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Farouq MAH, Kubiak-Ossowska K, Al Qaraghuli MM, Ferro VA, Mulheran PA. Functionalisation of Inorganic Material Surfaces with Staphylococcus Protein A: A Molecular Dynamics Study. Int J Mol Sci 2022; 23:ijms23094832. [PMID: 35563221 PMCID: PMC9103475 DOI: 10.3390/ijms23094832] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/21/2022] [Accepted: 04/25/2022] [Indexed: 12/07/2022] Open
Abstract
Staphylococcus protein A (SpA) is found in the cell wall of Staphylococcus aureus bacteria. Its ability to bind to the constant Fc regions of antibodies means it is useful for antibody extraction, and further integration with inorganic materials can lead to the development of diagnostics and therapeutics. We have investigated the adsorption of SpA on inorganic surface models such as experimentally relevant negatively charged silica, as well as positively charged and neutral surfaces, by use of fully atomistic molecular dynamics simulations. We have found that SpA, which is itself negatively charged at pH7, is able to adsorb on all our surface models. However, adsorption on charged surfaces is more specific in terms of protein orientation compared to a neutral Au (111) surface, while the protein structure is generally well maintained in all cases. The results indicate that SpA adsorption is optimal on the siloxide-rich silica surface, which is negative at pH7 since this keeps the Fc binding regions free to interact with other species in solution. Due to the dominant role of electrostatics, the results are transferable to other inorganic materials and pave the way for new diagnostic and therapeutic designs where SpA might be used to conjugate antibodies to nanoparticles.
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Affiliation(s)
- Mohammed A. H. Farouq
- Department of Chemical and Process Engineering, University of Strathclyde, 75 Montrose Street, Glasgow G1 1XJ, UK; (M.M.A.Q.); (P.A.M.)
- Correspondence: ; Tel.: +44-01-4155-24400
| | - Karina Kubiak-Ossowska
- Department of Physics/Archie-West HPC, University of Strathclyde, 107 Rottenrow East, Glasgow G4 0NG, UK;
| | - Mohammed M. Al Qaraghuli
- Department of Chemical and Process Engineering, University of Strathclyde, 75 Montrose Street, Glasgow G1 1XJ, UK; (M.M.A.Q.); (P.A.M.)
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow G4 0RE, UK;
- EPSRC Future Manufacturing Research Hub for Continuous Manufacturing and Advanced Crystallisation (CMAC), University of Strathclyde, 99 George Street, Glasgow G1 1RD, UK
| | - Valerie A. Ferro
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow G4 0RE, UK;
| | - Paul A. Mulheran
- Department of Chemical and Process Engineering, University of Strathclyde, 75 Montrose Street, Glasgow G1 1XJ, UK; (M.M.A.Q.); (P.A.M.)
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2
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Edwards T, Foloppe N, Harris SA, Wells G. The future of biomolecular simulation in the pharmaceutical industry: what we can learn from aerodynamics modelling and weather prediction. Part 1. understanding the physical and computational complexity of in silico drug design. Acta Crystallogr D Struct Biol 2021; 77:1348-1356. [PMID: 34726163 PMCID: PMC8561735 DOI: 10.1107/s2059798321009712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 09/17/2021] [Indexed: 02/04/2023] Open
Abstract
The predictive power of simulation has become embedded in the infrastructure of modern economies. Computer-aided design is ubiquitous throughout industry. In aeronautical engineering, built infrastructure and materials manufacturing, simulations are routinely used to compute the performance of potential designs before construction. The ability to predict the behaviour of products is a driver of innovation by reducing the cost barrier to new designs, but also because radically novel ideas can be piloted with relatively little risk. Accurate weather forecasting is essential to guide domestic and military flight paths, and therefore the underpinning simulations are critical enough to have implications for national security. However, in the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology. Over the last 30 years, molecular-modelling tools have gradually gained a degree of acceptance in the pharmaceutical industry. Drug discovery has begun to benefit from physics-based simulations. While such simulations have great potential for improved molecular design, much scepticism remains about their value. The motivations for such reservations in industry and areas where simulations show promise for efficiency gains in preclinical research are discussed. In this, the first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1). In Part 2, the status of computer simulations in pharma is contrasted with aerodynamics modelling and weather forecasting, and comments are made on the cultural changes needed for equivalent computational technologies to become integrated into life-science industries.
