1
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Paz H, Beck S, Lee R, Ho J, Yu H. The Effects of Conformational Sampling and QM Region Size in QM/MM Simulations: An Adaptive QM/MM Study With Model Systems. J Comput Chem 2025; 46:e70109. [PMID: 40251879 PMCID: PMC12008714 DOI: 10.1002/jcc.70109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2024] [Revised: 03/19/2025] [Accepted: 04/05/2025] [Indexed: 04/21/2025]
Abstract
Molecular properties in combined quantum mechanics and molecular mechanics (QM/MM) simulations have been shown to be dependent on the size of the quantum mechanical (QM) region and the amount of conformational sampling. Previous studies have largely focused on enzymatic systems, which have made it difficult to distinguish the effects of QM region size and conformational sampling from other factors including QM-MM boundary artifacts and the boundary effects. This study uses the difference-based adaptive solvation QM/MM method to investigate the tautomerization reactions of alanine and aspartate in explicit solvent. The choice of computationally tractable systems enables the decoupling of QM region size effects from other factors and a direct comparison of free energy surfaces with potential energy surfaces (PESs). The results show that (1) it is crucial to properly account for thermal fluctuations along the reaction pathways, and (2) free energy surfaces converge rapidly with increasing QM region size, whereas charge transfer requires a slightly larger QM region to achieve convergence. These findings are expected to guide future studies of enzymatic systems and other complex systems where QM/MM methods are applied.
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Affiliation(s)
- Holden Paz
- School of ScienceUniversity of WollongongWollongongAustralia
- Molecular HorizonsUniversity of WollongongWollongongAustralia
- ARC Centre of Excellence in Quantum BiotechnologyUniversity of WollongongWollongongAustralia
| | - Silvan Beck
- School of ScienceUniversity of WollongongWollongongAustralia
- Institute of Biochemistry, Chair of Biochemistry and Molecular MedicineFriedrich‐Alexander‐Universität Erlangen‐NürnbergErlangenGermany
| | - Richmond Lee
- School of ScienceUniversity of WollongongWollongongAustralia
- Molecular HorizonsUniversity of WollongongWollongongAustralia
| | - Junming Ho
- School of ChemistryThe University of New South WalesSydneyAustralia
| | - Haibo Yu
- School of ScienceUniversity of WollongongWollongongAustralia
- Molecular HorizonsUniversity of WollongongWollongongAustralia
- ARC Centre of Excellence in Quantum BiotechnologyUniversity of WollongongWollongongAustralia
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2
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Bonfrate S, Park W, Trejo‐Zamora D, Ferré N, Ho Choi C, Huix‐Rotllant M. Assessment of Free Energies From Electrostatic Embedding Density Functional Tight Binding-Based/Molecular Mechanics in Periodic Boundary Conditions. J Comput Chem 2025; 46:e70107. [PMID: 40260846 PMCID: PMC12012883 DOI: 10.1002/jcc.70107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2025] [Revised: 03/26/2025] [Accepted: 04/02/2025] [Indexed: 04/24/2025]
Abstract
Electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) methods in periodic boundary conditions (PBC) can successfully describe the condensed phase reactivity of a fragment treated at the QM level with an atomistic description of an electrostatic environment treated at the MM level. The computational cost of ab initio QM methods limits the phase space sampling, thus affecting statistical quantities like free energies. Here, we describe the implementation of a PBC-adapted QM/MM model based on the semi-empirical density-functional based tight-binding (DFTB) method within the GAMESS-US quantum package interfaced with Tinker. Further, we take advantage of the free energy methods provided by a newly developed interface with the PLUMED plugin. The versatility of the implementation is illustrated by the prediction of the free energy profile for three different families of reactions in solution. Overall, using the DFTB/MM, it has been possible to obtain results that are at least in a qualitatively agreement with respect to the experimental data or high-level ab initio simulations.
