1
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Karmakar T, Soares TA, Merz KM. Enhancing Coarse-Grained Models through Machine Learning. J Chem Inf Model 2024; 64:2931-2932. [PMID: 38644772 DOI: 10.1021/acs.jcim.4c00537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Affiliation(s)
- Tarak Karmakar
- Department of Chemistry, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi 110016, India
| | - Thereza A Soares
- Department of Chemistry, FFCLRP, University of São Paulo, Ribeirão Preto 14040-901, Brazil
- Hylleraas Centre for Quantum Molecular Sciences, University of Oslo, Oslo 0315, Norway
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, Lansing 48824, Michigan, United States
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2
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Jin H, Merz KM. Modeling Zinc Complexes Using Neural Networks. J Chem Inf Model 2024; 64:3140-3148. [PMID: 38587510 DOI: 10.1021/acs.jcim.4c00095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method.
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Affiliation(s)
- Hongni Jin
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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3
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Merz KM, Wei GW, Zhu F. Editorial: Machine Learning in Bio-cheminformatics. J Chem Inf Model 2024; 64:2125-2128. [PMID: 38587006 DOI: 10.1021/acs.jcim.4c00444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Affiliation(s)
- Kenneth M Merz
- Department of Chemistry, Michigan State University, Lansing 48824, Michigan, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, Lansing 48824, Michigan, United States
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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4
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Jin H, Merz KM. Modeling Fe(II) Complexes Using Neural Networks. J Chem Theory Comput 2024; 20:2551-2558. [PMID: 38439716 PMCID: PMC10976644 DOI: 10.1021/acs.jctc.4c00063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/18/2024] [Accepted: 02/22/2024] [Indexed: 03/06/2024]
Abstract
We report a Fe(II) data set of more than 23000 conformers in both low-spin (LS) and high-spin (HS) states. This data set was generated to develop a neural network model that is capable of predicting the energy and the energy splitting as a function of the conformation of a Fe(II) organometallic complex. In order to achieve this, we propose a type of scaled electronic embedding to cover the long-range interactions implicitly in our neural network describing the Fe(II) organometallic complexes. For the total energy prediction, the lowest MAE is 0.037 eV, while the lowest MAE of the splitting energy is 0.030 eV. Compared to baseline models, which only incorporate short-range interactions, our scaled electronic embeddings improve the accuracy by over 70% for the prediction of the total energy and the splitting energy. With regard to semiempirical methods, our proposed models reduce the MAE, with respect to these methods, by 2 orders of magnitude.
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Affiliation(s)
- Hongni Jin
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Kenneth M. Merz
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
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5
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Das S, Merz KM. Molecular Gas-Phase Conformational Ensembles. J Chem Inf Model 2024; 64:749-760. [PMID: 38206321 DOI: 10.1021/acs.jcim.3c01309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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6
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Jafari M, Li Z, Song LF, Sagresti L, Brancato G, Merz KM. Thermodynamics of Metal-Acetate Interactions. J Phys Chem B 2024; 128:684-697. [PMID: 38226860 DOI: 10.1021/acs.jpcb.3c06567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Metal ions play crucial roles in protein- and ligand-mediated interactions. They not only act as catalysts to facilitate biological processes but are also important as protein structural elements. Accurately predicting metal ion interactions in computational studies has always been a challenge, and various methods have been suggested to improve these interactions. One such method is the 12-6-4 Lennard-Jones (LJ)-type nonbonded model. Using this model, it has been possible to successfully reproduce the experimental properties of metal ions in aqueous solution. The model includes induced dipole interactions typically ignored in the standard 12-6 LJ nonbonded model. In this we expand the applicability of this model to metal ion-carboxylate interactions. Using 12-6-4 parameters that reproduce the solvation free energies of the metal ions leads to an overestimation of metal ion-acetate interactions, thus, prompting us to fine-tune the model to specifically handle the latter. We also show that the standard 12-6 LJ model significantly falls short in reproducing the experimental binding free energy between acetate and 11 metal ions (Ni(II), Mg(II), Cu(II), Zn(II), Co(II), Cu(I), Fe(II), Mn(II), Cd(II), Ca(II), and Ag(I)). In this study, we describe optimized C4 parameters for the 12-6-4 LJ nonbonded model to be used with three widely employed water models (Transferable Intermolecular Potential with 3 Points (TIP3P), Simple Point Charge Extended (SPC/E), and Optimal Point Charge (OPC) water models). These parameters can accurately match the experimental binding free energy between 11 metal ions and acetate. These parameters can be applied to the study of metalloproteins and transition metal ion channels and transporters, as acetate serves as a representative of the negatively charged amino acid side chains from aspartate and glutamate.
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Affiliation(s)
- Majid Jafari
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Zhen Li
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lin Frank Song
- Biochemical and Biophysical Systems Group, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Luca Sagresti
- Scuola Normale Superiore and CSGI, Piazza dei Cavalieri 7, I-56126 Pisa, Italy
- Istituto Nazionale di Fisica Nucleare (INFN) sezione di Pisa, Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Giuseppe Brancato
- Scuola Normale Superiore and CSGI, Piazza dei Cavalieri 7, I-56126 Pisa, Italy
- Istituto Nazionale di Fisica Nucleare (INFN) sezione di Pisa, Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Kenneth M Merz
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
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7
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Merz KM. In Memoriam: Dr. George W. A. Milne. J Chem Inf Model 2024; 64:1-2. [PMID: 38186320 DOI: 10.1021/acs.jcim.3c02016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Affiliation(s)
- Kenneth M Merz
- Journal of Chemical Information and Modeling American Chemical Society
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8
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Naidoo KJ, Merz KM, Wei GW. Modeling Reactions from Chemical Theories to Machine Learning. J Chem Inf Model 2023; 63:7273. [PMID: 38073434 DOI: 10.1021/acs.jcim.3c01807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Affiliation(s)
- Kevin J Naidoo
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, Western Cape, South Africa
- Department of Chemistry, Michigan State University, East Lansing 48824, Michigan, United States
- Department of Mathematics, Michigan State University, East Lansing 48824, Michigan, United States
| | - Kenneth M Merz
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, Western Cape, South Africa
- Department of Chemistry, Michigan State University, East Lansing 48824, Michigan, United States
- Department of Mathematics, Michigan State University, East Lansing 48824, Michigan, United States
| | - Guo-Wei Wei
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, Western Cape, South Africa
- Department of Chemistry, Michigan State University, East Lansing 48824, Michigan, United States
- Department of Mathematics, Michigan State University, East Lansing 48824, Michigan, United States
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9
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Shajan A, Manathunga M, Götz AW, Merz KM. Geometry Optimization: A Comparison of Different Open-Source Geometry Optimizers. J Chem Theory Comput 2023; 19:7533-7541. [PMID: 37870541 DOI: 10.1021/acs.jctc.3c00188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open-source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and energy calculations. The findings demonstrate how the choice of the coordinate system influences the optimization process to reach an equilibrium structure. With fewer steps, internal coordinates outperform Cartesian coordinates, while the choice of the initial Hessian and Hessian update method in quasi-Newton approaches made by different optimization algorithms also contributes to the rate of convergence. Furthermore, an available open-source machine learning method based on Gaussian process regression (GPR) was evaluated for energy minimizations over surrogate potential energy surfaces with both Cartesian and internal coordinates with internal coordinates outperforming Cartesian. Overall, geomeTRIC and DL-FIND with their default optimization method as well as with the GPR-based model using Hartree-Fock theory with the 6-31G** basis set needed a comparable number of geometry optimization steps to the approach of Baker using a unit matrix as the initial Hessian to reach the optimized geometry. On the other hand, the Berny and Sella offerings in ASE outperformed the other algorithms. Based on this, we recommend using the file-based approaches, ASE/Berny and ASE/Sella, for large-scale optimization efforts, while if using a single executable is preferable, we now distribute QUICK integrated with DL-FIND.
