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Kim Y, Bui Y, Tazhigulov RN, Bravaya KB, Slipchenko LV. Effective Fragment Potentials for Flexible Molecules: Transferability of Parameters and Amino Acid Database. J Chem Theory Comput 2020; 16:7735-7747. [PMID: 33236635 DOI: 10.1021/acs.jctc.0c00758] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
An accurate but efficient description of noncovalent interactions is a key to predictive modeling of biological and materials systems. The effective fragment potential (EFP) is an ab initio-based force field that provides a physically meaningful decomposition of noncovalent interactions of a molecular system into Coulomb, polarization, dispersion, and exchange-repulsion components. An EFP simulation protocol consists of two steps, preparing parameters for molecular fragments by a series of ab initio calculations on each individual fragment, and calculation of interaction energy and properties of a total molecular system based on the prepared parameters. As the fragment parameters (distributed multipoles, polarizabilities, localized wave function, etc.) depend on a fragment geometry, straightforward application of the EFP method requires recomputing parameters of each fragment if its geometry changes, for example, during thermal fluctuations of a molecular system. Thus, recomputing fragment parameters can easily become both computational and human bottlenecks and lead to a loss of efficiency of a simulation protocol. An alternative approach, in which fragment parameters are adjusted to different fragment geometries, referred to as "flexible EFP", is explored here. The parameter adjustment is based on translations and rotations of local coordinate frames associated with fragment atoms. The protocol is validated on extensive benchmark of amino acid dimers extracted from molecular dynamics snapshots of a cryptochrome protein. A parameter database for standard amino acids is developed to automate flexible EFP simulations in proteins. To demonstrate applicability of flexible EFP in large-scale protein simulations, binding energies and vertical electron ionization and electron attachment energies of a lumiflavin chromophore of the cryptochrome protein are computed. The results obtained with flexible EFP are in a close agreement with the standard EFP procedure but provide a significant reduction in computational cost.
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Affiliation(s)
- Yongbin Kim
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Yen Bui
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Ruslan N Tazhigulov
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States
| | - Ksenia B Bravaya
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
| | - Lyudmila V Slipchenko
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
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Barca GMJ, Bertoni C, Carrington L, Datta D, De Silva N, Deustua JE, Fedorov DG, Gour JR, Gunina AO, Guidez E, Harville T, Irle S, Ivanic J, Kowalski K, Leang SS, Li H, Li W, Lutz JJ, Magoulas I, Mato J, Mironov V, Nakata H, Pham BQ, Piecuch P, Poole D, Pruitt SR, Rendell AP, Roskop LB, Ruedenberg K, Sattasathuchana T, Schmidt MW, Shen J, Slipchenko L, Sosonkina M, Sundriyal V, Tiwari A, Galvez Vallejo JL, Westheimer B, Włoch M, Xu P, Zahariev F, Gordon MS. Recent developments in the general atomic and molecular electronic structure system. J Chem Phys 2020; 152:154102. [PMID: 32321259 DOI: 10.1063/5.0005188] [Citation(s) in RCA: 506] [Impact Index Per Article: 126.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods such as the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features of LibCChem, as it is the associated problem of power consumption as the power of computers increases dramatically. The process by which a complex program suite such as GAMESS is maintained and developed is considered. Future developments are briefly summarized.
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Affiliation(s)
- Giuseppe M J Barca
- Research School of Computer Science, Australian National University, Canberra, ACT 2601, Australia
| | - Colleen Bertoni
- Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Laura Carrington
- EP Analytics, 12121 Scripps Summit Dr. Ste. 130, San Diego, California 92131, USA
| | - Dipayan Datta
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Nuwan De Silva
- Department of Physical and Biological Sciences, Western New England University, Springfield, Massachusetts 01119, USA
| | - J Emiliano Deustua
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA
| | - Dmitri G Fedorov
- Research Center for Computational Design of Advanced Functional Materials (CD-FMat), National Institute of Advanced Industrial Science and Technology (AIST), Umezono 1-1-1, Tsukuba 305-8568, Japan
| | - Jeffrey R Gour
- Microsoft, 15590 NE 31st St., Redmond, Washington 98052, USA
| | - Anastasia O Gunina
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Emilie Guidez
- Department of Chemistry, University of Colorado Denver, Denver, Colorado 80217, USA
| | - Taylor Harville
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Stephan Irle
- Computational Science and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA
| | - Joe Ivanic
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, USA
| | - Karol Kowalski
- Physical Sciences Division, Battelle, Pacific Northwest National Laboratory, K8-91, P.O. Box 999, Richland, Washington 99352, USA
| | - Sarom S Leang
- EP Analytics, 12121 Scripps Summit Dr. Ste. 130, San Diego, California 92131, USA
| | - Hui Li
- Department of Chemistry, University of Nebraska, Lincoln, Nebraska 68588, USA
| | - Wei Li
- School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People's Republic of China
| | - Jesse J Lutz
- Center for Computing Research, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | - Ilias Magoulas
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA
| | - Joani Mato
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Vladimir Mironov
- Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, Moscow 119991, Russian Federation
| | - Hiroya Nakata
- Kyocera Corporation, Research Institute for Advanced Materials and Devices, 3-5-3 Hikaridai Seika-cho, Souraku-gun, Kyoto 619-0237, Japan
