1
|
Pederson JP, McDaniel JG. PyDFT-QMMM: A modular, extensible software framework for DFT-based QM/MM molecular dynamics. J Chem Phys 2024; 161:034103. [PMID: 39007371 DOI: 10.1063/5.0219851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
PyDFT-QMMM is a Python-based package for performing hybrid quantum mechanics/molecular mechanics (QM/MM) simulations at the density functional level of theory. The program is designed to treat short-range and long-range interactions through user-specified combinations of electrostatic and mechanical embedding procedures within periodic simulation domains, providing necessary interfaces to external quantum chemistry and molecular dynamics software. To enable direct embedding of long-range electrostatics in periodic systems, we have derived and implemented force terms for our previously described QM/MM/PME approach [Pederson and McDaniel, J. Chem. Phys. 156, 174105 (2022)]. Communication with external software packages Psi4 and OpenMM is facilitated through Python application programming interfaces (APIs). The core library contains basic utilities for running QM/MM molecular dynamics simulations, and plug-in entry-points are provided for users to implement custom energy/force calculation and integration routines, within an extensible architecture. The user interacts with PyDFT-QMMM primarily through its Python API, allowing for complex workflow development with Python scripting, for example, interfacing with PLUMED for free energy simulations. We provide benchmarks of forces and energy conservation for the QM/MM/PME and alternative QM/MM electrostatic embedding approaches. We further demonstrate a simple example use case for water solute in a water solvent system, for which radial distribution functions are computed from 100 ps QM/MM simulations; in this example, we highlight how the solvation structure is sensitive to different basis-set choices due to under- or over-polarization of the QM water molecule's electron density.
Collapse
Affiliation(s)
- John P Pederson
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA
| | - Jesse G McDaniel
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA
| |
Collapse
|
2
|
Giese TJ, Zeng J, Lerew L, McCarthy E, Tao Y, Ekesan Ş, York DM. Software Infrastructure for Next-Generation QM/MM-ΔMLP Force Fields. J Phys Chem B 2024; 128:6257-6271. [PMID: 38905451 DOI: 10.1021/acs.jpcb.4c01466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2024]
Abstract
We present software infrastructure for the design and testing of new quantum mechanical/molecular mechanical and machine-learning potential (QM/MM-ΔMLP) force fields for a wide range of applications. The software integrates Amber's molecular dynamics simulation capabilities with fast, approximate quantum models in the xtb package and machine-learning potential corrections in DeePMD-kit. The xtb package implements the recently developed density-functional tight-binding QM models with multipolar electrostatics and density-dependent dispersion (GFN2-xTB), and the interface with Amber enables their use in periodic boundary QM/MM simulations with linear-scaling QM/MM particle-mesh Ewald electrostatics. The accuracy of the semiempirical models is enhanced by including machine-learning correction potentials (ΔMLPs) enabled through an interface with the DeePMD-kit software. The goal of this paper is to present and validate the implementation of this software infrastructure in molecular dynamics and free energy simulations. The utility of the new infrastructure is demonstrated in proof-of-concept example applications. The software elements presented here are open source and freely available. Their interface provides a powerful enabling technology for the design of new QM/MM-ΔMLP models for studying a wide range of problems, including biomolecular reactivity and protein-ligand binding.
Collapse
Affiliation(s)
- Timothy J Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Lauren Lerew
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Erika McCarthy
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Yujun Tao
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Şölen Ekesan
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| |
Collapse
|
3
|
Tao Y, Giese TJ, Ekesan Ş, Zeng J, Aradi B, Hourahine B, Aktulga HM, Götz AW, Merz KM, York DM. Amber free energy tools: Interoperable software for free energy simulations using generalized quantum mechanical/molecular mechanical and machine learning potentials. J Chem Phys 2024; 160:224104. [PMID: 38856060 DOI: 10.1063/5.0211276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 05/15/2024] [Indexed: 06/11/2024] Open
Abstract
We report the development and testing of new integrated cyberinfrastructure for performing free energy simulations with generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and machine learning potentials (MLPs) in Amber. The Sander molecular dynamics program has been extended to leverage fast, density-functional tight-binding models implemented in the DFTB+ and xTB packages, and an interface to the DeePMD-kit software enables the use of MLPs. The software is integrated through application program interfaces that circumvent the need to perform "system calls" and enable the incorporation of long-range Ewald electrostatics into the external software's self-consistent field procedure. The infrastructure provides access to QM/MM models that may serve as the foundation for QM/MM-ΔMLP potentials, which supplement the semiempirical QM/MM model with a MLP correction trained to reproduce ab initio QM/MM energies and forces. Efficient optimization of minimum free energy pathways is enabled through a new surface-accelerated finite-temperature string method implemented in the FE-ToolKit package. Furthermore, we interfaced Sander with the i-PI software by implementing the socket communication protocol used in the i-PI client-server model. The new interface with i-PI allows for the treatment of nuclear quantum effects with semiempirical QM/MM-ΔMLP models. The modular interoperable software is demonstrated on proton transfer reactions in guanine-thymine mispairs in a B-form deoxyribonucleic acid helix. The current work represents a considerable advance in the development of modular software for performing free energy simulations of chemical reactions that are important in a wide range of applications.
Collapse
Affiliation(s)
- Yujun Tao
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Timothy J Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Şölen Ekesan
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Bálint Aradi
- Bremen Center for Computational Materials Science, University of Bremen, D-28334 Bremen, Germany
| | - Ben Hourahine
- SUPA, Department of Physics, University of Strathclyde, Glasgow G4 0NG, United Kingdom
| | - Hasan Metin Aktulga
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, USA
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| |
Collapse
|
4
|
Tao Y, Giese TJ, York DM. Electronic and Nuclear Quantum Effects on Proton Transfer Reactions of Guanine-Thymine (G-T) Mispairs Using Combined Quantum Mechanical/Molecular Mechanical and Machine Learning Potentials. Molecules 2024; 29:2703. [PMID: 38893576 PMCID: PMC11173453 DOI: 10.3390/molecules29112703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 05/30/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
Rare tautomeric forms of nucleobases can lead to Watson-Crick-like (WC-like) mispairs in DNA, but the process of proton transfer is fast and difficult to detect experimentally. NMR studies show evidence for the existence of short-time WC-like guanine-thymine (G-T) mispairs; however, the mechanism of proton transfer and the degree to which nuclear quantum effects play a role are unclear. We use a B-DNA helix exhibiting a wGT mispair as a model system to study tautomerization reactions. We perform ab initio (PBE0/6-31G*) quantum mechanical/molecular mechanical (QM/MM) simulations to examine the free energy surface for tautomerization. We demonstrate that while the ab initio QM/MM simulations are accurate, considerable sampling is required to achieve high precision in the free energy barriers. To address this problem, we develop a QM/MM machine learning potential correction (QM/MM-ΔMLP) that is able to improve the computational efficiency, greatly extend the accessible time scales of the simulations, and enable practical application of path integral molecular dynamics to examine nuclear quantum effects. We find that the inclusion of nuclear quantum effects has only a modest effect on the mechanistic pathway but leads to a considerable lowering of the free energy barrier for the GT*⇌G*T equilibrium. Our results enable a rationalization of observed experimental data and the prediction of populations of rare tautomeric forms of nucleobases and rates of their interconversion in B-DNA.
Collapse
|
5
|
Bonfrate S, Ferré N, Huix-Rotllant M. Analytic Gradients for the Electrostatic Embedding QM/MM Model in Periodic Boundary Conditions Using Particle-Mesh Ewald Sums and Electrostatic Potential Fitted Charge Operators. J Chem Theory Comput 2024; 20:4338-4349. [PMID: 38712506 DOI: 10.1021/acs.jctc.4c00201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Long-range electrostatic effects are fundamental for describing chemical reactivity in the condensed phase. Here, we present the methodology of an efficient quantum mechanical/molecular mechanical (QM/MM) model in periodic boundary conditions (PBC) compatible with QM/MM boundaries at chemical bonds. The method combines electrostatic potential fitted charge operators and electrostatic potentials derived from the smooth particle-mesh Ewald (PME) sum approach. The total energy and its analytic first derivatives with respect to QM, MM, and lattice vectors allow QM/MM molecular dynamics (MD) in the most common thermodynamic ensembles. We demonstrate the robustness of the method by performing a QM/MM MD equilibration of methanol in water. We simulate the cis/trans isomerization free-energy profiles in water of proline amino acid and a proline-containing oligopeptide, showing a correct description of the reaction barrier. Our PBC-compatible QM/MM model can efficiently be used to study the chemical reactivity in the condensed phase and enzymatic catalysis.