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Affiliation(s)
- Akhil Shajan
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Madushanka Manathunga
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093-0505, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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10
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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: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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11
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Merz KM, Wei GW, Zhu F. Editorial: Harnessing the Power of Large Language Model-Based Chatbots for Scientific Discovery. J Chem Inf Model 2023; 63:5395. [PMID: 37691490 DOI: 10.1021/acs.jcim.3c01244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Affiliation(s)
- Kenneth M Merz
- Department of Chemistry, Michigan State University, Michigan 48824, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, Michigan 48824, United States
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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12
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Das S, Dinpazhoh L, Tanemura KA, Merz KM. Rapid and Automated Ab Initio Metabolite Collisional Cross Section Prediction from SMILES Input. J Chem Inf Model 2023; 63:4995-5000. [PMID: 37548575 DOI: 10.1021/acs.jcim.3c00890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
We implemented an ab initio CCS prediction workflow which incrementally refines generated structures using molecular mechanics, a deep learning potential, conformational clustering, and quantum mechanics (QM). Automating intermediate steps for a high performance computing (HPC) environment allows users to input the SMILES structure of small organic molecules and obtain a Boltzmann averaged collisional cross section (CCS) value as output. The CCS of a molecular species is a metric measured by ion mobility spectrometry (IMS) which can improve annotation of untargeted metabolomics experiments. We report only a minor drop in accuracy when we expedite the CCS calculation by replacing the QM geometry refinement step with a single-point energy calculation. Even though the workflow involves stochastic steps (i.e., conformation generation and clustering), the final CCS value was highly reproducible for multiple iterations on L-carnosine. Finally, we illustrate that the gas phase ensembles modeled for the workflow are intermediate files which can be used for the prediction of other properties such as aqueous phase nuclear magnetic resonance chemical shift prediction. The software is available at the following link: https://github.com/DasSusanta/snakemake_ccs.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Laleh Dinpazhoh
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kiyoto Aramis Tanemura
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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13
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Sharma G, Jafari M, Merz KM. Getting zinc into and out of cells. Methods Enzymol 2023; 687:263-278. [PMID: 37666635 DOI: 10.1016/bs.mie.2023.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Ion channels are specialized proteins located on the plasma membrane and control the movement of ions across the membrane. Zn ion plays an indispensable role as a structural constituent of various proteins, moreover, it plays an important dynamic role in cell signaling. In this chapter, we discuss computational insights into zinc efflux and influx mechanism through YiiP (from Escherichia coli and Shewanella oneidensis) and BbZIP (Bordetella bronchiseptica) transporters, respectively. Gaining knowledge about the mechanism of zinc transport at the molecular level can aid in developing treatments for conditions such as diabetes and cancer by manipulating extracellular and intracellular levels of zinc ions.
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Affiliation(s)
- Gaurav Sharma
- Department of Chemistry, Michigan State University, East Lansing, MI, United States
| | - Majid Jafari
- Department of Chemistry, Michigan State University, East Lansing, MI, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, MI, United States; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States.
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14
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Soares TA, Cournia Z, Naidoo K, Amaro R, Wahab H, Merz KM. Guidelines for Reporting Molecular Dynamics Simulations in JCIM Publications. J Chem Inf Model 2023. [PMID: 37191169 DOI: 10.1021/acs.jcim.3c00599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
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15
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Abstract
Atomic radii play important roles in scientific research. The covalent radii of atoms, ionic radii of ions, and van der Waals (VDW) radii of neutral atoms can all be derived from crystal structures. However, the VDW radii of ions are a challenge to determine because the atomic distances in crystal structures were determined by a combination of VDW interactions and electrostatic interactions, making it unclear how to define the VDW sphere of ions in such an environment. In the present study, we found that VDW radii, which were determined based on the 0.0015 au electron density contour through a wavefunction analysis on atoms, have excellent agreement with the VDW radii of noble-gas atoms determined experimentally. Based on this criterion, we calculated the VDW radii for various atomic ions across the periodic table, providing a systematic set of VDW radii of ions. Previously we have shown that the 12-6 Lennard-Jones nonbonded model could not simultaneously reproduce the hydration free energy (HFE) and ion-oxygen distance (IOD) for an atomic ion when its charge is +2 or higher. Because of this, we developed the 12-6-4 model to reproduce both properties at the same time by explicitly considering the ion-induced dipole interactions. However, recent studies showed it was possible to use the 12-6 model to simulate both properties simultaneously when an ion has the Rmin/2 parameter (i.e., the VDW radius) close to the Shannon ionic radius. In the present study, we show that such a "success" is due to an unphysical overfitting, as the VDW radius of an ion should be significantly larger than its ionic radius. Through molecular dynamics simulations, we show that such overfitting causes significant issues when transferring the parameters from ion-water systems to ion-ligand and metalloprotein systems. In comparison, the 12-6-4 model shows significant improvement in comparison to the overfitted 12-6 model, showing excellent transferability across different systems. In summary, although both the 12-6-4 and 12-6 models could reproduce HFE and IOD for an ion, the 12-6-4 model accomplishes such a task based on the consideration of the physics involved, while the 12-6 model accomplishes this through overfitting, which brings significant transferability issues when simulating other systems. Hence, we strongly recommend the use of the 12-6-4 model (or even more sophisticated models) instead of overfitted 12-6 models when simulating complex systems such as metalloproteins.