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Affiliation(s)
| | - Woojin Park
- Aix Marseille Univ, CNRS, ICRMarseilleFrance
- Department of ChemistryKyungpook National UniversityDaeguSouth Korea
| | - Dulce Trejo‐Zamora
- Aix Marseille Univ, CNRS, ICRMarseilleFrance
- Université Toulouse III: Paul SabatierToulouseFrance
| | | | - Cheol Ho Choi
- Department of ChemistryKyungpook National UniversityDaeguSouth Korea
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3
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Yagi K, Gunst K, Shiozaki T, Sugita Y. High-performance QM/MM Enhanced Sampling Molecular Dynamics Simulations with GENESIS SPDYN and QSimulate-QM. J Chem Theory Comput 2025; 21:4016-4029. [PMID: 40174884 PMCID: PMC12020374 DOI: 10.1021/acs.jctc.5c00163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2025] [Revised: 03/10/2025] [Accepted: 03/12/2025] [Indexed: 04/04/2025]
Abstract
A new module for quantum mechanical/molecular mechanical (QM/MM) calculations is implemented in a molecular dynamics (MD) program, SPDYN in GENESIS, interfaced with an electronic structure program, QSimulate-QM. The periodic boundary condition (PBC) in QM/MM simulation is incorporated via QM calculation in real space with duplicated MM charges and particle mesh Ewald (PME) calculation with QM and MM charges. A highly optimized code is implemented in QSimulate-QM, particularly for the density functional tight-binding (DFTB) method, where the interaction between the QM and MM regions is computed utilizing multipole expansions. Together with highly parallelized algorithms in SPDYN, the developed program performs MD simulations based on DFTB in the QM size of ∼100 atoms and MM of ∼100,000 atoms with a better performance than 1 ns/day using one computer node. This feature paves the way for QM/MM-MD enhanced sampling simulations. Various enhanced sampling methods in GENESIS, namely, generalized replica exchange solute tempering (gREST), replica-exchange umbrella sampling (REUS), and path sampling with the string method, are demonstrated at the QM/MM level to compute the Ramachandran plot of alanine dipeptide and the potential of mean force (PMF) of a proton transfer reaction in an enzyme.
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Affiliation(s)
- Kiyoshi Yagi
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Department
of Chemistry, Institute of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571, Japan
| | - Klaas Gunst
- Quantum
Simulation Technologies, Inc.,Boston, Massachusetts 02135, United States
| | - Toru Shiozaki
- Quantum
Simulation Technologies, Inc.,Boston, Massachusetts 02135, United States
| | - Yuji Sugita
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, 7-1-26 minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Laboratory
for Biomolecular Function Simulation, RIKEN
Center for Biosystems Dynamics Research, 1-6-5 minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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4
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Arattu Thodika A, Pan X, Shao Y, Nam K. Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions. J Chem Theory Comput 2025; 21:817-832. [PMID: 39815393 PMCID: PMC11781312 DOI: 10.1021/acs.jctc.4c01487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Revised: 12/30/2024] [Accepted: 01/03/2025] [Indexed: 01/18/2025]
Abstract
Integrating machine learning potentials (MLPs) with quantum mechanical/molecular mechanical (QM/MM) free energy simulations has emerged as a powerful approach for studying enzymatic catalysis. However, its practical application has been hindered by the time-consuming process of generating the necessary training, validation, and test data for MLP models through QM/MM simulations. Furthermore, the entire process needs to be repeated for each specific enzyme system and reaction. To overcome this bottleneck, it is required that trained MLPs exhibit transferability across different enzyme environments and reacting species, thereby eliminating the need for retraining with each new enzyme variant. In this study, we explore this potential by evaluating the transferability of a pretrained ΔMLP model across different enzyme mutations within the MM environment using the QM/MM-based ML architecture developed by Pan, X. J. Chem. Theory Comput. 2021, 17(9), 5745-5758. The study includes scenarios such as single point substitutions, a homologous enzyme from different species, and even a transition to an aqueous environment, where the last two systems have MM environment that is substantially different from that used in MLP training. The results show that the ΔMLP model effectively captures and predicts the effects of enzyme mutations on electrostatic interactions, producing reliable free energy profiles of enzyme-catalyzed reactions without the need for retraining. The study also identified notable limitations in transferability, particularly when transitioning from enzyme to water-rich MM environments. Overall, this study demonstrates the robustness of the Pan et al.'s QM/MM-based ML architecture for application to diverse enzyme systems, as well as the need for further research and the development of more sophisticated MLP models and training methods.