Collapse
Affiliation(s)
| | - Nicolas Ferré
- Aix-Marseille Univ, CNRS, ICR, Marseille 13013, France
| | | |
Collapse
|
6
|
Case D, Aktulga HM, Belfon K, Cerutti DS, Cisneros GA, Cruzeiro VD, Forouzesh N, Giese TJ, Götz AW, Gohlke H, Izadi S, Kasavajhala K, Kaymak MC, King E, Kurtzman T, Lee TS, Li P, Liu J, Luchko T, Luo R, Manathunga M, Machado MR, Nguyen HM, O’Hearn KA, Onufriev AV, Pan F, Pantano S, Qi R, Rahnamoun A, Risheh A, Schott-Verdugo S, Shajan A, Swails J, Wang J, Wei H, Wu X, Wu Y, Zhang S, Zhao S, Zhu Q, Cheatham TE, Roe DR, Roitberg A, Simmerling C, York DM, Nagan MC, Merz KM. AmberTools. J Chem Inf Model 2023; 63:6183-6191. [PMID: 37805934 PMCID: PMC10598796 DOI: 10.1021/acs.jcim.3c01153] [Citation(s) in RCA: 175] [Impact Index Per Article: 175.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Indexed: 10/10/2023]
Abstract
AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.
Collapse
Affiliation(s)
- David
A. Case
- Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway 08854, New Jersey, United States
| | - Hasan Metin Aktulga
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Kellon Belfon
- FOG
Pharmaceuticals Inc., Cambridge 02140, Massachusetts, United States
| | - David S. Cerutti
- Psivant, 451 D Street, Suite 205, Boston 02210, Massachusetts, United States
| | - G. Andrés Cisneros
- Department
of Physics, Department of Chemistry and Biochemistry, University of Texas at Dallas, Richardson 75801, Texas, United States
| | - Vinícius
Wilian D. Cruzeiro
- Department
of Chemistry and The PULSE Institute, Stanford
University, Stanford 94305, California, United States
| | - Negin Forouzesh
- Department
of Computer Science, California State University, Los Angeles 90032, California, United States
| | - Timothy J. Giese
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Andreas W. Götz
- San
Diego Supercomputer Center, University of
California San Diego, La Jolla 92093-0505, California, United States
| | - Holger Gohlke
- Institute
for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute
of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Saeed Izadi
- Pharmaceutical
Development, Genentech, Inc., South San Francisco 94080, California, United
States
| | - Koushik Kasavajhala
- Laufer
Center for Physical and Quantitative Biology, Department of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Mehmet C. Kaymak
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Edward King
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Tom Kurtzman
- Ph.D.
Programs in Chemistry, Biochemistry, and Biology, The Graduate Center of the City University of New York, 365 Fifth Avenue, New York 10016, New York, United States
- Department
of Chemistry, Lehman College, 250 Bedford Park Blvd West, Bronx 10468, New York, United States
| | - Tai-Sung Lee
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Pengfei Li
- Department
of Chemistry and Biochemistry, Loyola University
Chicago, Chicago 60660, Illinois, United States
| | - Jian Liu
- Beijing
National Laboratory for Molecular Sciences, Institute of Theoretical
and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Tyler Luchko
- Department
of Physics and Astronomy, California State
University, Northridge, Northridge 91330, California, United States
| | - Ray Luo
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Madushanka Manathunga
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
| | | | - Hai Minh Nguyen
- Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway 08854, New Jersey, United States
| | - Kurt A. O’Hearn
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Alexey V. Onufriev
- Departments
of Computer Science and Physics, Virginia
Tech, Blacksburg 24061, Virginia, United
States
| | - Feng Pan
- Department
of Statistics, Florida State University, Tallahassee 32304, Florida, United States
| | - Sergio Pantano
- Institut Pasteur de Montevideo, Montevideo 11400, Uruguay
| | - Ruxi Qi
- Cryo-EM
Center, Southern University of Science and
Technology, Shenzhen 518055, China
| | - Ali Rahnamoun
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Ali Risheh
- Department
of Computer Science, California State University, Los Angeles 90032, California, United States
| | - Stephan Schott-Verdugo
- Institute
of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Akhil Shajan
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
| | - Jason Swails
- Entos, 4470 W Sunset
Blvd, Suite 107, Los Angeles 90027, California, United States
| | - Junmei Wang
- Department
of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh 15261, Pennsylvania, United States
| | - Haixin Wei
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Xiongwu Wu
- Laboratory
of Computational Biology, NHLBI, NIH, Bethesda 20892, Maryland, United States
| | - Yongxian Wu
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Shi Zhang
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Shiji Zhao
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
- Nurix Therapeutics, Inc., San Francisco 94158, California, United States
| | - Qiang Zhu
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Thomas E. Cheatham
- Department
of Medicinal Chemistry, The University of
Utah, 30 South 2000 East, Salt Lake City 84112, Utah, United
States
| | - Daniel R. Roe
- Laboratory
of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda 20892, Maryland, United States
| | - Adrian Roitberg
- Department
of Chemistry, The University of Florida, 440 Leigh Hall, Gainesville 32611-7200, Florida, United States
| | - Carlos Simmerling
- Laufer
Center for Physical and Quantitative Biology, Department of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Darrin M. York
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Maria C. Nagan
- Department
of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Kenneth M. Merz
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
| |
Collapse
|
7
|
Zeng J, Zhang D, Lu D, Mo P, Li Z, Chen Y, Rynik M, Huang L, Li Z, Shi S, Wang Y, Ye H, Tuo P, Yang J, Ding Y, Li Y, Tisi D, Zeng Q, Bao H, Xia Y, Huang J, Muraoka K, Wang Y, Chang J, Yuan F, Bore SL, Cai C, Lin Y, Wang B, Xu J, Zhu JX, Luo C, Zhang Y, Goodall REA, Liang W, Singh AK, Yao S, Zhang J, Wentzcovitch R, Han J, Liu J, Jia W, York DM, E W, Car R, Zhang L, Wang H. DeePMD-kit v2: A software package for deep potential models. J Chem Phys 2023; 159:054801. [PMID: 37526163 PMCID: PMC10445636 DOI: 10.1063/5.0155600] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/03/2023] [Indexed: 08/02/2023] Open
Abstract
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.
Collapse
Affiliation(s)
- Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | | | - Denghui Lu
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, People’s Republic of China
| | - Pinghui Mo
- College of Electrical and Information Engineering, Hunan University, Changsha, People’s Republic of China
| | - Zeyu Li
- Yuanpei College, Peking University, Beijing 100871, People’s Republic of China
| | - Yixiao Chen
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08540, USA
| | - Marián Rynik
- Department of Experimental Physics, Comenius University, Mlynská Dolina F2, 842 48 Bratislava, Slovakia
| | - Li’ang Huang
- Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
| | | | - Shaochen Shi
- ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
| | | | - Haotian Ye
- Yuanpei College, Peking University, Beijing 100871, People’s Republic of China
| | - Ping Tuo
- AI for Science Institute, Beijing 100080, People’s Republic of China
| | - Jiabin Yang
- Baidu, Inc., Beijing, People’s Republic of China
| | | | - Yifan Li
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | | | - Qiyu Zeng
- Department of Physics, National University of Defense Technology, Changsha, Hunan 410073, People’s Republic of China
| | | | - Yu Xia
- ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
| | | | - Koki Muraoka
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Yibo Wang
- DP Technology, Beijing 100080, People’s Republic of China
| | | | - Fengbo Yuan
- DP Technology, Beijing 100080, People’s Republic of China
| | - Sigbjørn Løland Bore
- Hylleraas Centre for Quantum Molecular Sciences and Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, 0315 Oslo, Norway
| | | | - Yinnian Lin
- Wangxuan Institute of Computer Technology, Peking University, Beijing 100871, People’s Republic of China
| | - Bo Wang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry and Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, People’s Republic of China
| | - Jiayan Xu
- School of Chemistry and Chemical Engineering, Queen’s University Belfast, Belfast BT9 5AG, United Kingdom
| | - Jia-Xin Zhu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People’s Republic of China
| | - Chenxing Luo
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
| | - Yuzhi Zhang
- DP Technology, Beijing 100080, People’s Republic of China
| | | | - Wenshuo Liang
- DP Technology, Beijing 100080, People’s Republic of China
| | - Anurag Kumar Singh
- Department of Data Science, Indian Institute of Technology, Palakkad, Kerala, India
| | - Sikai Yao
- DP Technology, Beijing 100080, People’s Republic of China
| | - Jingchao Zhang
- NVIDIA AI Technology Center (NVAITC), Santa Clara, California 95051, USA
| | | | - Jiequn Han
- Center for Computational Mathematics, Flatiron Institute, New York, New York 10010, USA
| | - Jie Liu
- College of Electrical and Information Engineering, Hunan University, Changsha, People’s Republic of China
| | | | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | | | - Roberto Car
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | | | - Han Wang
- Author to whom correspondence should be addressed:
| |
Collapse
|
8
|
McCarthy E, Ekesan Ş, Giese TJ, Wilson TJ, Deng J, Huang L, Lilley DJ, York DM. Catalytic mechanism and pH dependence of a methyltransferase ribozyme (MTR1) from computational enzymology. Nucleic Acids Res 2023; 51:4508-4518. [PMID: 37070188 PMCID: PMC10201425 DOI: 10.1093/nar/gkad260] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/09/2023] [Accepted: 04/10/2023] [Indexed: 04/19/2023] Open
Abstract
A methyltransferase ribozyme (MTR1) was selected in vitro to catalyze alkyl transfer from exogenous O6-methylguanine (O6mG) to a target adenine N1, and recently, high-resolution crystal structures have become available. We use a combination of classical molecular dynamics, ab initio quantum mechanical/molecular mechanical (QM/MM) and alchemical free energy (AFE) simulations to elucidate the atomic-level solution mechanism of MTR1. Simulations identify an active reactant state involving protonation of C10 that hydrogen bonds with O6mG:N1. The deduced mechanism involves a stepwise mechanism with two transition states corresponding to proton transfer from C10:N3 to O6mG:N1 and rate-controlling methyl transfer (19.4 kcal·mol-1 barrier). AFE simulations predict the pKa for C10 to be 6.3, close to the experimental apparent pKa of 6.2, further implicating it as a critical general acid. The intrinsic rate derived from QM/MM simulations, together with pKa calculations, enables us to predict an activity-pH profile that agrees well with experiment. The insights gained provide further support for a putative RNA world and establish new design principles for RNA-based biochemical tools.