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Affiliation(s)
- Madelyn Smith
- Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, Illinois 60660, United States
| | - Zhen Li
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Luke Landry
- Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, Illinois 60660, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Pengfei Li
- Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, Illinois 60660, United States
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16
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Manathunga M, Aktulga HM, Götz AW, Merz KM. Quantum Mechanics/Molecular Mechanics Simulations on NVIDIA and AMD Graphics Processing Units. J Chem Inf Model 2023; 63:711-717. [PMID: 36720086 DOI: 10.1021/acs.jcim.2c01505] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
We have ported and optimized the graphics processing unit (GPU)-accelerated QUICK and AMBER-based ab initio quantum mechanics/molecular mechanics (QM/MM) implementation on AMD GPUs. This encompasses the entire Fock matrix build and force calculation in QUICK including one-electron integrals, two-electron repulsion integrals, exchange-correlation quadrature, and linear algebra operations. General performance improvements to the QUICK GPU code are also presented. Benchmarks carried out on NVIDIA V100 and AMD MI100 cards display similar performance on both hardware for standalone HF/DFT calculations with QUICK and QM/MM molecular dynamics simulations with QUICK/AMBER. Furthermore, with respect to the QUICK/AMBER release version 21, significant speedups are observed for QM/MM molecular dynamics simulations. This significantly increases the range of scientific problems that can be addressed with open-source QM/MM software on state-of-the-art computer hardware.
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Affiliation(s)
- Madushanka Manathunga
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan48824-1322, United States
| | - Hasan Metin Aktulga
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan48824-1322, United States
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California92093-0505, United States
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan48824-1322, United States
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17
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Merz KM, Wei GW, Zhu F. Editorial: Machine Learning in Bio-cheminformatics. J Chem Inf Model 2023; 63:1. [PMID: 36617996 DOI: 10.1021/acs.jcim.2c01496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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18
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Tsai HC, Lee TS, Ganguly A, Giese TJ, Ebert MCCJC, Labute P, Merz KM, York DM. AMBER Free Energy Tools: A New Framework for the Design of Optimized Alchemical Transformation Pathways. J Chem Theory Comput 2023; 19:10.1021/acs.jctc.2c00725. [PMID: 36622640 PMCID: PMC10329732 DOI: 10.1021/acs.jctc.2c00725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
We develop a framework for the design of optimized alchemical transformation pathways in free energy simulations using nonlinear mixing and a new functional form for so-called "softcore" potentials. We describe the implementation and testing of this framework in the GPU-accelerated AMBER software suite. The new optimized alchemical transformation pathways integrate a number of important features, including (1) the use of smoothstep functions to stabilize behavior near the transformation end points, (2) consistent power scaling of Coulomb and Lennard-Jones (LJ) interactions with unitless control parameters to maintain balance of electrostatic attractions and exchange repulsions, (3) pairwise form based on the LJ contact radius for the effective interaction distance with separation-shifted scaling, and (4) rigorous smoothing of the potential at the nonbonded cutoff boundary. The new softcore potential form is combined with smoothly transforming nonlinear λ weights for mixing specific potential energy terms, along with flexible λ-scheduling features, to enable robust and stable alchemical transformation pathways. The resulting pathways are demonstrated and tested, and shown to be superior to the traditional methods in terms of numerical stability and minimal variance of the free energy estimates for all cases considered. The framework presented here can be used to design new alchemical enhanced sampling methods, and leveraged in robust free energy workflows for large ligand data sets.
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Affiliation(s)
- Hsu-Chun Tsai
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Tai-Sung Lee
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Abir Ganguly
- 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
| | - Maximilian CCJC Ebert
- Congruence Therapeutics, 7171 Rue Frederick Banting #117, Saint-Laurent, Quebec, Canada H4S 1Z9
| | - Paul Labute
- Chemical Computing Group ULC, 910-1010 Sherbrooke West, Montreal, Quebec, Canada H3A 2R7
| | - Kenneth M. Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, 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
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19
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Abstract
Modeling the interaction between a metal ion and small molecules can provide pivotal information to bridge and close the gap between two types of simulations: metal ions in water and metal ions in metalloproteins. As previously established, the 12-6-4 Lennard-Jones (LJ)-type nonbonded model, because of its ability to account for the induced dipole effect, has been highly successful in simulating metal ion systems. Using the potential of mean force (PMF) method, the polarizability of the metal-chelating nitrogen from two types of imidazole molecules, delta nitrogen protonated (HID) and epsilon nitrogen protonated (HIE), has been parametrized against experiment for 11 metals (Ag(I), Ca(II), Cd(II), Co(II), Cu(I), Cu(II), Fe(II), Mg(II), Mn(II), Ni(II), and Zn(II)) in conjunction with three commonly used water models (TIP3P, SPC/E, and OPC). We show that the standard 12-6 and unmodified 12-6-4 models are not able to accurately model these interactions and, indeed, predict that the complex should be unstable. The resultant parameters further establish the flexibility and the reliability of the 12-6-4 LJ-type nonbonded model, which can correctly describe three-component interactions between a metal, ligand, and solvent by simply tuning the polarizability of the chelating atom. Also, the transferability of this model was tested, showing the capability of describing metal-ligand interactions in various environments.
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Affiliation(s)
- Zhen Li
- Department of Chemistry, Michigan State University, East Lansing, Michigan48824, United States
| | - Lin Frank Song
- Department of Chemistry, Michigan State University, East Lansing, Michigan48824, United States
| | - Gaurav Sharma
- Department of Chemistry, Michigan State University, East Lansing, Michigan48824, United States
| | - Basak Koca Fındık
- Department of Chemistry, Boğaziçi University, Bebek, 34342Istanbul, Turkey
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan48824, United States
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20
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Abstract
The recent outbreak of COVID-19 infection started in Wuhan, China, and spread across China and beyond. Since the WHO declared COVID-19 a pandemic (March 11, 2020), three vaccines and only one antiviral drug (remdesivir) have been approved (Oct 22, 2020) by the FDA. The coronavirus enters human epithelial cells by the binding of the densely glycosylated fusion spike protein (S protein) to a receptor (angiotensin-converting enzyme 2, ACE2) on the host cell surface. Therefore, inhibiting the viral entry is a promising treatment pathway for preventing or ameliorating the effects of COVID-19 infection. In the current work, we have used all-atom molecular dynamics (MD) simulations to investigate the influence of the MLN-4760 inhibitor on the conformational properties of ACE2 and its interaction with the receptor-binding domain (RBD) of SARS-CoV-2. We have found that the presence of an inhibitor tends to completely/partially open the ACE2 receptor where the two subdomains (I and II) move away from each other, while the absence results in partial or complete closure. The current study increases our understanding of ACE inhibition by MLN-4760 and how it modulates the conformational properties of ACE2.
