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van Gunsteren WF, Oostenbrink C. Methods for Classical-Mechanical Molecular Simulation in Chemistry: Achievements, Limitations, Perspectives. J Chem Inf Model 2024; 64:6281-6304. [PMID: 39136351 DOI: 10.1021/acs.jcim.4c00823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
More than a half century ago it became feasible to simulate, using classical-mechanical equations of motion, the dynamics of molecular systems on a computer. Since then classical-physical molecular simulation has become an integral part of chemical research. It is widely applied in a variety of branches of chemistry and has significantly contributed to the development of chemical knowledge. It offers understanding and interpretation of experimental results, semiquantitative predictions for measurable and nonmeasurable properties of substances, and allows the calculation of properties of molecular systems under conditions that are experimentally inaccessible. Yet, molecular simulation is built on a number of assumptions, approximations, and simplifications which limit its range of applicability and its accuracy. These concern the potential-energy function used, adequate sampling of the vast statistical-mechanical configurational space of a molecular system and the methods used to compute particular properties of chemical systems from statistical-mechanical ensembles. During the past half century various methodological ideas to improve the efficiency and accuracy of classical-physical molecular simulation have been proposed, investigated, evaluated, implemented in general simulation software or were abandoned. The latter because of fundamental flaws or, while being physically sound, computational inefficiency. Some of these methodological ideas are briefly reviewed and the most effective methods are highlighted. Limitations of classical-physical simulation are discussed and perspectives are sketched.
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Affiliation(s)
- Wilfred F van Gunsteren
- Institute for Molecular Physical Science, Swiss Federal Institute of Technology, ETH, CH-8093 Zurich, Switzerland
| | - Chris Oostenbrink
- Institute of Molecular Modelling and Simulation, BOKU University, 1190 Vienna, Austria
- Christian Doppler Laboratory for Molecular Informatics in the Biosciences, BOKU University, Muthgasse 18, 1190 Vienna, Austria
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Pederson JP, McDaniel JG. PyDFT-QMMM: A modular, extensible software framework for DFT-based QM/MM molecular dynamics. J Chem Phys 2024; 161:034103. [PMID: 39007371 DOI: 10.1063/5.0219851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
PyDFT-QMMM is a Python-based package for performing hybrid quantum mechanics/molecular mechanics (QM/MM) simulations at the density functional level of theory. The program is designed to treat short-range and long-range interactions through user-specified combinations of electrostatic and mechanical embedding procedures within periodic simulation domains, providing necessary interfaces to external quantum chemistry and molecular dynamics software. To enable direct embedding of long-range electrostatics in periodic systems, we have derived and implemented force terms for our previously described QM/MM/PME approach [Pederson and McDaniel, J. Chem. Phys. 156, 174105 (2022)]. Communication with external software packages Psi4 and OpenMM is facilitated through Python application programming interfaces (APIs). The core library contains basic utilities for running QM/MM molecular dynamics simulations, and plug-in entry-points are provided for users to implement custom energy/force calculation and integration routines, within an extensible architecture. The user interacts with PyDFT-QMMM primarily through its Python API, allowing for complex workflow development with Python scripting, for example, interfacing with PLUMED for free energy simulations. We provide benchmarks of forces and energy conservation for the QM/MM/PME and alternative QM/MM electrostatic embedding approaches. We further demonstrate a simple example use case for water solute in a water solvent system, for which radial distribution functions are computed from 100 ps QM/MM simulations; in this example, we highlight how the solvation structure is sensitive to different basis-set choices due to under- or over-polarization of the QM water molecule's electron density.
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Affiliation(s)
- John P Pederson
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA
| | - Jesse G McDaniel
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA
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Barnes TA, Ellis S, Chen J, Plimpton SJ, Nash JA. Plugin-based interoperability and ecosystem management for the MolSSI Driver Interface Project. J Chem Phys 2024; 160:214114. [PMID: 38832733 DOI: 10.1063/5.0214279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 05/15/2024] [Indexed: 06/05/2024] Open
Abstract
The MolSSI Driver Interface (MDI) Project is an effort to simplify and standardize the process of enabling tight interoperability between independently developed code bases and is supported by numerous software packages across the domain of chemical physics. It enables a wide variety of use cases, including quantum mechanics/molecular mechanics, advanced sampling, path integral molecular dynamics, machine learning, ab initio molecular dynamics, etc. We describe two major developments within the MDI Project that provide novel solutions to key interoperability challenges. The first of these is the development of the MDI Plugin System, which allows MDI-supporting libraries to be used as highly modular plugins, with MDI enforcing a standardized application programming interface across plugins. Codes can use these plugins without linking against them during their build process, and end-users can select which plugin(s) they wish to use at runtime. The MDI Plugin System features a sophisticated callback system that allows codes to interact with plugins on a highly granular level and represents a significant advancement toward increased modularity among scientific codes. The second major development is MDI Mechanic, an ecosystem management tool that utilizes Docker containerization to simplify the process of developing, validating, maintaining, and deploying MDI-supporting codes. Additionally, MDI Mechanic provides a framework for launching MDI simulations in which each interoperating code is executed within a separate computational environment. This eliminates the need to compile multiple production codes within a single computational environment, reducing opportunities for dependency conflicts and lowering the barrier to entry for users of MDI-enabled codes.