Collapse
Affiliation(s)
- Erika McCarthy
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Şölen Ekesan
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Timothy J Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Timothy J Wilson
- Nucleic Acid Structure Research Group, MSI/WTB Complex, The University of Dundee, Dundee, Dow Street, Dundee DD1 5EH, UK
| | - Jie Deng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong–Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
- Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Lin Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong–Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
- Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - David M J Lilley
- Nucleic Acid Structure Research Group, MSI/WTB Complex, The University of Dundee, Dundee, Dow Street, Dundee DD1 5EH, UK
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| |
Collapse
|
9
|
Zeng J, Tao Y, Giese TJ, York DM. QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery. J Chem Theory Comput 2023; 19:1261-1275. [PMID: 36696673 PMCID: PMC9992268 DOI: 10.1021/acs.jctc.2c01172] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
We report QDπ-v1.0 for modeling the internal energy of drug molecules containing H, C, N, and O atoms. The QDπ model is in the form of a quantum mechanical/machine learning potential correction (QM/Δ-MLP) that uses a fast third-order self-consistent density-functional tight-binding (DFTB3/3OB) model that is corrected to a quantitatively high-level of accuracy through a deep-learning potential (DeepPot-SE). The model has the advantage that it is able to properly treat electrostatic interactions and handle changes in charge/protonation states. The model is trained against reference data computed at the ωB97X/6-31G* level (as in the ANI-1x data set) and compared to several other approximate semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3, MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QDπ model is demonstrated to be accurate for a wide range of intra- and intermolecular interactions (despite its intended use as an internal energy model) and has shown to perform exceptionally well for relative protonation/deprotonation energies and tautomers. An example application to model reactions involved in RNA strand cleavage catalyzed by protein and nucleic acid enzymes illustrates QDπ has average errors less than 0.5 kcal/mol, whereas the other models compared have errors over an order of magnitude greater. Taken together, this makes QDπ highly attractive as a potential force field model for drug discovery.
Collapse
Affiliation(s)
- Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Yujun Tao
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| |
Collapse
|
10
|
Csizi K, Reiher M. Universal
QM
/
MM
approaches for general nanoscale applications. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
| | - Markus Reiher
- Laboratorium für Physikalische Chemie ETH Zürich Zürich Switzerland
| |
Collapse
|
11
|
Cruzeiro VWD, Wang Y, Pieri E, Hohenstein EG, Martínez TJ. TeraChem protocol buffers (TCPB): Accelerating QM and QM/MM simulations with a client-server model. J Chem Phys 2023; 158:044801. [PMID: 36725506 DOI: 10.1063/5.0130886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
The routine use of electronic structures in many chemical simulation applications calls for efficient and easy ways to access electronic structure programs. We describe how the graphics processing unit (GPU) accelerated electronic structure program TeraChem can be set up as an electronic structure server, to be easily accessed by third-party client programs. We exploit Google's protocol buffer framework for data serialization and communication. The client interface, called TeraChem protocol buffers (TCPB), has been designed for ease of use and compatibility with multiple programming languages, such as C++, Fortran, and Python. To demonstrate the ease of coupling third-party programs with electronic structures using TCPB, we have incorporated the TCPB client into Amber for quantum mechanics/molecular mechanics (QM/MM) simulations. The TCPB interface saves time with GPU initialization and I/O operations, achieving a speedup of more than 2× compared to a prior file-based implementation for a QM region with ∼250 basis functions. We demonstrate the practical application of TCPB by computing the free energy profile of p-hydroxybenzylidene-2,3-dimethylimidazolinone (p-HBDI-)-a model chromophore in green fluorescent proteins-on the first excited singlet state using Hamiltonian replica exchange for enhanced sampling. All calculations in this work have been performed with the non-commercial freely-available version of TeraChem, which is sufficient for many QM region sizes in common use.
Collapse
Affiliation(s)
| | - Yuanheng Wang
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, USA
| | - Elisa Pieri
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, USA
| | - Edward G Hohenstein
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, USA
| | - Todd J Martínez
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, USA
| |
Collapse
|
12
|
Bonfrate S, Ferré N, Huix-Rotllant M. An efficient electrostatic embedding QM/MM method using periodic boundary conditions based on particle-mesh Ewald sums and electrostatic potential fitted charge operators. J Chem Phys 2023; 158:021101. [PMID: 36641406 DOI: 10.1063/5.0133646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Hybrid quantum mechanics/molecular mechanics (QM/MM) models are successful at describing the properties and reactivity of biological macromolecules. Combining ab initio QM/MM methods and periodic boundary conditions (PBC) is currently the optimal approach for modeling chemical processes in an infinite environment, but frequently, these models are too time-consuming for general applicability to biological systems in a solution. Here, we define a simple and efficient electrostatic embedding QM/MM model in PBC, combining the benefits of electrostatic potential fitted atomic charges and particle-mesh Ewald sums, which can efficiently treat systems of an arbitrary size at a reasonable computational cost. To illustrate this, we apply our scheme to extract the lowest singlet excitation energies from a model for Arabidopsis thaliana cryptochrome 1 containing circa 93 000 atoms, accurately reproducing the experimental absorption maximum.
Collapse
Affiliation(s)
| | - Nicolas Ferré
- Aix-Marseille University, CNRS, ICR, Marseille, France
| | | |
Collapse
|
13
|
Giese TJ, Zeng J, York DM. Multireference Generalization of the Weighted Thermodynamic Perturbation Method. J Phys Chem A 2022; 126:8519-8533. [PMID: 36301936 PMCID: PMC9771595 DOI: 10.1021/acs.jpca.2c06201] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We describe the generalized weighted thermodynamic perturbation (gwTP) method for estimating the free energy surface of an expensive "high-level" potential energy function from the umbrella sampling performed with multiple inexpensive "low-level" reference potentials. The gwTP method is a generalization of the weighted thermodynamic perturbation (wTP) method developed by Li and co-workers [J. Chem. Theory Comput. 2018, 14, 5583-5596] that uses a single "low-level" reference potential. The gwTP method offers new possibilities in model design whereby the sampling generated from several low-level potentials may be combined (e.g., specific reaction parameter models that might have variable accuracy at different stages of a multistep reaction). The gwTP method is especially well suited for use with machine learning potentials (MLPs) that are trained against computationally expensive ab initio quantum mechanical/molecular mechanical (QM/MM) energies and forces using active learning procedures that naturally produce multiple distinct neural network potentials. Simulations can be performed with greater sampling using the fast MLPs and then corrected to the ab initio level using gwTP. The capabilities of the gwTP method are demonstrated by creating reference potentials based on the MNDO/d and DFTB2/MIO semiempirical models supplemented with the "range-corrected deep potential" (DPRc). The DPRc parameters are trained to ab initio QM/MM data, and the potentials are used to calculate the free energy surface of stepwise mechanisms for nonenzymatic RNA 2'-O-transesterification model reactions. The extended sampling made possible by the reference potentials allows one to identify unequilibrated portions of the simulations that are not always evident from the short time scale commonly used with ab initio QM/MM potentials. We show that the reference potential approach can yield more accurate ab initio free energy predictions than the wTP method or what can be reasonably afforded from explicit ab initio QM/MM sampling.