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Affiliation(s)
- Gaurav Sharma
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lin Frank Song
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Kenneth M. Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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21
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Affiliation(s)
- Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
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22
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Kaymak MC, Rahnamoun A, O'Hearn KA, van Duin ACT, Merz KM, Aktulga HM. JAX-ReaxFF: A Gradient-Based Framework for Fast Optimization of Reactive Force Fields. J Chem Theory Comput 2022; 18:5181-5194. [PMID: 35978524 DOI: 10.1021/acs.jctc.2c00363] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The reactive force field (ReaxFF) model bridges the gap between traditional classical models and quantum mechanical (QM) models by incorporating dynamic bonding and polarizability. To achieve realistic simulations using ReaxFF, model parameters must be optimized against high fidelity training data which typically come from QM calculations. Existing parameter optimization methods for ReaxFF consist of black box techniques using genetic algorithms or Monte Carlo methods. Due to the stochastic behavior of these methods, the optimization process oftentimes requires millions of error evaluations for complex parameter fitting tasks, thereby significantly hampering the rapid development of high quality parameter sets. Rapid optimization of the parameters is essential for developing and refining Reax force fields because producing a force field which exhibits empirical accuracy in terms of dynamics typically requires multiple refinements to the training data as well as to the parameters under optimization. In this work, we present JAX-ReaxFF, a novel software tool that leverages modern machine learning infrastructure to enable fast optimization of ReaxFF parameters. By calculating gradients of the loss function using the JAX library, JAX-ReaxFF utilizes highly effective local optimization methods that are initiated from multiple guesses in the high dimensional optimization space to obtain high quality results. Leveraging the architectural portability of the JAX framework, JAX-ReaxFF can execute efficiently on multicore CPUs, graphics processing units (GPUs), or even tensor processing units (TPUs). As a result of using the gradient information and modern hardware accelerators, we are able to decrease ReaxFF parameter optimization time from days to mere minutes. Furthermore, the JAX-ReaxFF framework can also serve as a sandbox environment for domain scientists to explore customizing the ReaxFF functional form for more accurate modeling.
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Affiliation(s)
- Mehmet Cagri Kaymak
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan 48824, United States
| | - Ali Rahnamoun
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kurt A O'Hearn
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan 48824, United States
| | - Adri C T van Duin
- Department of Mechanical Engineering, The Pennsylvania State University, University Park, State College, Pennsylvania 16802, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Hasan Metin Aktulga
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan 48824, United States
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23
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Thakur A, Sharma G, Badavath VN, Jayaprakash V, Merz KM, Blum G, Acevedo O. Primer for Designing Main Protease (M pro) Inhibitors of SARS-CoV-2. J Phys Chem Lett 2022; 13:5776-5786. [PMID: 35726889 PMCID: PMC9235046 DOI: 10.1021/acs.jpclett.2c01193] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/13/2022] [Indexed: 05/08/2023]
Abstract
The COVID-19 outbreak has been devastating, with hundreds of millions of infections and millions of deaths reported worldwide. In response, the application of structure-activity relationships (SAR) upon experimentally validated inhibitors of SARS-CoV-2 main protease (Mpro) may provide an avenue for the identification of new lead compounds active against COVID-19. Upon the basis of information gleaned from a combination of reported crystal structures and the docking of experimentally validated inhibitors, four "rules" for designing potent Mpro inhibitors have been proposed. The aim here is to guide medicinal chemists toward the most probable hits and to provide guidance on repurposing available structures as Mpro inhibitors. Experimental examination of our own previously reported inhibitors using the four "rules" identified a potential lead compound, the cathepsin inhibitor GB111-NH2, that was 2.3 times more potent than SARS-CoV-2 Mpro inhibitor N3.
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Affiliation(s)
- Abhishek Thakur
- Department
of Chemistry, University of Miami, Coral Gables, Florida 33146, United States
| | - Gaurav Sharma
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Vishnu Nayak Badavath
- School
of Pharmacy & Technology Management, SVKM’s Narsee Monjee Institute of Management Studies (NMIMS), Hyderabad 509301, India
- Department
of Pharmaceutical Sciences and Technology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand 835 215, India
| | - Venkatesan Jayaprakash
- Department
of Pharmaceutical Sciences and Technology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand 835 215, India
| | - Kenneth M. Merz
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Galia Blum
- Institute
for Drug Research, The Hebrew University
of Jerusalem, Jerusalem, 9112001, Israel
| | - Orlando Acevedo
- Department
of Chemistry, University of Miami, Coral Gables, Florida 33146, United States
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24
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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.
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25
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Abstract
A novel locally polarizable multisite model based on the original cation dummy atom (CDA) model is described for molecular dynamics simulations of ions in condensed phases. Polarization effects are introduced by the electronegativity equalization model (EEM) method where charges on the metal ion and its dummy atoms can fluctuate to respond to the environment. This model includes explicit polarization and ion-induced interactions and can be coupled with nonpolarizable or polarizable water models, making it more transferable to simpler force fields. This approach allows us to enhance the original fixed charge CDA model where the charge distribution cannot adapt to the local solvent structure. To illustrate the new CDApol model, we examined properties of the Zn2+, Al3+, and Zr4+ ions in aqueous solution. The polarizable model and Lennard-Jones parameters were refined for octahedrally coordinated Zn2+, Al3+, and Zr4+ CDAs to reproduce thermodynamic and geometrical properties. Using this locally polarizable model, we were able to obtain the experimental hydration free energy, ion-oxygen distance, and coordination number coupled with the standard 12-6 Lennard-Jones model. This model can be used in myriad additional applications where local polarization and charge transfer is important.
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Affiliation(s)
- Ali Rahnamoun
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824-1322, United States
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Kurt A O'Hearn
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Mehmet Cagri Kaymak
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Zhen Li
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Hasan Metin Aktulga
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan 48824-1322, United States
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26
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Das S, Tanemura KA, Dinpazhoh L, Keng M, Schumm C, Leahy L, Asef CK, Rainey M, Edison AS, Fernández FM, Merz KM. In Silico Collision Cross Section Calculations to Aid Metabolite Annotation. J Am Soc Mass Spectrom 2022; 33:750-759. [PMID: 35378036 PMCID: PMC9277703 DOI: 10.1021/jasms.1c00315] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kiyoto Aramis Tanemura
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Laleh Dinpazhoh
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Mithony Keng
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Christina Schumm
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Lydia Leahy
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Carter K Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Markace Rainey
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Arthur S Edison
- Departments of Genetics and Biochemistry, Institute of Bioinformatics and Complex Carbohydrate Center, University of Georgia, 315 Riverbend Road, Athens, Georgia 30602, United States
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
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27
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Abstract
As a fundamental property of all fluids, diffusion plays myriad roles in both science and our daily lives. Diffusive properties of many liquids including water have been extensively studied both experimentally and theoretically, while for transition metal ions, there exist significant experimental data that have not been extensively studied theoretically. Hence, high-confidence predictions for challenging systems like radioactive ions that are biohazardous cannot be reliably generated. In this work, a workflow named ISAIAH (Ion Simulation using AMBER for dIffusion Action when Hydrated) was designed to accurately simulate the diffusion coefficients of 15 monoatomic ions with charges varying from -1 to +3 in four water models. As the results indicate, good agreement with experimental values was achieved, leading us to select 239Pu4+ (for which no experimental data are available) as a candidate ion to make a theoretical prediction of its diffusion coefficient in water. Among all the force field parameter sets, the ones parametrized using an augmented 12-6-4 Lennard-Jones (LJ) potential showed lower average unsigned errors (AUE) for ions of various radii and electron configurations relative to some 12-6 LJ parameters. This observation agrees well with the fact that diffusion is affected by both the hydration free energy (HFE) and the ion-oxygen distance (IOD) between solute and solvent molecules, both of which are handled well by the 12-6-4 model.