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Affiliation(s)
- Abdul
Raafik Arattu Thodika
- Department
of Chemistry and Biochemistry, University
of Texas at Arlington, Arlington, Texas 76019, United States
| | - Xiaoliang Pan
- Department
of Chemistry and Biochemistry, University
of Oklahoma, Norman, Oklahoma 73019, United States
| | - Yihan Shao
- Department
of Chemistry and Biochemistry, University
of Oklahoma, Norman, Oklahoma 73019, United States
| | - Kwangho Nam
- Department
of Chemistry and Biochemistry, University
of Texas at Arlington, Arlington, Texas 76019, United States
- Division
of Data Science, University of Texas at
Arlington, Arlington, Texas 76019, United States
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5
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Van R, Pan X, Rostami S, Liu J, Agarwal PK, Brooks B, Rajan R, Shao Y. Exploring CRISPR-Cas9 HNH-Domain-Catalyzed DNA Cleavage Using Accelerated Quantum Mechanical Molecular Mechanical Free Energy Simulation. Biochemistry 2025; 64:289-299. [PMID: 39680038 PMCID: PMC12005057 DOI: 10.1021/acs.biochem.4c00651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2024]
Abstract
The target DNA (tDNA) cleavage catalyzed by the CRISPR Cas9 enzyme is a critical step in the Cas9-based genome editing technologies. Previously, the tDNA cleavage from an active SpyCas9 enzyme conformation was modeled by Palermo and co-workers (Nierzwicki et al., Nat. Catal. 2022 5, 912) using ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulations, where the free energy barrier was found to be more favorable than that from a pseudoactive enzyme conformation. In this work, we performed ai-QM/MM simulations based on another catalytically active conformation (PDB 7Z4J) of the Cas9 HNH domain from cryo-electron microscopy experiments. For the wildtype enzyme, we acquired a free energy profile for the tDNA cleavage that is largely consistent with the previous report. Furthermore, we explored the role of the active-site K866 residue on the catalytic efficiency by modeling the K866A mutant and found that the K866A mutation increased the reaction free energy barrier, which is consistent with the experimentally observed reduction in the enzyme activity.
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Affiliation(s)
- Richard Van
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, OK 73019, United States
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Xiaoliang Pan
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, OK 73019, United States
| | - Saadi Rostami
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, OK 73019, United States
| | - Jin Liu
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, TX 76107, United States
| | - Pratul K. Agarwal
- High Performance Computing Center, Oklahoma State University, 106 Math Sciences, Stillwater, OK 74078, United States
| | - Bernard Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Rakhi Rajan
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, OK 73019, United States
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, OK 73019, United States
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6
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Jung J, Yagi K, Tan C, Oshima H, Mori T, Yu I, Matsunaga Y, Kobayashi C, Ito S, Ugarte La Torre D, Sugita Y. GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models. J Phys Chem B 2024; 128:6028-6048. [PMID: 38876465 PMCID: PMC11215777 DOI: 10.1021/acs.jpcb.4c02096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/16/2024]
Abstract
GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.