Collapse
Affiliation(s)
- Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| |
Collapse
|
14
|
Ekesan Ş, York DM. Who stole the proton? Suspect general base guanine found with a smoking gun in the pistol ribozyme. Org Biomol Chem 2022; 20:6219-6230. [PMID: 35452066 PMCID: PMC9378597 DOI: 10.1039/d2ob00234e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The pistol ribozyme (Psr) is one among the most recently discovered classes of small nucleolytic ribozymes that catalyze site-specific RNA self-cleavage through 2'-O-transphosphorylation. The Psr contains a conserved guanine (G40) that in crystal structures is in a position suggesting it plays the role of the general base to abstract a proton from the nucleophile to activate the reaction. Although some functional data is consistent with this mechanistic role, a notable exception is 2-aminopurine (2AP) substitution which has no effect on the rate, unlike similar substitutions across other so-called "G + M" and "G + A" ribozyme classes. Herein we postulate that an alternate conserved guanine, G42, is the primary general base, and provide evidence from molecular simulations that the active site of Psr can undergo local refolding into a structure that is consistent with the common "L-platform/L-scaffold" architecture identified in G + M and G + A ribozyme classes with Psr currently the notable exception. We summarize the key currently available experimental data and present new classical and combined quantum mechanical/molecular mechanical simulation results that collectively suggest a new hypothesis. We hypothesize that there are two available catalytic pathways supported by different conformational states connected by a local refolding of the active site: (1) a primary pathway with an active site architecture aligned with the L-platform/L-scaffold framework where G42 acts as a general base, and (2) a secondary pathway with the crystallographic active site architecture where G40 acts as a general base. We go on to make several experimentally testable predictions, and suggest specific experiments that would ultimately bring closure to the mystery as to "who stole the proton in the pistol ribozyme?".
Collapse
Affiliation(s)
- Şölen Ekesan
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA.
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA.
| |
Collapse
|
15
|
Giese TJ, Zeng J, Ekesan Ş, York DM. Combined QM/MM, Machine Learning Path Integral Approach to Compute Free Energy Profiles and Kinetic Isotope Effects in RNA Cleavage Reactions. J Chem Theory Comput 2022; 18:4304-4317. [PMID: 35709391 PMCID: PMC9283286 DOI: 10.1021/acs.jctc.2c00151] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We present a fast, accurate, and robust approach for determination of free energy profiles and kinetic isotope effects for RNA 2'-O-transphosphorylation reactions with inclusion of nuclear quantum effects. We apply a deep potential range correction (DPRc) for combined quantum mechanical/molecular mechanical (QM/MM) simulations of reactions in the condensed phase. The method uses the second-order density-functional tight-binding method (DFTB2) as a fast, approximate base QM model. The DPRc model modifies the DFTB2 QM interactions and applies short-range corrections to the QM/MM interactions to reproduce ab initio DFT (PBE0/6-31G*) QM/MM energies and forces. The DPRc thus enables both QM and QM/MM interactions to be tuned to high accuracy, and the QM/MM corrections are designed to smoothly vanish at a specified cutoff boundary (6 Å in the present work). The computational speed-up afforded by the QM/MM+DPRc model enables free energy profiles to be calculated that include rigorous long-range QM/MM interactions under periodic boundary conditions and nuclear quantum effects through a path integral approach using a new interface between the AMBER and i-PI software. The approach is demonstrated through the calculation of free energy profiles of a native RNA cleavage model reaction and reactions involving thio-substitutions, which are important experimental probes of the mechanism. The DFTB2+DPRc QM/MM free energy surfaces agree very closely with the PBE0/6-31G* QM/MM results, and it is vastly superior to the DFTB2 QM/MM surfaces with and without weighted thermodynamic perturbation corrections. 18O and 34S primary kinetic isotope effects are compared, and the influence of nuclear quantum effects on the free energy profiles is examined.
Collapse
Affiliation(s)
- Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Şölen Ekesan
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| |
Collapse
|
16
|
Manathunga M, Götz AW, Merz KM. Computer-aided drug design, quantum-mechanical methods for biological problems. Curr Opin Struct Biol 2022; 75:102417. [PMID: 35779437 DOI: 10.1016/j.sbi.2022.102417] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/14/2022] [Accepted: 05/16/2022] [Indexed: 11/28/2022]
Abstract
Quantum chemistry enables to study systems with chemical accuracy (<1 kcal/mol from experiment) but is restricted to a handful of atoms due to its computational expense. This has led to ongoing interest to optimize and simplify these methods while retaining accuracy. Implementing quantum mechanical (QM) methods on modern hardware such as multiple-GPUs is one example of how the field is optimizing performance. Multiscale approaches like the so-called QM/molecular mechanical method are gaining popularity in drug discovery because they focus the application of QM methods on the region of choice (e.g., the binding site), while using efficient MM models to represent less relevant areas. The creation of simplified QM methods is another example, including the use of machine learning to create ultra-fast and accurate QM models. Herein, we summarize recent advancements in the development of optimized QM methods that enhance our ability to use these methods in computer aided drug discovery.
Collapse
Affiliation(s)
- Madushanka Manathunga
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, United States. https://twitter.com/@MaduManathunga
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, United States. https://twitter.com/@awgoetz
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, United States.
| |
Collapse
|
17
|
Pederson JP, McDaniel J. DFT-based QM/MM with Particle-Mesh Ewald for Direct, Long-Range Electrostatic Embedding. J Chem Phys 2022; 156:174105. [DOI: 10.1063/5.0087386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We present a DFT-based, QM/MM implementation with long-range electrostatic embedding achieved by direct real-space integration of the particle mesh Ewald (PME) computed electrostatic potential. The key transformation is the interpolation of the electrostatic potential from the PME grid to the DFT quadrature grid, from which integrals are easily evaluated utilizing standard DFT machinery. We provide benchmarks of the numerical accuracy with choice of grid size and real-space corrections, and demonstrate that good convergence is achieved while introducing nominal computational overhead. Furthermore, the approach requires only small modification to existing software packages, as is demonstrated with our implementation in the OpenMM and Psi4 software. After presenting convergence benchmarks, we evaluate the importance of long-range electrostatic embedding in three solute/solvent systems modeled with QM/MM. Water and BMIM/BF4 ionic liquid were considered as ``simple' and ``complex' solvents respectively, with water and p-phenylenediamine (PPD) solute molecules treated at QM level of theory. While electrostatic embedding with standard real-space truncation may introduce negligible error for simple systems such as water solute in water solvent, errors become more significant when QM/MM is applied to complex solvents such as ionic liquids. An extreme example is the electrostatic embedding energy for oxidized PPD in BMIM/BF4 for which real-space truncation produces severe error even at 2-3 nm cutoff distances. This latter example illustrates that utilization of QM/MM to compute redox potentials within concentrated electrolytes/ionic media requires carefully chosen long-range electrostatic embedding algorithms, with our presented algorithm providing a general and robust approach.
Collapse
Affiliation(s)
| | - Jesse McDaniel
- Chemistry, Georgia Institute of Technology, United States of America
| |
Collapse
|
18
|
Zeng J, Giese TJ, Ekesan Ş, York DM. Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution. J Chem Theory Comput 2021; 17:6993-7009. [PMID: 34644071 DOI: 10.1021/acs.jctc.1c00201] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
We develop a new deep potential─range correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase. The new range correction enables short-ranged QM/MM interactions to be tuned for higher accuracy, and the correction smoothly vanishes within a specified cutoff. We further develop an active learning procedure for robust neural network training. We test the DPRc model and training procedure against a series of six nonenzymatic phosphoryl transfer reactions in solution that are important in mechanistic studies of RNA-cleaving enzymes. Specifically, we apply DPRc corrections to a base QM model and test its ability to reproduce free-energy profiles generated from a target QM model. We perform these comparisons using the MNDO/d and DFTB2 semiempirical models because they differ in the way they treat orbital orthogonalization and electrostatics and produce free-energy profiles which differ significantly from each other, thereby providing us a rigorous stress test for the DPRc model and training procedure. The comparisons show that accurate reproduction of the free-energy profiles requires correction of the QM/MM interactions out to 6 Å. We further find that the model's initial training benefits from generating data from temperature replica exchange simulations and including high-temperature configurations into the fitting procedure, so the resulting models are trained to properly avoid high-energy regions. A single DPRc model was trained to reproduce four different reactions and yielded good agreement with the free-energy profiles made from the target QM/MM simulations. The DPRc model was further demonstrated to be transferable to 2D free-energy surfaces and 1D free-energy profiles that were not explicitly considered in the training. Examination of the computational performance of the DPRc model showed that it was fairly slow when run on CPUs but was sped up almost 100-fold when using NVIDIA V100 GPUs, resulting in almost negligible overhead. The new DPRc model and training procedure provide a potentially powerful new tool for the creation of next-generation QM/MM potentials for a wide spectrum of free-energy applications ranging from drug discovery to enzyme design.