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Affiliation(s)
- Tom Edwards
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
| | | | - Sarah Anne Harris
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
- School of Physics and Astronomy, University of Leeds, Leeds, United Kingdom
| | - Geoff Wells
- School of Pharmacy, University College London, London, United Kingdom
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3
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Roy D, Kovalenko A. Biomolecular Simulations with the Three-Dimensional Reference Interaction Site Model with the Kovalenko-Hirata Closure Molecular Solvation Theory. Int J Mol Sci 2021; 22:5061. [PMID: 34064655 DOI: 10.3390/ijms22105061] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/08/2021] [Accepted: 05/10/2021] [Indexed: 11/17/2022] Open
Abstract
The statistical mechanics-based 3-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) molecular solvation theory has proven to be an essential part of a multiscale modeling framework, covering a vast region of molecular simulation techniques. The successful application ranges from the small molecule solvation energy to the bulk phase behavior of polymers, macromolecules, etc. The 3D-RISM-KH successfully predicts and explains the molecular mechanisms of self-assembly and aggregation of proteins and peptides related to neurodegeneration, protein-ligand binding, and structure-function related solvation properties. Upon coupling the 3D-RISM-KH theory with a novel multiple time-step molecular dynamic (MD) of the solute biomolecule stabilized by the optimized isokinetic Nosé-Hoover chain thermostat driven by effective solvation forces obtained from 3D-RISM-KH and extrapolated forward by generalized solvation force extrapolation (GSFE), gigantic outer time-steps up to picoseconds to accurately calculate equilibrium properties were obtained in this new quasidynamics protocol. The multiscale OIN/GSFE/3D-RISM-KH algorithm was implemented in the Amber package and well documented for fully flexible model of alanine dipeptide, miniprotein 1L2Y, and protein G in aqueous solution, with a solvent sampling rate ~150 times faster than a standard MD simulation in explicit water. Further acceleration in computation can be achieved by modifying the extent of solvation layers considered in the calculation, as well as by modifying existing closure relations. This enhanced simulation technique has proven applications in protein-ligand binding energy calculations, ligand/solvent binding site prediction, molecular solvation energy calculations, etc. Applications of the RISM-KH theory in molecular simulation are discussed in this work.
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4
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Schlick T, Portillo-Ledesma S, Myers CG, Beljak L, Chen J, Dakhel S, Darling D, Ghosh S, Hall J, Jan M, Liang E, Saju S, Vohr M, Wu C, Xu Y, Xue E. Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field. Annu Rev Biophys 2021; 50:267-301. [PMID: 33606945 PMCID: PMC8105287 DOI: 10.1146/annurev-biophys-091720-102019] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, New York 10003, USA;
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
- New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai 200122, China
| | | | - Christopher G Myers
- Department of Chemistry, New York University, New York, New York 10003, USA;
| | - Lauren Beljak
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Justin Chen
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sami Dakhel
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Daniel Darling
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sayak Ghosh
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Joseph Hall
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Mikaeel Jan
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Emily Liang
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sera Saju
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Mackenzie Vohr
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Chris Wu
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Yifan Xu
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Eva Xue
- College of Arts and Science, New York University, New York, New York 10003, USA
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5
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Brotzakis ZF, Vendruscolo M, Bolhuis PG. A method of incorporating rate constants as kinetic constraints in molecular dynamics simulations. Proc Natl Acad Sci U S A 2021; 118:e2012423118. [PMID: 33376207 PMCID: PMC7812743 DOI: 10.1073/pnas.2012423118] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
From the point of view of statistical mechanics, a full characterization of a molecular system requires an accurate determination of its possible states, their populations, and the respective interconversion rates. Toward this goal, well-established methods increase the accuracy of molecular dynamics simulations by incorporating experimental information about states using structural restraints and about populations using thermodynamics restraints. However, it is still unclear how to include experimental knowledge about interconversion rates. Here, we introduce a method of imposing known rate constants as constraints in molecular dynamics simulations, which is based on a combination of the maximum-entropy and maximum-caliber principles. Starting from an existing ensemble of trajectories, obtained from either molecular dynamics or enhanced trajectory sampling, this method provides a minimally perturbed path distribution consistent with the kinetic constraints, as well as modified free energy and committor landscapes. We illustrate the application of the method to a series of model systems, including all-atom molecular simulations of protein folding. Our results show that by combining experimental rate constants and molecular dynamics simulations, this approach enables the determination of transition states, reaction mechanisms, and free energies. We anticipate that this method will extend the applicability of molecular simulations to kinetic studies in structural biology and that it will assist the development of force fields to reproduce kinetic and thermodynamic observables. Furthermore, this approach is generally applicable to a wide range of systems in biology, physics, chemistry, and material science.
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Affiliation(s)
- Z Faidon Brotzakis
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Michele Vendruscolo
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Peter G Bolhuis
- van't Hoff Institute for Molecular Sciences, University of Amsterdam, 1090 GD Amsterdam, The Netherlands
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6
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Abstract
The impact of molecular dynamics (MD) simulations in molecular biology and drug discovery has expanded dramatically in recent years. These simulations capture the behavior of proteins and other biomolecules in full atomic detail and at very fine temporal resolution. Major improvements in simulation speed, accuracy, and accessibility, together with the proliferation of experimental structural data, have increased the appeal of biomolecular simulation to experimentalists-a trend particularly noticeable in, although certainly not limited to, neuroscience. Simulations have proven valuable in deciphering functional mechanisms of proteins and other biomolecules, in uncovering the structural basis for disease, and in the design and optimization of small molecules, peptides, and proteins. Here we describe, in practical terms, the types of information MD simulations can provide and the ways in which they typically motivate further experimental work.