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Affiliation(s)
- Jaewoon Jung
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
| | - Kiyoshi Yagi
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
| | - Cheng Tan
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
| | - Hiraku Oshima
- Laboratory
for Biomolecular Function Simulation, RIKEN
Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
- Graduate
School of Life Science, University of Hyogo, Harima Science Park City, Hyogo 678-1297, Japan
| | - Takaharu Mori
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
- Department
of Chemistry, Tokyo University of Science, Shinjuku-ku, Tokyo 162-8601, Japan
| | - Isseki Yu
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
- Department
of Bioinformatics, Maebashi Institute of
Technology, Maebashi, Gunma 371-0816, Japan
| | - Yasuhiro Matsunaga
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
- Graduate
School of Science and Engineering, Saitama
University, Saitama 338-8570, Japan
| | - Chigusa Kobayashi
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
| | - Shingo Ito
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
| | - Diego Ugarte La Torre
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, Kobe, Hyogo 650-0047, Japan
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, Wako, Saitama 351-0198, Japan
- Laboratory
for Biomolecular Function Simulation, RIKEN
Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
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7
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Zheng J, Frisch MJ. Multiple-time scale integration method based on an interpolated potential energy surface for ab initio path integral molecular dynamics. J Chem Phys 2024; 160:144111. [PMID: 38597307 DOI: 10.1063/5.0196634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/25/2024] [Indexed: 04/11/2024] Open
Abstract
A new multiple-time scale integration method is presented that propagates ab initio path integral molecular dynamics (PIMD). This method uses a large time step to generate an approximate geometrical configuration whose energy and gradient are evaluated at the level of an ab initio method, and then, a more precise integration scheme, e.g., the Bulirsch-Stoer method or velocity Verlet integration with a smaller time step, is used to integrate from the previous step using the computationally efficient interpolated potential energy surface constructed from two consecutive points. This method makes the integration of PIMD more efficient and accurate compared with the velocity Verlet integration. A Nosé-Hoover chain thermostat combined with this new multiple-time scale method has good energy conservation even with a large time step, which is usually challenging in velocity Verlet integration for PIMD due to the very small chain mass when a large number of beads are used. The new method is used to calculate infrared spectra and free energy profiles to demonstrate its accuracy and capabilities.
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Affiliation(s)
- Jingjing Zheng
- Gaussian, Inc., 340 Quinnipiac St. Bldg. 40, Wallingford, Connecticut 06492, USA
| | - Michael J Frisch
- Gaussian, Inc., 340 Quinnipiac St. Bldg. 40, Wallingford, Connecticut 06492, USA
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8
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Nam K, Shao Y, Major DT, Wolf-Watz M. Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development. ACS OMEGA 2024; 9:7393-7412. [PMID: 38405524 PMCID: PMC10883025 DOI: 10.1021/acsomega.3c09084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/15/2024] [Accepted: 01/19/2024] [Indexed: 02/27/2024]
Abstract
Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.
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Affiliation(s)
- Kwangho Nam
- Department
of Chemistry and Biochemistry, University
of Texas at Arlington, Arlington, Texas 76019, United States
| | - Yihan Shao
- Department
of Chemistry and Biochemistry, University
of Oklahoma, Norman, Oklahoma 73019-5251, United States
| | - Dan T. Major
- Department
of Chemistry and Institute for Nanotechnology & Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel
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9
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McFarlane NR, Harvey JN. Exploration of biochemical reactivity with a QM/MM growing string method. Phys Chem Chem Phys 2024; 26:5999-6007. [PMID: 38293892 DOI: 10.1039/d3cp05772k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
In this work, we have implemented the single-ended growing string method using a hybrid internal/Cartesian coordinate scheme within our in-house QM/MM package, QoMMMa, representing the first implementation of the growing string method in the QM/MM framework. The goal of the implementation was to facilitate generation of QM/MM reaction pathways with minimal user input, and also to improve the quality of the pathways generated as compared to the widely used adiabatic mapping approach. We have validated the algorithm against a reaction which has been studied extensively in previous computational investigations - the Claisen rearrangement catalysed by chorismate mutase. The nature of the transition state and the height of the barrier was predicted well using our algorithm, where more than 88% of the pathways generated were deemed to be of production quality. Directly compared to using adiabatic mapping, we found that while our QM/MM single-ended growing string method is slightly less efficient, it readily produces reaction pathways with fewer discontinuites and thus minimises the need for involved remapping of unsatisfactory energy profiles.