Collapse
Affiliation(s)
- Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine, and Department of Chemistry and Chemical Biology, Rutgers the State University of New Jersey, New Brunswick, New Jersey 08901-8554, United States
| | - Timothy J Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine, and Department of Chemistry and Chemical Biology, Rutgers the State University of New Jersey, New Brunswick, New Jersey 08901-8554, United States
| | - Şölen Ekesan
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine, and Department of Chemistry and Chemical Biology, Rutgers the State University of New Jersey, New Brunswick, New Jersey 08901-8554, United States
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine, and Department of Chemistry and Chemical Biology, Rutgers the State University of New Jersey, New Brunswick, New Jersey 08901-8554, United States
| |
Collapse
|
19
|
Nochebuena J, Naseem-Khan S, Cisneros GA. Development and application of quantum mechanics/molecular mechanics methods with advanced polarizable potentials. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2021; 11:e1515. [PMID: 34367343 PMCID: PMC8341087 DOI: 10.1002/wcms.1515] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 12/19/2020] [Indexed: 01/02/2023]
Abstract
Quantum mechanics/molecular mechanics (QM/MM) simulations are a popular approach to study various features of large systems. A common application of QM/MM calculations is in the investigation of reaction mechanisms in condensed-phase and biological systems. The combination of QM and MM methods to represent a system gives rise to several challenges that need to be addressed. The increase in computational speed has allowed the expanded use of more complicated and accurate methods for both QM and MM simulations. Here, we review some approaches that address several common challenges encountered in QM/MM simulations with advanced polarizable potentials, from methods to account for boundary across covalent bonds and long-range effects, to polarization and advanced embedding potentials.
Collapse
Affiliation(s)
- Jorge Nochebuena
- Department of Chemistry, University of North Texas, Denton, Texas, USA
| | - Sehr Naseem-Khan
- Department of Chemistry, University of North Texas, Denton, Texas, USA
| | - G Andrés Cisneros
- Department of Chemistry, University of North Texas, Denton, Texas, USA
| |
Collapse
|
20
|
Giese TJ, Ekesan Ş, York DM. Extension of the Variational Free Energy Profile and Multistate Bennett Acceptance Ratio Methods for High-Dimensional Potential of Mean Force Profile Analysis. J Phys Chem A 2021; 125:4216-4232. [PMID: 33784093 DOI: 10.1021/acs.jpca.1c00736] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
We redevelop the variational free energy profile (vFEP) method using a cardinal B-spline basis to extend the method for analyzing free energy surfaces (FESs) involving three or more reaction coordinates. We also implemented software for evaluating high-dimensional profiles based on the multistate Bennett acceptance ratio (MBAR) method which constructs an unbiased probability density from global reweighting of the observed samples. The MBAR method takes advantage of a fast algorithm for solving the unbinned weighted histogram (UWHAM)/MBAR equations which replaces the solution of simultaneous equations with a nonlinear optimization of a convex function. We make use of cardinal B-splines and multiquadric radial basis functions to obtain smooth, differentiable MBAR profiles in arbitrary high dimensions. The cardinal B-spline vFEP and MBAR methods are compared using three example systems that examine 1D, 2D, and 3D profiles. Both methods are found to be useful and produce nearly indistinguishable results. The vFEP method is found to be 150 times faster than MBAR when applied to periodic 2D profiles, but the MBAR method is 4.5 times faster than vFEP when evaluating unbounded 3D profiles. In agreement with previous comparisons, we find the vFEP method produces superior FESs when the overlap between umbrella window simulations decreases. Finally, the associative reaction mechanism of hammerhead ribozyme is characterized using 3D, 4D, and 6D profiles, and the higher-dimensional profiles are found to have smaller reaction barriers by as much as 1.5 kcal/mol. The methods presented here have been implemented into the FE-ToolKit software package along with new methods for network-wide free energy analysis in drug discovery.
Collapse
Affiliation(s)
- Timothy J Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
| | - Şölen Ekesan
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
| |
Collapse
|
21
|
Yagi K, Ito S, Sugita Y. Exploring the Minimum-Energy Pathways and Free-Energy Profiles of Enzymatic Reactions with QM/MM Calculations. J Phys Chem B 2021; 125:4701-4713. [PMID: 33914537 PMCID: PMC10986901 DOI: 10.1021/acs.jpcb.1c01862] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Understanding molecular mechanisms of enzymatic reactions is of vital importance in biochemistry and biophysics. Here, we introduce new functions of hybrid quantum mechanical/molecular mechanical (QM/MM) calculations in the GENESIS program to compute the minimum-energy pathways (MEPs) and free-energy profiles of enzymatic reactions. For this purpose, an interface in GENESIS is developed to utilize a highly parallel electronic structure program, QSimulate-QM (https://qsimulate.com), calling it as a shared library from GENESIS. Second, algorithms to search the MEP are implemented, combining the string method (E et al. J. Chem. Phys. 2007, 126, 164103) with the energy minimization of the buffer MM region. The method implemented in GENESIS is applied to an enzyme, triosephosphate isomerase, which converts dihyroxyacetone phosphate to glyceraldehyde 3-phosphate in four proton-transfer processes. QM/MM-molecular dynamics simulations show performances of greater than 1 ns/day with the density functional tight binding (DFTB), and 10-30 ps/day with the hybrid density functional theory, B3LYP-D3. These performances allow us to compute not only MEP but also the potential of mean force (PMF) of the enzymatic reactions using the QM/MM calculations. The barrier height obtained as 13 kcal mol-1 with B3LYP-D3 in the QM/MM calculation is in agreement with the experimental results. The impact of conformational sampling in PMF calculations and the level of electronic structure calculations (DFTB vs B3LYP-D3) suggests reliable computational protocols for enzymatic reactions without high computational costs.
Collapse
Affiliation(s)
- Kiyoshi Yagi
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Shingo Ito
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - 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
| |
Collapse
|
22
|
Cruzeiro VWD, Manathunga M, Merz KM, Götz AW. Open-Source Multi-GPU-Accelerated QM/MM Simulations with AMBER and QUICK. J Chem Inf Model 2021; 61:2109-2115. [PMID: 33913331 DOI: 10.1021/acs.jcim.1c00169] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The quantum mechanics/molecular mechanics (QM/MM) approach is an essential and well-established tool in computational chemistry that has been widely applied in a myriad of biomolecular problems in the literature. In this publication, we report the integration of the QUantum Interaction Computational Kernel (QUICK) program as an engine to perform electronic structure calculations in QM/MM simulations with AMBER. This integration is available through either a file-based interface (FBI) or an application programming interface (API). Since QUICK is an open-source GPU-accelerated code with multi-GPU parallelization, users can take advantage of "free of charge" GPU-acceleration in their QM/MM simulations. In this work, we discuss implementation details and give usage examples. We also investigate energy conservation in typical QM/MM simulations performed at the microcanonical ensemble. Finally, benchmark results for two representative systems in bulk water, the N-methylacetamide (NMA) molecule and the photoactive yellow protein (PYP), show the performance of QM/MM simulations with QUICK and AMBER using a varying number of CPU cores and GPUs. Our results highlight the acceleration obtained from a single or multiple GPUs; we observed speedups of up to 53× between a single GPU vs a single CPU core and of up to 2.6× when comparing four GPUs to a single GPU. Results also reveal speedups of up to 3.5× when the API is used instead of FBI.
Collapse
Affiliation(s)
- Vinícius Wilian D Cruzeiro
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, United States.,Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Madushanka Manathunga
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute of Cyber-Enabled Research, Michigan State University, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute of Cyber-Enabled Research, Michigan State University, East Lansing, Michigan 48824, United States
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, United States
| |
Collapse
|
23
|
Watanabe HC, Yamada M, Suzuki Y. Proton transfer in bulk water using the full adaptive QM/MM method: integration of solute- and solvent-adaptive approaches. Phys Chem Chem Phys 2021; 23:8344-8360. [PMID: 33875999 DOI: 10.1039/d1cp00116g] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The quantum mechanical/molecular mechanical (QM/MM) method is a hybrid molecular simulation technique that increases the accessibility of local electronic structures of large systems. The technique combines the benefit of accuracy found in the QM method and that of cost efficiency found in the MM method. However, it is difficult to directly apply the QM/MM method to the dynamics of solution systems, particularly for proton transfer. As explained in the Grotthuss mechanism, proton transfer is a structural interconversion between hydronium ions and solvent water molecules. Hence, when the QM/MM method is applied, an adaptive treatment, namely on-the-fly revisions on molecular definitions, is required for both the solute and solvent. Although several solvent-adaptive methods have been proposed, a full adaptive framework, which is an approach that also considers adaptation for solutes, remains untapped. In this paper, we propose a new numerical expression for the coordinates of the excess proton and its control algorithm. Furthermore, we confirm that this method can stably and accurately simulate proton transfer dynamics in bulk water.