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Affiliation(s)
- Zhen Li
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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28
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Abstract
Zinc is an essential transition metal ion that plays as a structural, functional (catalytic), and a signaling molecule regulating cellular function. Unbalanced levels of zinc in cells can result in various pathological conditions. In the current work, all-atom molecular dynamics simulations were used to study the structure-function correlation between different YiiP states embedded in a lipid bilayer. This study enabled us to develop a hypothesis on the zinc efflux mechanism of YiiP. We have created six different models of YiiP representing the stages of the ion-transport process. We found that zinc ion plays a crucial role in restraining the transmembrane domains (TMDs) of the protein. In addition, H153, located in the TMD, has been proposed to guide the zinc ion toward the ZnA site of the YiiP transporter. Understanding the molecular-level Zn2+-transport process sheds light on the strategies affecting intracellular transition-metal ion concentrations in order to treat diseases like diabetes and cancer.
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Affiliation(s)
- Gaurav Sharma
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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29
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Abstract
Scoring functions are the essential component in molecular docking methods. An accurate scoring function is expected to distinguish the native ligand pose from decoy poses. Our previous experience (Pei et al. J. Chem. Inf. Model. 2019, 59 (7), 3305-3315) proved that combining the random forest (RF) algorithm with knowledge-based potential functions can emphasize germane pair wise interactions and improve the performance of original knowledge-based potential functions on protein-ligand decoy detection. One of the most important potential function classes is the force field (FF) potential with one example being the Amber collection of FFs, which are widely available in the AMBER suite of simulation programs. However, for use in RF modeling studies, one needs pair wise energies that are hard to directly extract from Amber. To address this issue, FFENCODER-PL was constructed to calculate the pair wise energies based on the FF14SB and GAFF2 FFs in Amber. FFENCODER-PL was validated using 275 ligand and 21 protein-ligand structures. RF models were built by combining an RF classification algorithm with the pair wise energies calculated from FFENCODER-PL. CASF-2016 (Su et al. J. Chem. Inf. Model. 2019, 59, 895-913) was employed to test the performance of the resultant RF models, which outperformed 33 scoring functions on accuracy and native ranking tests. For the best decoy RMSD test, RF models give a best decoy with an RMSD of around 2 Å from the native pose after including the best decoy-decoy comparisons in the RF model. The relative importance of the RF algorithm and force field potentials was also tested with the results suggesting that both the RF algorithm and force field potentials are important and combining them is the only way to achieve high accuracy. Finally, FFENCODER-PL makes force field-based pair wise energies available for further development of machine learning-based scoring functions. The codes and data used in this paper can be found at https://github.com/JunPei000/Amber_protein_ligand_encoding.
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Affiliation(s)
- Jun Pei
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Lin Frank Song
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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30
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Abstract
Zinc homeostasis in mammals is constantly and precisely maintained by sophisticated regulatory proteins. Among them, the Zrt/Irt-like protein (ZIP) regulates the influx of zinc into the cytoplasm. In this work, we have employed all-atom molecular dynamics simulations to investigate the Zn2+ transport mechanism in prokaryotic ZIP obtained from Bordetella bronchiseptica (BbZIP) in a membrane bilayer. Additionally, the structural and dynamical transformations of BbZIP during this process have been analyzed. This study allowed us to develop a hypothesis for the zinc influx mechanism and formation of the metal-binding site. We have created a model for the outward-facing form of BbZIP (experimentally only the inward-facing form has been characterized) that has allowed us, for the first time, to observe the Zn2+ ion entering the channel and binding to the negatively charged M2 site. It is thought that the M2 site is less favored than the M1 site, which then leads to metal ion egress; however, we have not observed the M1 site being occupied in our simulations. Furthermore, removing both Zn2+ ions from this complex resulted in the collapse of the metal-binding site, illustrating the "structural role" of metal ions in maintaining the binding site and holding the proteins together. Finally, due to the long Cd2+-residue bond distances observed in the X-ray structures, we have proposed the existence of an H3O+ ion at the M2 site that plays an important role in protein stability in the absence of the metal ion.
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Affiliation(s)
- Gaurav Sharma
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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31
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Muratov EN, Amaro R, Andrade CH, Brown N, Ekins S, Fourches D, Isayev O, Kozakov D, Medina-Franco JL, Merz KM, Oprea TI, Poroikov V, Schneider G, Todd MH, Varnek A, Winkler DA, Zakharov AV, Cherkasov A, Tropsha A. A critical overview of computational approaches employed for COVID-19 drug discovery. Chem Soc Rev 2021; 50:9121-9151. [PMID: 34212944 PMCID: PMC8371861 DOI: 10.1039/d0cs01065k] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Indexed: 01/18/2023]
Abstract
COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.
