-01       Impact factor: 6.578

2.  Doubly Polarized QM/MM with Machine Learning Chaperone Polarizability.

Authors:  Bryant Kim; Yihan Shao; Jingzhi Pu
Journal:  J Chem Theory Comput       Date:  2021-11-01       Impact factor: 6.578

  2 in total

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