Collapse
Affiliation(s)
- Hiroshi C Watanabe
- Quantum Computing Center, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
| | | | | |
Collapse
|
24
|
Pan X, Nam K, Epifanovsky E, Simmonett AC, Rosta E, Shao Y. A simplified charge projection scheme for long-range electrostatics in ab initio QM/MM calculations. J Chem Phys 2021; 154:024115. [PMID: 33445891 DOI: 10.1063/5.0038120] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
In a previous work [Pan et al., Molecules 23, 2500 (2018)], a charge projection scheme was reported, where outer molecular mechanical (MM) charges [>10 Å from the quantum mechanical (QM) region] were projected onto the electrostatic potential (ESP) grid of the QM region to accurately and efficiently capture long-range electrostatics in ab initio QM/MM calculations. Here, a further simplification to the model is proposed, where the outer MM charges are projected onto inner MM atom positions (instead of ESP grid positions). This enables a representation of the long-range MM electrostatic potential via augmentary charges (AC) on inner MM atoms. Combined with the long-range electrostatic correction function from Cisneros et al. [J. Chem. Phys. 143, 044103 (2015)] to smoothly switch between inner and outer MM regions, this new QM/MM-AC electrostatic model yields accurate and continuous ab initio QM/MM electrostatic energies with a 10 Å cutoff between inner and outer MM regions. This model enables efficient QM/MM cluster calculations with a large number of MM atoms as well as QM/MM calculations with periodic boundary conditions.
Collapse
Affiliation(s)
- Xiaoliang Pan
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, Oklahoma 73019, USA
| | - Kwangho Nam
- Department of Chemistry and Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, USA
| | - Evgeny Epifanovsky
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | - Andrew C Simmonett
- National Institutes of Health-National Heart, Lung and Blood Institute, Laboratory of Computational Biology, Bethesda, Maryland 20892, USA
| | - Edina Rosta
- Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, Oklahoma 73019, USA
| |
Collapse
|
25
|
Ricard TC, Iyengar SS. Efficient and Accurate Approach To Estimate Hybrid Functional and Large Basis-Set Contributions to Condensed-Phase Systems and Molecule–Surface Interactions. J Chem Theory Comput 2020; 16:4790-4812. [DOI: 10.1021/acs.jctc.9b01089] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Timothy C. Ricard
- Department of Chemistry and Department of Physics, Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
| | - Srinivasan S. Iyengar
- Department of Chemistry and Department of Physics, Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
| |
Collapse
|
26
|
He Y, Lei J, Pan X, Huang X, Zhao Y. The hydrolytic water molecule of Class A β-lactamase relies on the acyl-enzyme intermediate ES* for proper coordination and catalysis. Sci Rep 2020; 10:10205. [PMID: 32576842 PMCID: PMC7311446 DOI: 10.1038/s41598-020-66431-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 01/06/2020] [Indexed: 11/16/2022] Open
Abstract
Serine-based β-lactamases of Class A, C and D all rely on a key water molecule to hydrolyze and inactivate β-lactam antibiotics. This process involves two conserved catalytic steps. In the first acylation step, the β-lactam antibiotic forms an acyl-enzyme intermediate (ES*) with the catalytic serine residue. In the second deacylation step, an activated water molecule serves as nucleophile (WAT_Nu) to attack ES* and release the inactivated β-lactam. The coordination and activation of WAT_Nu is not fully understood. Using time-resolved x-ray crystallography and QM/MM simulations, we analyzed three intermediate structures of Class A β-lactamase PenP as it slowly hydrolyzed cephaloridine. WAT_Nu is centrally located in the apo structure but becomes slightly displaced away by ES* in the post-acylation structure. In the deacylation structure, WAT_Nu moves back and is positioned along the Bürgi–Dunitz trajectory with favorable energetic profile to attack ES*. Unexpectedly, WAT_Nu is also found to adopt a catalytically incompetent conformation in the deacylation structure forming a hydrogen bond with ES*. Our results reveal that ES* plays a significant role in coordinating and activating WAT_Nu through subtle yet distinct interactions at different stages of the catalytic process. These interactions may serve as potential targets to circumvent β-lactamase-mediated antibiotic resistance.
Collapse
Affiliation(s)
- Yunjiao He
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, P. R. China.,Department of Applied Biology and Chemical Technology, State Key Laboratory of Chemical Biology and Drug Discovery, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P. R. China
| | - Jinping Lei
- Department of Chemistry, Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, P. R. China.,School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, P. R. China
| | - Xuehua Pan
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, P. R. China.,Department of Applied Biology and Chemical Technology, State Key Laboratory of Chemical Biology and Drug Discovery, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P. R. China
| | - Xuhui Huang
- Department of Chemistry, Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, P. R. China.
| | - Yanxiang Zhao
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, P. R. China. .,Department of Applied Biology and Chemical Technology, State Key Laboratory of Chemical Biology and Drug Discovery, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P. R. China.
| |
Collapse
|
27
|
Jones LO, Mosquera MA, Schatz GC, Ratner MA. Embedding Methods for Quantum Chemistry: Applications from Materials to Life Sciences. J Am Chem Soc 2020; 142:3281-3295. [PMID: 31986877 DOI: 10.1021/jacs.9b10780] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Quantum mechanical embedding methods hold the promise to transform not just the way calculations are performed, but to significantly reduce computational costs and improve scaling for macro-molecular systems containing hundreds if not thousands of atoms. The field of embedding has grown increasingly broad with many approaches of different intersecting flavors. In this perspective, we lay out the methods into two streams: QM:MM and QM:QM, showcasing the advantages and disadvantages of both. We provide a review of the literature, the underpinning theories including our contributions, and we highlight current applications with select examples spanning both materials and life sciences. We conclude with prospects and future outlook on embedding, and our view on the use of universal test case scenarios for cross-comparisons of the many available (and future) embedding theories.
Collapse
Affiliation(s)
- Leighton O Jones
- Department of Chemistry , Northwestern University , Evanston , Illinois 60208 , United States
| | - Martín A Mosquera
- Department of Chemistry , Northwestern University , Evanston , Illinois 60208 , United States
| | - George C Schatz
- Department of Chemistry , Northwestern University , Evanston , Illinois 60208 , United States
| | - Mark A Ratner
- Department of Chemistry , Northwestern University , Evanston , Illinois 60208 , United States
| |
Collapse
|
28
|
Bondanza M, Nottoli M, Cupellini L, Lipparini F, Mennucci B. Polarizable embedding QM/MM: the future gold standard for complex (bio)systems? Phys Chem Chem Phys 2020; 22:14433-14448. [DOI: 10.1039/d0cp02119a] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We provide a perspective of the induced dipole formulation of polarizable QM/MM, showing how efficient implementations will enable their application to the modeling of dynamics, spectroscopy, and reactivity in complex biosystems.
Collapse
Affiliation(s)
- Mattia Bondanza
- Dipartimento di Chimica e Chimica Industriale
- Università di Pisa
- I-56124 Pisa
- Italy
| | - Michele Nottoli
- Dipartimento di Chimica e Chimica Industriale
- Università di Pisa
- I-56124 Pisa
- Italy
| | - Lorenzo Cupellini
- Dipartimento di Chimica e Chimica Industriale
- Università di Pisa
- I-56124 Pisa
- Italy
| | - Filippo Lipparini
- Dipartimento di Chimica e Chimica Industriale
- Università di Pisa
- I-56124 Pisa
- Italy
| | - Benedetta Mennucci
- Dipartimento di Chimica e Chimica Industriale
- Università di Pisa
- I-56124 Pisa
- Italy
| |
Collapse
|
29
|
Giese TJ, York DM. Development of a Robust Indirect Approach for MM → QM Free Energy Calculations That Combines Force-Matched Reference Potential and Bennett's Acceptance Ratio Methods. J Chem Theory Comput 2019; 15:5543-5562. [PMID: 31507179 DOI: 10.1021/acs.jctc.9b00401] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
We use the PBE0/6-31G* density functional method to perform ab initio quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) simulations under periodic boundary conditions with rigorous electrostatics using the ambient potential composite Ewald method in order to test the convergence of MM → QM/MM free energy corrections for the prediction of 17 small-molecule solvation free energies and eight ligand binding free energies to T4 lysozyme. The "indirect" thermodynamic cycle for calculating free energies is used to explore whether a series of reference potentials improve the statistical quality of the predictions. Specifically, we construct a series of reference potentials that optimize a molecular mechanical (MM) force field's parameters to reproduce the ab initio QM/MM forces from a QM/MM simulation. The optimizations form a systematic progression of successively expanded parameters that include bond, angle, dihedral, and charge parameters. For each reference potential, we calculate benchmark quality reference values for the MM → QM/MM correction by performing the mixed MM and QM/MM Hamiltonians at 11 intermediate states, each for 200 ps. We then compare forward and reverse application of Zwanzig's relation, thermodynamic integration (TI), and Bennett's acceptance ratio (BAR) methods as a function of reference potential, simulation time, and the number of simulated intermediate states. We find that Zwanzig's equation is inadequate unless a large number of intermediate states are explicitly simulated. The TI and BAR mean signed errors are very small even when only the end-state simulations are considered, and the standard deviations of the TI and BAR errors are decreased by choosing a reference potential that optimizes the bond and angle parameters. We find a robust approach for the data sets of fairly rigid molecules considered here is to use bond + angle reference potential together with the end-state-only BAR analysis. This requires QM/MM simulations to be performed in order to generate reference data to parametrize the bond + angle reference potential, and then this same simulation serves a dual purpose as the full QM/MM end state. The convergence of the results with respect to time suggests that computational resources may be used more efficiently by running multiple simulations for no more than 50 ps, rather than running one long simulation.