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Affiliation(s)
- T A Barnes
- Molecular Sciences Software Institute, Blacksburg, Virginia 24060, USA
| | - S Ellis
- Molecular Sciences Software Institute, Blacksburg, Virginia 24060, USA
| | - J Chen
- Molecular Sciences Software Institute, Blacksburg, Virginia 24060, USA
| | - S J Plimpton
- Temple University, Philadelphia, Pennsylvania 19122, USA
| | - J A Nash
- Molecular Sciences Software Institute, Blacksburg, Virginia 24060, USA
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Qureshi R, Irfan M, Gondal TM, Khan S, Wu J, Hadi MU, Heymach J, Le X, Yan H, Alam T. AI in drug discovery and its clinical relevance. Heliyon 2023; 9:e17575. [PMID: 37396052 PMCID: PMC10302550 DOI: 10.1016/j.heliyon.2023.e17575] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/17/2023] [Accepted: 06/21/2023] [Indexed: 07/04/2023] Open
Abstract
The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.
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Affiliation(s)
- Rizwan Qureshi
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
- Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, USA
| | - Muhammad Irfan
- Faculty of Electrical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Swabi, Pakistan
| | | | - Sheheryar Khan
- School of Professional Education & Executive Development, The Hong Kong Polytechnic University, Hong Kong
| | - Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, USA
| | | | - John Heymach
- Department of Thoracic Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas, MD Anderson Cancer Center, Houston, USA
| | - Xiuning Le
- Department of Thoracic Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas, MD Anderson Cancer Center, Houston, USA
| | - Hong Yan
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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Hofstetter A, Böselt L, Riniker S. Graph-convolutional neural networks for (QM)ML/MM molecular dynamics simulations. Phys Chem Chem Phys 2022; 24:22497-22512. [PMID: 36106790 DOI: 10.1039/d2cp02931f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
To accurately study the chemical reactions in the condensed phase or within enzymes, both quantum-mechanical description and sufficient configurational sampling are required to reach converged estimates. Here, quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations play an important role, providing QM accuracy for the region of interest at a decreased computational cost. However, QM/MM simulations are still too expensive to study large systems on longer time scales. Recently, machine learning (ML) models have been proposed to replace the QM description. The main limitation of these models lies in the accurate description of long-range interactions present in condensed-phase systems. To overcome this issue, a recent workflow has been introduced combining a semi-empirical method (i.e. density functional tight binding (DFTB)) and a high-dimensional neural network potential (HDNNP) in a Δ-learning scheme. This approach has been shown to be capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. One of the promising alternative approaches to efficiently take long-range effects into account is the development of graph-convolutional neural networks (GCNNs) for the prediction of the potential-energy surface. In this work, we investigate the use of GCNN models - with and without a Δ-learning scheme - for (QM)ML/MM MD simulations. We show that the Δ-learning approach using a GCNN and DFTB as a baseline achieves competitive performance on our benchmarking set of solutes and chemical reactions in water. This method is additionally validated by performing prospective (QM)ML/MM MD simulations of retinoic acid in water and S-adenoslymethionine interacting with cytosine in water. The results indicate that the Δ-learning GCNN model is a valuable alternative for the (QM)ML/MM MD simulations of condensed-phase systems.
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Affiliation(s)
- Albert Hofstetter
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland.
| | - Lennard Böselt
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland.
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland.