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Affiliation(s)
- Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North CarolinaChapel HillNCUSA
| | - Rommie Amaro
- University of California in San DiegoSan DiegoCAUSA
| | | | | | - Sean Ekins
- Collaborations PharmaceuticalsRaleighNCUSA
| | - Denis Fourches
- Department of Chemistry, North Carolina State UniversityRaleighNCUSA
| | - Olexandr Isayev
- Department of Chemistry, Carnegie Melon UniversityPittsburghPAUSA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookNYUSA
| | | | - Kenneth M. Merz
- Department of Chemistry, Michigan State UniversityEast LansingMIUSA
| | - Tudor I. Oprea
- Department of Internal Medicine and UNM Comprehensive Cancer Center, University of New Mexico, AlbuquerqueNMUSA
- Department of Rheumatology and Inflammation Research, Gothenburg UniversitySweden
- Novo Nordisk Foundation Center for Protein Research, University of CopenhagenDenmark
| | | | - Gisbert Schneider
- Institute of Pharmaceutical Sciences, Swiss Federal Institute of TechnologyZurichSwitzerland
| | | | - Alexandre Varnek
- Department of Chemistry, University of StrasbourgStrasbourgFrance
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido UniversitySapporoJapan
| | - David A. Winkler
- Monash Institute of Pharmaceutical Sciences, Monash UniversityMelbourneVICAustralia
- School of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe UniversityBundooraAustralia
- School of Pharmacy, University of NottinghamNottinghamUK
| | | | - Artem Cherkasov
- Vancouver Prostate Centre, University of British ColumbiaVancouverBCCanada
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North CarolinaChapel HillNCUSA
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32
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Manathunga M, Jin C, Cruzeiro VWD, Miao Y, Mu D, Arumugam K, Keipert K, Aktulga HM, Merz KM, Götz AW. Harnessing the Power of Multi-GPU Acceleration into the Quantum Interaction Computational Kernel Program. J Chem Theory Comput 2021; 17:3955-3966. [PMID: 34062061 DOI: 10.1021/acs.jctc.1c00145] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We report a new multi-GPU capable ab initio Hartree-Fock/density functional theory implementation integrated into the open source QUantum Interaction Computational Kernel (QUICK) program. Details on the load balancing algorithms for electron repulsion integrals and exchange correlation quadrature across multiple GPUs are described. Benchmarking studies carried out on up to four GPU nodes, each containing four NVIDIA V100-SXM2 type GPUs demonstrate that our implementation is capable of achieving excellent load balancing and high parallel efficiency. For representative medium to large size protein/organic molecular systems, the observed parallel efficiencies remained above 82% for the Kohn-Sham matrix formation and above 90% for nuclear gradient calculations. The accelerations on NVIDIA A100, P100, and K80 platforms also have realized parallel efficiencies higher than 68% in all tested cases, paving the way for large-scale ab initio electronic structure calculations with QUICK.
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Affiliation(s)
- Madushanka Manathunga
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Chi Jin
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Vinícius Wilian D Cruzeiro
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0505, United States.,Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Yipu Miao
- Facebook, 1 Hacker Way, Menlo Park, California 94025, United States
| | - Dawei Mu
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 1205 W Clark Street, Urbana, Illinois 61801, United States
| | - Kamesh Arumugam
- NVIDIA Corporation, Santa Clara, California 95051, United States
| | | | - Hasan Metin Aktulga
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0505, United States
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33
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Sengupta A, Li Z, Song LF, Li P, Merz KM. Correction to "Parameterization of Monovalent Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models". J Chem Inf Model 2021; 61:3734-3735. [PMID: 34180237 DOI: 10.1021/acs.jcim.1c00576] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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34
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Merz KM, Amaro R, Cournia Z, Rarey M, Soares T, Tropsha A, Wahab HA, Wang R. Editorial: Method and Data Sharing and Reproducibility of Scientific Results. J Chem Inf Model 2021; 60:5868-5869. [PMID: 33378854 DOI: 10.1021/acs.jcim.0c01389] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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35
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Borges R, Colby SM, Das S, Edison AS, Fiehn O, Kind T, Lee J, Merrill AT, Merz KM, Metz TO, Nunez JR, Tantillo DJ, Wang LP, Wang S, Renslow RS. Quantum Chemistry Calculations for Metabolomics. Chem Rev 2021; 121:5633-5670. [PMID: 33979149 PMCID: PMC8161423 DOI: 10.1021/acs.chemrev.0c00901] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Indexed: 02/07/2023]
Abstract
A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.
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Affiliation(s)
- Ricardo
M. Borges
- Walter
Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, Brazil
| | - Sean M. Colby
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Susanta Das
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Arthur S. Edison
- Departments
of Genetics and Biochemistry and Molecular Biology, Complex Carbohydrate
Research Center and Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States
| | - Oliver Fiehn
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Tobias Kind
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Jesi Lee
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Amy T. Merrill
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Kenneth M. Merz
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Thomas O. Metz
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Jamie R. Nunez
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Dean J. Tantillo
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Lee-Ping Wang
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Shunyang Wang
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Ryan S. Renslow
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
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36
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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.
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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
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37
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Li Z, Song LF, Li P, Merz KM. Parametrization of Trivalent and Tetravalent Metal Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models. J Chem Theory Comput 2021; 17:2342-2354. [PMID: 33793233 DOI: 10.1021/acs.jctc.0c01320] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Commonly seen in rare-earth chemistry and materials science, highly charged metal ions play key roles in many chemical processes. Computer simulations have become an important tool for scientific research nowadays. Meaningful simulations require reliable parameters. In the present work, we parametrized 18 M(III) and 6 M(IV) metal ions for four new water models (OPC3, OPC, TIP3P-FB, TIP4P-FB) in conjunction with each of the 12-6 and 12-6-4 nonbonded models. Similar to what was observed previously, issues with the 12-6 model can be fixed by using the 12-6-4 model. Moreover, the four new water models showed comparable performance or considerable improvement over the previous water models (TIP3P, SPC/E, and TIP4PEW) in the same category (3-point or 4-point water models, respectively). Finally, we reported a study of a metalloprotein system demonstrating the capability of the 12-6-4 model to model metalloproteins. The reported parameters will facilitate accurate simulations of highly charged metal ions in aqueous solution.
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Affiliation(s)
- Zhen Li
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lin Frank Song
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Pengfei Li
- Department of Chemistry, Yale University, New Haven, Connecticut 06511, United States.,Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, Illinois 60660, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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38
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Abstract
While accurately modeling the conformational ensemble is required for predicting properties of flexible molecules, the optimal method of obtaining the conformational ensemble appears as varied as their applications. Ensemble structures have been modeled by generation, refinement, and clustering of conformations with a sufficient number of samples. We present a conformational clustering algorithm intended to automate the conformational clustering step through the Louvain algorithm, which requires minimal hyperparameters and importantly no predefined number of clusters or threshold values. The conformational graphs produced by this method for O-succinyl-l-homoserine, oxidized nicotinamide adenine dinucleotide, and 200 representative metabolites each preserved the geometric/energetic correlation expected for points on the potential energy surface. Clustering based on these graphs provides partitions informed by the potential energy surface. Automating conformational clustering in a workflow with AutoGraph may mitigate human biases introduced by guess and check over hyperparameter selection while allowing flexibility to the result by not imposing predefined criteria other than optimizing the model's loss function. Associated codes are available at https://github.com/TanemuraKiyoto/AutoGraph.