Collapse
Affiliation(s)
- Timothy J Giese
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology , Rutgers University , Piscataway , New Jersey 08854-8087 , United States
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology , Rutgers University , Piscataway , New Jersey 08854-8087 , United States
| |
Collapse
|
30
|
Gaines CS, Giese TJ, York DM. Cleaning Up Mechanistic Debris Generated by Twister Ribozymes Using Computational RNA Enzymology. ACS Catal 2019; 9:5803-5815. [PMID: 31328021 PMCID: PMC6641568 DOI: 10.1021/acscatal.9b01155] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The catalytic properties of RNA have been a subject of fascination and intense research since their discovery over 30 years ago. Very recently, several classes of nucleolytic ribozymes have emerged and been characterized structurally. Among these, the twister ribozyme has been center-stage, and a topic of debate about its architecture and mechanism owing to conflicting interpretations of different crystal structures, and in some cases conflicting interpretations of the same functional data. In the present work, we attempt to clean up the mechanistic "debris" generated by twister ribozymes using a comprehensive computational RNA enzymology approach aimed to provide a unified interpretation of existing structural and functional data. Simulations in the crystalline environment and in solution provide insight into the origins of observed differences in crystal structures, and coalesce on a common active site architecture, and dynamical ensemble in solution. We use GPU-accelerated free energy methods with enhanced sampling to ascertain microscopic nucleobase pK a values of the implicated general acid and base, from which predicted activity-pH profiles can be compared directly with experiments. Next, ab initio quantum mechanical/molecular mechanical (QM/MM) simulations with full dynamic solvation under periodic boundary conditions are used to determine mechanistic pathways through multi-dimensional free energy landscapes for the reaction. We then characterize the rate-controlling transition state, and make predictions about kinetic isotope effects and linear free energy relations. Computational mutagenesis is performed to explain the origin of rate effects caused by chemical modifications and make experimentally testable predictions. Finally, we provide evidence that helps to resolve conflicting issues related to the role of metal ions in catalysis. Throughout each stage, we highlight how a conserved L-platform structural motif, to- gether with a key L-anchor residue, forms the characteristic active site scaffold enabling each of the catalytic strategies to come together not only for the twister ribozyme, but the majority of the known small nucleolytic ribozyme classes.
Collapse
Affiliation(s)
- Colin S. Gaines
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| |
Collapse
|
31
|
Liu J, Sun H, Glover WJ, He X. Prediction of Excited-State Properties of Oligoacene Crystals Using Fragment-Based Quantum Mechanical Method. J Phys Chem A 2019; 123:5407-5417. [PMID: 31187994 DOI: 10.1021/acs.jpca.8b12552] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
A fundamental understanding of the excited-state properties of molecular crystals is of central importance for their optoelectronics applications. In this study, we developed the electrostatically embedded generalized molecular fractionation (EE-GMF) method for the quantitative characterization of the excited-state properties of locally excited molecular clusters. The accuracy of the EE-GMF method is systematically assessed for oligoacene crystals. Our result demonstrates that the EE-GMF method is capable of providing the lowest vertical singlet (S1) and triplet excitation energies (T1), in excellent agreement with the full-system quantum mechanical calculations. Using this method, we also investigated the performance of different density functionals in predicting the excited-state properties of the oligoacene crystals. Among the 13 tested functionals, B3LYP and MN15 give the two lowest overall mean unsigned errors with reference to the experimental S1 and T1 excitation energies. The EE-GMF approach can be readily utilized for studying the excited-state properties of large-scale organic solids at diverse ab initio levels.
Collapse
Affiliation(s)
- Jinfeng Liu
- Department of Basic Medicine and Clinical Pharmacy , China Pharmaceutical University , Nanjing 210009 , China
| | | | - William J Glover
- NYU Shanghai , Shanghai 200122 , China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai , Shanghai 200062 , China.,Department of Chemistry , New York University , New York , New York 10003 , United States
| | - Xiao He
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai , Shanghai 200062 , China
| |
Collapse
|
32
|
Li P, Liu F, Shao Y, Mei Y. Computational Insights into Endo/Exo Selectivity of the Diels–Alder Reaction in Explicit Solvent at Ab Initio Quantum Mechanical/Molecular Mechanical Level. J Phys Chem B 2019; 123:5131-5138. [DOI: 10.1021/acs.jpcb.9b01989] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Pengfei Li
- State Key Laboratory of Precision Spectroscopy, School of Physics and Materials Science, East China Normal University, Shanghai 200062, China
| | - Fengjiao Liu
- College of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Ye Mei
- State Key Laboratory of Precision Spectroscopy, School of Physics and Materials Science, East China Normal University, Shanghai 200062, China
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| |
Collapse
|
33
|
Holden ZC, Rana B, Herbert JM. Analytic gradient for the QM/MM-Ewald method using charges derived from the electrostatic potential: Theory, implementation, and application to ab initio molecular dynamics simulation of the aqueous electron. J Chem Phys 2019; 150:144115. [DOI: 10.1063/1.5089673] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Affiliation(s)
- Zachary C. Holden
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Bhaskar Rana
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - John M. Herbert
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| |
Collapse
|
34
|
Gökcan H, Vázquez-Montelongo EA, Cisneros GA. LICHEM 1.1: Recent Improvements and New Capabilities. J Chem Theory Comput 2019; 15:3056-3065. [PMID: 30908049 DOI: 10.1021/acs.jctc.9b00028] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The QM/MM method has become a useful tool to investigate various properties of complex systems. We previously introduced the layered interacting chemical models (LICHEM) package to enable QM/MM simulations with advanced potentials by combining various (unmodified) QM and MM codes ( J. Comp. Chem., 2016, 37, 1019). LICHEM provides several capabilities such as the ability to use polarizable force fields, such as AMOEBA, for the MM environment. Here, we describe an updated version of LICHEM (v1.1), which includes several new functionalities including a new method to account for long-range electrostatic effects in QM/MM (QM/MM-LREC), a new implementation for QM/MM with the Gaussian electrostatic model (GEM), and new capabilities for path optimizations using the quadratic string model (QSM) coupled with restrained MM environment optimization.
Collapse
Affiliation(s)
- Hatice Gökcan
- Department of Chemistry , University of North Texas , Denton , Texas 76201 , United States
| | | | - G Andrés Cisneros
- Department of Chemistry , University of North Texas , Denton , Texas 76201 , United States
| |
Collapse
|
35
|
Two symmetric arginine residues play distinct roles in Thermus thermophilus Argonaute DNA guide strand-mediated DNA target cleavage. Proc Natl Acad Sci U S A 2018; 116:845-853. [PMID: 30591565 DOI: 10.1073/pnas.1817041116] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Bacterium Thermus thermophilus Argonaute (Ago; TtAgo) is a prokaryotic Ago (pAgo) that acts as the host defense against the uptake and propagation of foreign DNA by catalyzing the DNA cleavage reaction. The TtAgo active site consists of a plugged-in glutamate finger with two arginine residues (R545 and R486) located symmetrically around it. An interesting challenge is to understand how they can collaboratively facilitate enzymatic catalysis. In Kluyveromyces polysporus Ago, a eukaryotic Ago, the evolutionarily symmetrical residues are arginine and histidine, both of which function to stabilize the plugged-in catalytic tetrad conformation. Surprisingly, our simulation results indicated that, in TtAgo, only R545 is involved in the cleavage reaction by serving as a critical structural anchor to stabilize the catalytic tetrad Asp-Glu-Asp-Asp that is completed by the insertion of the glutamate finger, whereas R486 is not involved in target cleavage. The TtAgo-mediated target DNA cleavage occurs in a substrate-assisted mechanism, in which the pro-Rp (Rp, a tetrahedral phosphorus center with "R-type" chirality) oxygen of scissile phosphate acts as a general base to activate the nucleophilic water. Our unexpected theoretical findings on distinct roles played by R545 and R486 in TtAgo catalysis have been validated by single-point site-mutagenesis experiments, wherein the target cleavage is abolished for all mutants of R545. In sharp contrast, the cleavage activity is maintained for all mutants of R486. Our work provides mechanistic insights on the catalytic specificity of Ago proteins and could facilitate the design of new gene-editing tools in the long term.