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6
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Böselt L, Thürlemann M, Riniker S. Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems. J Chem Theory Comput 2021; 17:2641-2658. [PMID: 33818085 DOI: 10.1021/acs.jctc.0c01112] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD simulations are computationally expensive compared to fully classical simulations as all valence electrons are treated explicitly and a self-consistent field (SCF) procedure is required. Recently, approaches have been proposed to replace the QM description with machine-learned (ML) models. However, condensed-phase systems pose a challenge for these approaches due to long-range interactions. Here, we establish a workflow, which incorporates the MM environment as an element type in a high-dimensional neural network potential (HDNNP). The fitted HDNNP describes the potential-energy surface of the QM particles with an electrostatic embedding scheme. Thus, the MM particles feel a force from the polarized QM particles. To achieve chemical accuracy, we find that even simple systems require models with a strong gradient regularization, a large number of data points, and a substantial number of parameters. To address this issue, we extend our approach to a Δ-learning scheme, where the ML model learns the difference between a reference method (density functional theory (DFT)) and a cheaper semiempirical method (density functional tight binding (DFTB)). We show that such a scheme reaches the accuracy of the DFT reference method while requiring significantly less parameters. Furthermore, the Δ-learning scheme is capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. It is validated by performing MD simulations of retinoic acid in water and the interaction between S-adenoslymethioniat and cytosine in water. The presented results indicate that Δ-learning is a promising approach for (QM)ML/MM MD simulations of condensed-phase systems.
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Affiliation(s)
- Lennard Böselt
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Moritz Thürlemann
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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van Gunsteren WF, Daura X, Fuchs PFJ, Hansen N, Horta BAC, Hünenberger PH, Mark AE, Pechlaner M, Riniker S, Oostenbrink C. On the Effect of the Various Assumptions and Approximations used in Molecular Simulations on the Properties of Bio-Molecular Systems: Overview and Perspective on Issues. Chemphyschem 2020; 22:264-282. [PMID: 33377305 DOI: 10.1002/cphc.202000968] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Indexed: 12/14/2022]
Abstract
Computer simulations of molecular systems enable structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic or supra-molecular level and plays an increasingly important role in chemistry, biology and physics. To interpret the results of such simulations appropriately, the degree of uncertainty and potential errors affecting the calculated properties must be considered. Uncertainty and errors arise from (1) assumptions underlying the molecular model, force field and simulation algorithms, (2) approximations implicit in the interatomic interaction function (force field), or when integrating the equations of motion, (3) the chosen values of the parameters that determine the accuracy of the approximations used, and (4) the nature of the system and the property of interest. In this overview, advantages and shortcomings of assumptions and approximations commonly used when simulating bio-molecular systems are considered. What the developers of bio-molecular force fields and simulation software can do to facilitate and broaden research involving bio-molecular simulations is also discussed.
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Affiliation(s)
- Wilfred F van Gunsteren
- Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, 8093, Zurich, Switzerland
| | - Xavier Daura
- Institute of Biotechnology and Biomedicine, Universitat Autonoma de Barcelona (UAB), 08193, Barcelona, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), 08010, Barcelona, Spain
| | - Patrick F J Fuchs
- Sorbonne Université, Ecole Normale Supérieure, PSL Research University, CNRS, Laboratoire des Biomolécules (LBM), F-75005, Paris, France.,Université de Paris, UFR Sciences du Vivant, F-75013, Paris, France
| | - Niels Hansen
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569, Stuttgart, Germany
| | - Bruno A C Horta
- Instituto de Química, Universidade Federal de Rio de Janeiro, Rio de Janeiro, 21941-909, Brazil
| | - Philippe H Hünenberger
- Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, 8093, Zurich, Switzerland
| | - Alan E Mark
- School of Chemistry and Molecular Biosciences, University of Queensland, St. Lucia, QLD, 4072, Australia
| | - Maria Pechlaner
- Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, 8093, Zurich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, 8093, Zurich, Switzerland
| | - Chris Oostenbrink
- Institute of Molecular Modelling and Simulation, University of Natural Resources and Life Sciences, Vienna, Austria
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8
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Hahn DF, Milić JV, Hünenberger PH. Vase
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Kite
Equilibrium of Resorcin[4]arene Cavitands Investigated Using Molecular Dynamics Simulations with Ball‐and‐Stick Local Elevation Umbrella Sampling. Helv Chim Acta 2019. [DOI: 10.1002/hlca.201900060] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- David F. Hahn
- Laboratory of Physical Chemistry, Department of Chemistry and Applied BiosciencesETH Zürich Vladimir-Prelog-Weg 2 CH-8093 Zürich Switzerland
| | - Jovana V. Milić
- Laboratory of Photonics and InterfacesÉcole Polytechnique Fédérale de Lausanne, EPFL SB ISIC LPI, Station 6 CH-1015 Lausanne Switzerland