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Affiliation(s)
- Kiyoto Aramis Tanemura
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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39
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Abstract
Monovalent ions play significant roles in various biological and material systems. Recently, four new water models (OPC3, OPC, TIP3P-FB, and TIP4P-FB), with significantly improved descriptions of condensed phase water, have been developed. The pairwise interaction between the metal ion and water necessitates the development of ion parameters specifically for these water models. Herein, we parameterized the 12-6 and the 12-6-4 nonbonded models for 12 monovalent ions with the respective four new water models. These monovalent ions contain eight cations including alkali metal ions (Li+, Na+, K+, Rb+, Cs+), transition-metal ions (Cu+ and Ag+), and Tl+ from the boron family, along with four halide anions (F-, Cl-, Br-, I-). Our parameters were designed to reproduce the target hydration free energies (the 12-6 hydration free energy (HFE) set), the ion-oxygen distances (the 12-6 ion-oxygen distance (IOD) set), or both of them (the 12-6-4 set). The 12-6-4 parameter set provides highly accurate structural features overcoming the limitations of the routinely used 12-6 nonbonded model for ions. Specifically, we note that the 12-6-4 parameter set is able to reproduce experimental hydration free energies within 1 kcal/mol and experimental ion-oxygen distances within 0.01 Å simultaneously. We further reproduced the experimentally determined activity derivatives for salt solutions, validating the ion parameters for simulations of ion pairs. The improved performance of the present water models over our previous parameter sets for the TIP3P, TIP4P, and SPC/E water models (Li, P. et al J. Chem. Theory Comput. 2015 11 1645 1657) highlights the importance of the choice of water model in conjunction with the metal ion parameter set.
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Affiliation(s)
- Arkajyoti Sengupta
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Zhen Li
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lin Frank Song
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Pengfei Li
- Department of Chemistry, Yale University, New Haven, Connecticut 06511, United States.,Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, Illinois 60660, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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40
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Abstract
Modern scientometric techniques, applied at scale, can provide valuable information that complements qualitative investigation of the accumulation of knowledge in a field. We discuss a trio of articles from computational chemistry selected from an analysis of 181 million tri-cited articles.
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Affiliation(s)
- Wenxi Zhao
- Netelabs, NET ESolutions Corporation, McLean, Virginia 22102, United States
| | - Dmitriy Korobskiy
- Netelabs, NET ESolutions Corporation, McLean, Virginia 22102, United States
| | | | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - George Chacko
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
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41
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Affiliation(s)
- Kenneth M Merz
- Department of Chemistry, Michigan State University, Michigan, East Lansing 48824, United States
| | - Gianni De Fabritiis
- Computational Science Laboratory, Barcelona Biomedical Research Park (PRBB), Universitat Pompeu Fabra, C Dr Aiguader 88, 08003 Barcelona, Spain
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, Michigan, East Lansing 48824, United States
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42
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Nunes-Alves A, Mazzolari A, Merz KM. What Makes a Paper Be Highly Cited? 60 Years of the Journal of Chemical Information and Modeling. J Chem Inf Model 2020; 60:5866-5867. [PMID: 33378851 DOI: 10.1021/acs.jcim.0c01248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ariane Nunes-Alves
- Heidelberg Institute for Theoretical Studies (HITS), Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.,Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
| | - Angelica Mazzolari
- Department of Pharmaceutical Sciences, University of Milan, Via Mangiagalli 25, I-20133 Milan, Italy
| | - Kenneth M Merz
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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43
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Rahnamoun A, Kaymak MC, Manathunga M, Götz AW, van Duin ACT, Merz KM, Aktulga HM. ReaxFF/AMBER-A Framework for Hybrid Reactive/Nonreactive Force Field Molecular Dynamics Simulations. J Chem Theory Comput 2020; 16:7645-7654. [PMID: 33141581 DOI: 10.1021/acs.jctc.0c00874] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Combined quantum mechanical/molecular mechanical (QM/MM) models using semiempirical and ab initio methods have been extensively reported on over the past few decades. These methods have been shown to be capable of providing unique insights into a range of problems, but they are still limited to relatively short time scales, especially QM/MM models using ab initio methods. An intermediate approach between a QM based model and classical mechanics could help fill this time-scale gap and facilitate the study of a range of interesting problems. Reactive force fields represent the intermediate approach explored in this paper. A widely used reactive model is ReaxFF, which has largely been applied to materials science problems and is generally used as a stand-alone (i.e., the full system is modeled using ReaxFF). We report a hybrid ReaxFF/AMBER molecular dynamics (MD) tool, which introduces ReaxFF capabilities to capture bond breaking and formation within the AMBER MD software package. This tool enables us to study local reactive events in large systems at a fraction of the computational costs of QM/MM models. We describe the implementation of ReaxFF/AMBER, validate this implementation using a benzene molecule solvated in water, and compare its performance against a range of similar approaches. To illustrate the predictive capabilities of ReaxFF/AMBER, we carried out a Claisen rearrangement study in aqueous solution. In a first for ReaxFF, we were able to use AMBER's potential of mean force (PMF) capabilities to perform a PMF study on this organic reaction. The ability to capture local reaction events in large systems using combined ReaxFF/AMBER opens up a range of problems that can be tackled using this model to address both chemical and biological processes.
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Affiliation(s)
- Ali Rahnamoun
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Mehmet Cagri Kaymak
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Madushanka Manathunga
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0505, United States
| | - Adri C T van Duin
- Department of Mechanical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
| | - Hasan Metin Aktulga
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, Michigan 48824-1322, United States
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44
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Abstract
MRP.py is a Python-based parametrization program for covalently modified amino acid residues for molecular dynamics simulations. Charge derivation is performed via an RESP charge fit, and force constants are obtained through rewriting of either protein or GAFF database parameters. This allows for the description of interfacial interactions between the modifed residue and protein. MRP.py is capable of working with a variety of protein databases. MRP.py's highly general and systematic method of obtaining parameters allows the user to circumvent the process of parametrizing the modified residue-protein interface. Two examples, a covalently bound inhibitor and covalent adduct consisting of modified residues, are provided in the Supporting Information.