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Affiliation(s)
- Neil R McFarlane
- Department of Chemistry, KU Leuven, B-3001 Leuven, Celestijnenlaan 200f, 2404, Belgium.
| | - Jeremy N Harvey
- Department of Chemistry, KU Leuven, B-3001 Leuven, Celestijnenlaan 200f, 2404, Belgium.
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10
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Yuan Y, Cui Q. Accurate and Efficient Multilevel Free Energy Simulations with Neural Network-Assisted Enhanced Sampling. J Chem Theory Comput 2023; 19:5394-5406. [PMID: 37527495 PMCID: PMC10810721 DOI: 10.1021/acs.jctc.3c00591] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
Free energy differences (ΔF) are essential to quantitative characterization and understanding of chemical and biological processes. Their direct estimation with an accurate quantum mechanical potential is of great interest and yet impractical due to high computational cost and incompatibility with typical alchemical free energy protocols. One promising solution is the multilevel free energy simulation in which the estimate of ΔF at an inexpensive low level of theory is combined with the correction toward a higher level of theory. The poor configurational overlap generally expected between the two levels of theory, however, presents a major challenge. We overcome this challenge by using a deep neural network model and enhanced sampling simulations. An adversarial autoencoder is used to identify a low-dimensional (latent) space that compactly represents the degrees of freedom that encode the distinct distributions at the two levels of theory. Enhanced sampling in this latent space is then used to drive the sampling of configurations that predominantly contribute to the free energy correction. Results for both gas phase and condensed phase systems demonstrate that this data-driven approach offers high accuracy and efficiency with great potential for scalability to complex systems.
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Affiliation(s)
- Yuchen Yuan
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Qiang Cui
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
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11
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Snyder R, Kim B, Pan X, Shao Y, Pu J. Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation. J Chem Phys 2023; 159:054107. [PMID: 37530109 PMCID: PMC10400118 DOI: 10.1063/5.0156327] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/10/2023] [Indexed: 08/03/2023] Open
Abstract
Free energy simulations that employ combined quantum mechanical and molecular mechanical (QM/MM) potentials at ab initio QM (AI) levels are computationally highly demanding. Here, we present a machine-learning-facilitated approach for obtaining AI/MM-quality free energy profiles at the cost of efficient semiempirical QM/MM (SE/MM) methods. Specifically, we use Gaussian process regression (GPR) to learn the potential energy corrections needed for an SE/MM level to match an AI/MM target along the minimum free energy path (MFEP). Force modification using gradients of the GPR potential allows us to improve configurational sampling and update the MFEP. To adaptively train our model, we further employ the sparse variational GP (SVGP) and streaming sparse GPR (SSGPR) methods, which efficiently incorporate previous sample information without significantly increasing the training data size. We applied the QM-(SS)GPR/MM method to the solution-phase SN2 Menshutkin reaction, NH3+CH3Cl→CH3NH3++Cl-, using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. For 4000 configurations sampled along the MFEP, the iteratively optimized AM1-SSGPR-4/MM model reduces the energy error in AM1/MM from 18.2 to 4.4 kcal/mol. Although not explicitly fitting forces, our method also reduces the key internal force errors from 25.5 to 11.1 kcal/mol/Å and from 30.2 to 10.3 kcal/mol/Å for the N-C and C-Cl bonds, respectively. Compared to the uncorrected simulations, the AM1-SSGPR-4/MM method lowers the predicted free energy barrier from 28.7 to 11.7 kcal/mol and decreases the reaction free energy from -12.4 to -41.9 kcal/mol, bringing these results into closer agreement with their AI/MM and experimental benchmarks.
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Affiliation(s)
- Ryan Snyder
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, 402 N Blackford St., Indianapolis, Indiana 46202, USA
| | - Bryant Kim
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, 402 N Blackford St., Indianapolis, Indiana 46202, USA
| | - Xiaoliang Pan
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, Oklahoma 73019, USA
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, Oklahoma 73019, USA
| | - Jingzhi Pu
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, 402 N Blackford St., Indianapolis, Indiana 46202, USA
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