| | - Buu Q Pham
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Piotr Piecuch
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA
| | - David Poole
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Spencer R Pruitt
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Alistair P Rendell
- Research School of Computer Science, Australian National University, Canberra, ACT 2601, Australia
| | - Luke B Roskop
- Cray Inc., a Hewlett Packard Enterprise Company, 2131 Lindau Ln #1000, Bloomington, Minnesota 55425, USA
| | - Klaus Ruedenberg
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | | | - Michael W Schmidt
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Jun Shen
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA
| | - Lyudmila Slipchenko
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, USA
| | - Masha Sosonkina
- Department of Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, Virginia 23529, USA
| | - Vaibhav Sundriyal
- Department of Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, Virginia 23529, USA
| | - Ananta Tiwari
- EP Analytics, 12121 Scripps Summit Dr. Ste. 130, San Diego, California 92131, USA
| | - Jorge L Galvez Vallejo
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Bryce Westheimer
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Marta Włoch
- 530 Charlesina Dr., Rochester, Michigan 48306, USA
| | - Peng Xu
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Federico Zahariev
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Mark S Gordon
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
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Okiyama Y, Nakano T, Watanabe C, Fukuzawa K, Mochizuki Y, Tanaka S. Fragment Molecular Orbital Calculations with Implicit Solvent Based on the Poisson–Boltzmann Equation: Implementation and DNA Study. J Phys Chem B 2018; 122:4457-4471. [DOI: 10.1021/acs.jpcb.8b01172] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Yoshio Okiyama
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Tatsuya Nakano
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
- Division of Medicinal Safety Science, National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan
| | - Chiduru Watanabe
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Kaori Fukuzawa
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
- Faculty of Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
| | - Yuji Mochizuki
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Shigenori Tanaka
- Graduate School of System Informatics, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, Hyogo 657-8501, Japan
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Pruitt SR, Bertoni C, Brorsen KR, Gordon MS. Efficient and accurate fragmentation methods. Acc Chem Res 2014; 47:2786-94. [PMID: 24810424 DOI: 10.1021/ar500097m] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Conspectus Three novel fragmentation methods that are available in the electronic structure program GAMESS (general atomic and molecular electronic structure system) are discussed in this Account. The fragment molecular orbital (FMO) method can be combined with any electronic structure method to perform accurate calculations on large molecular species with no reliance on capping atoms or empirical parameters. The FMO method is highly scalable and can take advantage of massively parallel computer systems. For example, the method has been shown to scale nearly linearly on up to 131 000 processor cores for calculations on large water clusters. There have been many applications of the FMO method to large molecular clusters, to biomolecules (e.g., proteins), and to materials that are used as heterogeneous catalysts. The effective fragment potential (EFP) method is a model potential approach that is fully derived from first principles and has no empirically fitted parameters. Consequently, an EFP can be generated for any molecule by a simple preparatory GAMESS calculation. The EFP method provides accurate descriptions of all types of intermolecular interactions, including Coulombic interactions, polarization/induction, exchange repulsion, dispersion, and charge transfer. The EFP method has been applied successfully to the study of liquid water, π-stacking in substituted benzenes and in DNA base pairs, solvent effects on positive and negative ions, electronic spectra and dynamics, non-adiabatic phenomena in electronic excited states, and nonlinear excited state properties. The effective fragment molecular orbital (EFMO) method is a merger of the FMO and EFP methods, in which interfragment interactions are described by the EFP potential, rather than the less accurate electrostatic potential. The use of EFP in this manner facilitates the use of a smaller value for the distance cut-off (Rcut). Rcut determines the distance at which EFP interactions replace fully quantum mechanical calculations on fragment-fragment (dimer) interactions. The EFMO method is both more accurate and more computationally efficient than the most commonly used FMO implementation (FMO2), in which all dimers are explicitly included in the calculation. While the FMO2 method itself does not incorporate three-body interactions, such interactions are included in the EFMO method via the EFP self-consistent induction term. Several applications (ranging from clusters to proteins) of the three methods are discussed to demonstrate their efficacy. The EFMO method will be especially exciting once the analytic gradients have been completed, because this will allow geometry optimizations, the prediction of vibrational spectra, reaction path following, and molecular dynamics simulations using the method.
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Affiliation(s)
- Spencer R. Pruitt
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
- Argonne Leadership Computing Facility, Argonne, Illinois 60439, United States
| | - Colleen Bertoni
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
| | - Kurt R. Brorsen
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
| | - Mark S. Gordon
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
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