| | - Philippe H. Hünenberger
- Laboratory of Physical Chemistry, Department of Chemistry and Applied BiosciencesETH Zürich Vladimir-Prelog-Weg 2 CH-8093 Zürich Switzerland
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Vilseck JZ, Kostal J, Tirado-Rives J, Jorgensen WL. Application of a BOSS-Gaussian interface for QM/MM simulations of Henry and methyl transfer reactions. J Comput Chem 2015; 36:2064-74. [PMID: 26311531 PMCID: PMC4575649 DOI: 10.1002/jcc.24045] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Revised: 07/17/2015] [Accepted: 07/20/2015] [Indexed: 01/06/2023]
Abstract
Hybrid quantum mechanics and molecular mechanics (QM/MM) computer simulations have become an indispensable tool for studying chemical and biological phenomena for systems too large to treat with QM alone. For several decades, semiempirical QM methods have been used in QM/MM simulations. However, with increased computational resources, the introduction of ab initio and density function methods into on-the-fly QM/MM simulations is being increasingly preferred. This adaptation can be accomplished with a program interface that tethers independent QM and MM software packages. This report introduces such an interface for the BOSS and Gaussian programs, featuring modification of BOSS to request QM energies and partial atomic charges from Gaussian. A customizable C-shell linker script facilitates the interprogram communication. The BOSS-Gaussian interface also provides convenient access to Charge Model 5 (CM5) partial atomic charges for multiple purposes including QM/MM studies of reactions. In this report, the BOSS-Gaussian interface is applied to a nitroaldol (Henry) reaction and two methyl transfer reactions in aqueous solution. Improved agreement with experiment is found by determining free-energy surfaces with MP2/CM5 QM/MM simulations than previously reported investigations using semiempirical methods.
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Affiliation(s)
- Jonah Z. Vilseck
- Department of Chemistry, Yale University, New Haven, CT 06520-8107USA
| | - Jakub Kostal
- Department of Chemistry, Yale University, New Haven, CT 06520-8107USA
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Pezeshki S, Lin H. Recent developments in QM/MM methods towards open-boundary multi-scale simulations. MOLECULAR SIMULATION 2014. [DOI: 10.1080/08927022.2014.911870] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Götz AW, Clark MA, Walker RC. An extensible interface for QM/MM molecular dynamics simulations with AMBER. J Comput Chem 2014; 35:95-108. [PMID: 24122798 PMCID: PMC4063945 DOI: 10.1002/jcc.23444] [Citation(s) in RCA: 117] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2013] [Revised: 08/05/2013] [Accepted: 08/31/2013] [Indexed: 11/09/2022]
Abstract
We present an extensible interface between the AMBER molecular dynamics (MD) software package and electronic structure software packages for quantum mechanical (QM) and mixed QM and classical molecular mechanical (MM) MD simulations within both mechanical and electronic embedding schemes. With this interface, ab initio wave function theory and density functional theory methods, as available in the supported electronic structure software packages, become available for QM/MM MD simulations with AMBER. The interface has been written in a modular fashion that allows straight forward extensions to support additional QM software packages and can easily be ported to other MD software. Data exchange between the MD and QM software is implemented by means of files and system calls or the message passing interface standard. Based on extensive tests, default settings for the supported QM packages are provided such that energy is conserved for typical QM/MM MD simulations in the microcanonical ensemble. Results for the free energy of binding of calcium ions to aspartate in aqueous solution comparing semiempirical and density functional Hamiltonians are shown to demonstrate features of this interface.
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Affiliation(s)
- Andreas W. Götz
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0505, USA
| | - Matthew A. Clark
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0505, USA
| | - Ross C. Walker
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0505, USA
- Department of Chemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0505, USA
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Ganoth A, Tsfadia Y, Wiener R. Ubiquitin: Molecular modeling and simulations. J Mol Graph Model 2013; 46:29-40. [DOI: 10.1016/j.jmgm.2013.09.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2013] [Revised: 09/09/2013] [Accepted: 09/10/2013] [Indexed: 01/18/2023]
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Meier K, Choutko A, Dolenc J, Eichenberger AP, Riniker S, van Gunsteren WF. Multi-Resolution Simulation of Biomolecular Systems: A Review of Methodological Issues. Angew Chem Int Ed Engl 2013; 52:2820-34. [DOI: 10.1002/anie.201205408] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2012] [Revised: 09/12/2012] [Indexed: 01/01/2023]
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Meier K, Choutko A, Dolenc J, Eichenberger AP, Riniker S, van Gunsteren WF. Biomolekulare Simulationen mit mehreren Auflösungsniveaus: ein Überblick über methodische Aspekte. Angew Chem Int Ed Engl 2013. [DOI: 10.1002/ange.201205408] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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15
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Mennucci B. Modeling environment effects on spectroscopies through QM/classical models. Phys Chem Chem Phys 2013; 15:6583-94. [DOI: 10.1039/c3cp44417a] [Citation(s) in RCA: 92] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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