Collapse
|
36
|
Pan X, Rosta E, Shao Y. Representation of the QM Subsystem for Long-Range Electrostatic Interaction in Non-Periodic Ab Initio QM/MM Calculations. Molecules 2018; 23:E2500. [PMID: 30274290 PMCID: PMC6222767 DOI: 10.3390/molecules23102500] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Revised: 09/21/2018] [Accepted: 09/25/2018] [Indexed: 11/16/2022] Open
Abstract
In QM/MM calculations, it is essential to handle electrostatic interactions between the QM and MM subsystems accurately and efficiently. To achieve maximal efficiency, it is convenient to adopt a hybrid scheme, where the QM electron density is used explicitly in the evaluation of short-range QM/MM electrostatic interactions, while a multipolar representation for the QM electron density is employed to account for the long-range QM/MM electrostatic interactions. In order to avoid energy discontinuity at the cutoffs, which separate the short- and long-range QM/MM electrostatic interactions, a switching function should be utilized to ensure a smooth potential energy surface. In this study, we benchmarked the accuracy of such hybrid embedding schemes for QM/MM electrostatic interactions using different multipolar representations, switching functions and cutoff distances. For test systems (neutral and anionic oxyluciferin in MM (aqueous and enzyme) environments), the best accuracy was acquired with a combination of QM electrostatic potential (ESP) charges and dipoles and two switching functions (long-range electrostatic corrections (LREC) and Switch) in the treatment of long-range QM/MM electrostatics. It allowed us to apply a 10Å distance cutoff and still obtain QM/MM electrostatics/polarization energies within 0.1 kcal/mol and time-dependent density functional theory (TDDFT)/MM vertical excitation energies within 10-3 eV from theoretical reference values.
Collapse
Affiliation(s)
- Xiaoliang Pan
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK 73019⁻5251, USA.
| | - Edina Rosta
- Department of Chemistry, King's College London, London SE1 1DB, UK.
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK 73019⁻5251, USA.
| |
Collapse
|
37
|
Giese TJ, York DM. A GPU-Accelerated Parameter Interpolation Thermodynamic Integration Free Energy Method. J Chem Theory Comput 2018; 14:1564-1582. [PMID: 29357243 PMCID: PMC5849537 DOI: 10.1021/acs.jctc.7b01175] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
There has been a resurgence of interest in free energy methods motivated by the performance enhancements offered by molecular dynamics (MD) software written for specialized hardware, such as graphics processing units (GPUs). In this work, we exploit the properties of a parameter-interpolated thermodynamic integration (PI-TI) method to connect states by their molecular mechanical (MM) parameter values. This pathway is shown to be better behaved for Mg2+ → Ca2+ transformations than traditional linear alchemical pathways (with and without soft-core potentials). The PI-TI method has the practical advantage that no modification of the MD code is required to propagate the dynamics, and unlike with linear alchemical mixing, only one electrostatic evaluation is needed (e.g., single call to particle-mesh Ewald) leading to better performance. In the case of AMBER, this enables all the performance benefits of GPU-acceleration to be realized, in addition to unlocking the full spectrum of features available within the MD software, such as Hamiltonian replica exchange (HREM). The TI derivative evaluation can be accomplished efficiently in a post-processing step by reanalyzing the statistically independent trajectory frames in parallel for high throughput. We also show how one can evaluate the particle mesh Ewald contribution to the TI derivative evaluation without needing to perform two reciprocal space calculations. We apply the PI-TI method with HREM on GPUs in AMBER to predict p Ka values in double stranded RNA molecules and make comparison with experiments. Convergence to under 0.25 units for these systems required 100 ns or more of sampling per window and coupling of windows with HREM. We find that MM charges derived from ab initio QM/MM fragment calculations improve the agreement between calculation and experimental results.
Collapse
Affiliation(s)
- Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
| |
Collapse
|
38
|
Giese TJ, York DM. Quantum mechanical force fields for condensed phase molecular simulations. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2017; 29:383002. [PMID: 28817382 PMCID: PMC5821073 DOI: 10.1088/1361-648x/aa7c5c] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Molecular simulations are powerful tools for providing atomic-level details into complex chemical and physical processes that occur in the condensed phase. For strongly interacting systems where quantum many-body effects are known to play an important role, density-functional methods are often used to provide the model with the potential energy used to drive dynamics. These methods, however, suffer from two major drawbacks. First, they are often too computationally intensive to practically apply to large systems over long time scales, limiting their scope of application. Second, there remain challenges for these models to obtain the necessary level of accuracy for weak non-bonded interactions to obtain quantitative accuracy for a wide range of condensed phase properties. Quantum mechanical force fields (QMFFs) provide a potential solution to both of these limitations. In this review, we address recent advances in the development of QMFFs for condensed phase simulations. In particular, we examine the development of QMFF models using both approximate and ab initio density-functional models, the treatment of short-ranged non-bonded and long-ranged electrostatic interactions, and stability issues in molecular dynamics calculations. Example calculations are provided for crystalline systems, liquid water, and ionic liquids. We conclude with a perspective for emerging challenges and future research directions.
Collapse
|
39
|
Chen H, Giese TJ, Golden BL, York DM. Divalent Metal Ion Activation of a Guanine General Base in the Hammerhead Ribozyme: Insights from Molecular Simulations. Biochemistry 2017; 56:2985-2994. [PMID: 28530384 DOI: 10.1021/acs.biochem.6b01192] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The hammerhead ribozyme is a well-studied nucleolytic ribozyme that catalyzes the self-cleavage of the RNA phosphodiester backbone. Despite experimental and theoretical efforts, key questions remain about details of the mechanism with regard to the activation of the nucleophile by the putative general base guanine (G12). Straightforward interpretation of the measured activity-pH data implies the pKa value of the N1 position in the G12 nucleobase is significantly shifted by the ribozyme environment. Recent crystallographic and biochemical work has identified pH-dependent divalent metal ion binding at the N7/O6 position of G12, leading to the hypothesis that this binding mode could induce a pKa shift of G12 toward neutrality. We present computational results that support this hypothesis and provide a model that unifies the interpretation of available structural and biochemical data and paints a detailed mechanistic picture of the general base step of the reaction. Experimentally testable predictions are made for mutational and rescue effects on G12, which will give further insights into the catalytic mechanism. These results contribute to our growing knowledge of the potential roles of divalent metal ions in RNA catalysis.
Collapse
Affiliation(s)
- Haoyuan Chen
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry & Chemical Biology, Rutgers University , 174 Frelinghuysen Road, Piscataway, New Jersey 08854-8076, United States
| | - Timothy J Giese
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry & Chemical Biology, Rutgers University , 174 Frelinghuysen Road, Piscataway, New Jersey 08854-8076, United States
| | - Barbara L Golden
- Department of Biochemistry, Purdue University , West Lafayette, Indiana 47907, United States
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry & Chemical Biology, Rutgers University , 174 Frelinghuysen Road, Piscataway, New Jersey 08854-8076, United States
| |
Collapse
|
40
|
Golze D, Benedikter N, Iannuzzi M, Wilhelm J, Hutter J. Fast evaluation of solid harmonic Gaussian integrals for local resolution-of-the-identity methods and range-separated hybrid functionals. J Chem Phys 2017; 146:034105. [DOI: 10.1063/1.4973510] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Dorothea Golze
- Department of Chemistry, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Niels Benedikter
- QMath, Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 København, Denmark
| | - Marcella Iannuzzi
- Department of Chemistry, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Jan Wilhelm
- Department of Chemistry, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Jürg Hutter
- Department of Chemistry, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| |
Collapse
|
41
|
Kratz EG, Duke RE, Cisneros GA. Long-range electrostatic corrections in multipolar/polarizable QM/MM simulations. Theor Chem Acc 2016; 135:166. [PMID: 28367078 PMCID: PMC5373107 DOI: 10.1007/s00214-016-1923-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 06/04/2016] [Indexed: 10/21/2022]
Abstract
Taking long-range electrostatic effects into account in classical and hybrid quantum mechanics-molecular mechanics (QM/MM) simulations is necessary for an accurate description of the system under study. We have recently developed a method, termed long-range electrostatic corrections (LREC), for monopolar QM/MM calculations. Here, we present an extension of LREC for multipolar/polarizable QM/MM simulations within the LICHEM software package. Reaction barriers and QM-MM interaction energies converge with a LREC cutoff between 20 and 25 Å, in agreement with our previous results. Additionally, the LREC approach for the QM-MM interactions can be smoothly combined with standard shifting or Ewald summation methods in the MM calculations. We recommend the use of QM(LREC)/MM(PME), where the QM region is treated with LREC and the MM region is treated with particle mesh Ewald (PME) summation. This combination is an excellent compromise between simplicity, speed, and accuracy for large QM/MM simulations.
Collapse
Affiliation(s)
- Eric G Kratz
- Department of Chemistry, Wayne State University, Detroit, MI 48202, USA
| | - Robert E Duke
- Department of Chemistry, Wayne State University, Detroit, MI 48202, USA
| | - G Andrés Cisneros
- Department of Chemistry, Wayne State University, Detroit, MI 48202, USA
| |
Collapse
|