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Affiliation(s)
- Patrick G Sahrmann
- Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama36849-5312, United States
| | - Patrick H Donnan
- Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama36849-5312, United States
| | - Kenneth M Merz
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824-1312, United States
| | - Steven O Mansoorabadi
- Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama36849-5312, United States
| | - Douglas C Goodwin
- Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama36849-5312, United States
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Liu H, Deng J, Luo Z, Lin Y, Merz KM, Zheng Z. Receptor–Ligand Binding Free Energies from a Consecutive Histograms Monte Carlo Sampling Method. J Chem Theory Comput 2020; 16:6645-6655. [DOI: 10.1021/acs.jctc.0c00457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hao Liu
- School of Mechanical and Electronic Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, PR China
| | - Jianpeng Deng
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, PR China
| | - Zhou Luo
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, PR China
| | - Yawei Lin
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, PR China
| | - Kenneth M. Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Zheng Zheng
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, PR China
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Affiliation(s)
- Lin Frank Song
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M. Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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Burrows CJ, Huang J, Wang S, Kim HJ, Meyer GJ, Schanze K, Lee TR, Lutkenhaus JL, Kaplan D, Jones C, Bertozzi C, Kiessling L, Mulcahy MB, Lindsley CW, Finn MG, Blum JD, Kamat P, Choi W, Snyder S, Aldrich CC, Rowan S, Liu B, Liotta D, Weiss PS, Zhang D, Ganesh KN, Atwater HA, Gooding JJ, Allen DT, Voigt CA, Sweedler J, Schepartz A, Rotello V, Lecommandoux S, Sturla SJ, Hammes-Schiffer S, Buriak J, Steed JW, Wu H, Zimmerman J, Brooks B, Savage P, Tolman W, Hofmann TF, Brennecke JF, Holme TA, Merz KM, Scuseria G, Jorgensen W, Georg GI, Wang S, Proteau P, Yates JR, Stang P, Walker GC, Hillmyer M, Taylor LS, Odom TW, Carreira E, Rossen K, Chirik P, Miller SJ, Shea JE, McCoy A, Zanni M, Hartland G, Scholes G, Loo JA, Milne J, Tegen SB, Kulp DT, Laskin J. Confronting Racism in Chemistry Journals. ACS Pharmacol Transl Sci 2020; 3:559-561. [DOI: 10.1021/acsptsci.0c00067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Indexed: 11/29/2022]
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48
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Abstract
Atom pairwise potential functions make up an essential part of many scoring functions for protein decoy detection. With the development of machine learning (ML) tools, there are multiple ways to combine potential functions to create novel ML models and methods. Potential function parameters can be easily extracted; however, it is usually hard to directly obtain the calculated atom pairwise energies from scoring functions. Amber, as one of the most popular suites of modeling programs, has an extensive history and library of force field potential functions. In this work, we directly used the force field parameters in ff94 and ff14SB from Amber and encoded them to calculate atom pairwise energies for different interactions. Two sets of structures (single amino acid set and a dipeptide set) were used to evaluate the performance of our encoded Amber potentials. From the comparison results between energy terms obtained from our encoding and Amber, we find energy difference within ±0.06 kcal/mol for all tested structures. Previously we have shown that the Random Forest (RF) model can help to emphasize more important atom pairwise interactions and ignore insignificant ones [Pei, J.; Zheng, Z.; Merz, K. M. J. Chem. Inf. Model. 2019, 59, 1919-1929]. Here, as an example of combining ML methods with traditional potential functions, we followed the same work flow to combine the RF models with force field potential functions from Amber. To determine the performance of our RF models with force field potential functions, 224 different protein native-decoy systems were used as our training and testing sets We find that the RF models with ff94 and ff14SB force field parameters outperformed all other scoring functions (RF models with KECSA2, RWplus, DFIRE, dDFIRE, and GOAP) considered in this work for native structure detection, and they performed similarly in detecting the best decoy. Through inclusion of best decoy to decoy comparisons in building our RF models, we were able to generate models that outperformed the score functions tested herein both on accuracy and best decoy detection, again showing the performance and flexibility of our RF models to tackle this problem. Finally, the importance of the RF algorithm and force field parameters were also tested and the comparison results suggest that both the RF algorithm and force field potentials are important with the ML scoring function achieving its best performance only by combining them together. All code and data used in this work are available at https://github.com/JunPei000/FFENCODER_for_Protein_Folding_Pose_Selection.
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Affiliation(s)
- Jun Pei
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Lin Frank Song
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
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Burrows CJ, Huang J, Wang S, Kim HJ, Meyer GJ, Schanze K, Lee TR, Lutkenhaus JL, Kaplan D, Jones C, Bertozzi C, Kiessling L, Mulcahy MB, Lindsley CW, Finn MG, Blum JD, Kamat P, Choi W, Snyder S, Aldrich CC, Rowan S, Liu B, Liotta D, Weiss PS, Zhang D, Ganesh KN, Atwater HA, Gooding JJ, Allen DT, Voigt CA, Sweedler J, Schepartz A, Rotello V, Lecommandoux S, Sturla SJ, Hammes-Schiffer S, Buriak J, Steed JW, Wu H, Zimmerman J, Brooks B, Savage P, Tolman W, Hofmann TF, Brennecke JF, Holme TA, Merz KM, Scuseria G, Jorgensen W, Georg GI, Wang S, Proteau P, Yates JR, Stang P, Walker GC, Hillmyer M, Taylor LS, Odom TW, Carreira E, Rossen K, Chirik P, Miller SJ, Shea JE, McCoy A, Zanni M, Hartland G, Scholes G, Loo JA, Milne J, Tegen SB, Kulp DT, Laskin J. Confronting Racism in Chemistry Journals. J Proteome Res 2020; 19:2911-2913. [DOI: 10.1021/acs.jproteome.0c00436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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50
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Abstract
A major challenge for metabolomic analysis is to obtain an unambiguous identification of the metabolites detected in a sample. Among metabolomics techniques, NMR spectroscopy is a sophisticated, powerful, and generally applicable spectroscopic tool that can be used to ascertain the correct structure of newly isolated biogenic molecules. However, accurate structure prediction using computational NMR techniques depends on how much of the relevant conformational space of a particular compound is considered. It is intrinsically challenging to calculate NMR chemical shifts using high-level DFT when the conformational space of a metabolite is extensive. In this work, we developed NMR chemical shift calculation protocols using a machine learning model in conjunction with standard DFT methods. The pipeline encompasses the following steps: (1) conformation generation using a force field (FF)-based method, (2) filtering the FF generated conformations using the ASE-ANI machine learning model, (3) clustering of the optimized conformations based on structural similarity to identify chemically unique conformations, (4) DFT structural optimization of the unique conformations, and (5) DFT NMR chemical shift calculation. This protocol can calculate the NMR chemical shifts of a set of molecules using any available combination of DFT theory, solvent model, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Our protocol reduces the overall computational time by 2 orders of magnitude over methods that optimize the conformations using fully ab initio methods, while still producing good agreement with experimental observations. The complete protocol is designed in such a manner that makes the computation of chemical shifts tractable for a large number of conformationally flexible metabolites.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, USA
| | - Arthur S. Edison
- Departments of Genetics and Biochemistry, Institute of Bioinformatics and Complex Carbohydrate Center, University of Georgia, 315 Riverbend Rd, Athens, GA 30602, USA
| | - Kenneth M. Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, USA
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