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Affiliation(s)
- Scott A Hollingsworth
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA 94305, USA; Department of Structural Biology, Stanford University, Stanford, CA 94305, USA; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Ron O Dror
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA 94305, USA; Department of Structural Biology, Stanford University, Stanford, CA 94305, USA; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA.
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7
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Jung J, Nishima W, Daniels M, Bascom G, Kobayashi C, Adedoyin A, Wall M, Lappala A, Phillips D, Fischer W, Tung CS, Schlick T, Sugita Y, Sanbonmatsu KY. Scaling molecular dynamics beyond 100,000 processor cores for large-scale biophysical simulations. J Comput Chem 2019; 40:1919-1930. [PMID: 30994934 DOI: 10.1002/jcc.25840] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 03/06/2019] [Accepted: 03/20/2019] [Indexed: 01/27/2023]
Abstract
The growing interest in the complexity of biological interactions is continuously driving the need to increase system size in biophysical simulations, requiring not only powerful and advanced hardware but adaptable software that can accommodate a large number of atoms interacting through complex forcefields. To address this, we developed and implemented strategies in the GENESIS molecular dynamics package designed for large numbers of processors. Long-range electrostatic interactions were parallelized by minimizing the number of processes involved in communication. A novel algorithm was implemented for nonbonded interactions to increase single instruction multiple data (SIMD) performance, reducing memory usage for ultra large systems. Memory usage for neighbor searches in real-space nonbonded interactions was reduced by approximately 80%, leading to significant speedup. Using experimental data describing physical 3D chromatin interactions, we constructed the first atomistic model of an entire gene locus (GATA4). Taken together, these developments enabled the first billion-atom simulation of an intact biomolecular complex, achieving scaling to 65,000 processes (130,000 processor cores) with 1 ns/day performance. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- Jaewoon Jung
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe 650-0047, Japan
| | - Wataru Nishima
- Los Alamos National Laboratory, Los Alamos, New Mexico.,New Mexico Consortium, Los Alamos, New Mexico
| | | | | | - Chigusa Kobayashi
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe 650-0047, Japan
| | | | - Michael Wall
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe 650-0047, Japan
| | - Anna Lappala
- Los Alamos National Laboratory, Los Alamos, New Mexico
| | | | | | | | | | - Yuji Sugita
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe 650-0047, Japan
| | - Karissa Y Sanbonmatsu
- Los Alamos National Laboratory, Los Alamos, New Mexico.,New Mexico Consortium, Los Alamos, New Mexico
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8
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Leimkuhler B, Matthews C. Efficient molecular dynamics using geodesic integration and solvent-solute splitting. Proc Math Phys Eng Sci 2016; 472:20160138. [PMID: 27279779 PMCID: PMC4893190 DOI: 10.1098/rspa.2016.0138] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 04/08/2016] [Indexed: 12/03/2022] Open
Abstract
We present an approach to Langevin dynamics in the presence of holonomic constraints based on decomposition of the system into components representing geodesic flow, constrained impulse and constrained diffusion. We show that a particular ordering of the components results in an integrator that is an order of magnitude more accurate for configurational averages than existing alternatives. Moreover, by combining the geodesic integration method with a solvent-solute force splitting, we demonstrate that stepsizes of at least 8 fs can be used for solvated biomolecules with high sampling accuracy and without substantially altering diffusion rates, approximately increasing by a factor of two the efficiency of molecular dynamics sampling for such systems. The methods described in this article are easily implemented using the standard apparatus of modern simulation codes.
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Affiliation(s)
- Benedict Leimkuhler
- School of Mathematics and Maxwell Institute of Mathematical Sciences, University of Edinburgh, James Clerk Maxwell Building, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK
| | - Charles Matthews
- Department of Statistics, University of Chicago, 5734 S. University Avenue, Chicago, IL 60637, USA
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9
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Shaffer P, Valsson O, Parrinello M. Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin. Proc Natl Acad Sci U S A 2016; 113:1150-5. [PMID: 26787868 DOI: 10.1073/pnas.1519712113] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute free-energy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin.
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10
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Gray A, Harlen OG, Harris SA, Khalid S, Leung YM, Lonsdale R, Mulholland AJ, Pearson AR, Read DJ, Richardson RA. In pursuit of an accurate spatial and temporal model of biomolecules at the atomistic level: a perspective on computer simulation. Acta Crystallogr D Biol Crystallogr 2015; 71:162-72. [PMID: 25615870 PMCID: PMC4304696 DOI: 10.1107/s1399004714026777] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Accepted: 12/05/2014] [Indexed: 11/15/2022]
Abstract
Despite huge advances in the computational techniques available for simulating biomolecules at the quantum-mechanical, atomistic and coarse-grained levels, there is still a widespread perception amongst the experimental community that these calculations are highly specialist and are not generally applicable by researchers outside the theoretical community. In this article, the successes and limitations of biomolecular simulation and the further developments that are likely in the near future are discussed. A brief overview is also provided of the experimental biophysical methods that are commonly used to probe biomolecular structure and dynamics, and the accuracy of the information that can be obtained from each is compared with that from modelling. It is concluded that progress towards an accurate spatial and temporal model of biomacromolecules requires a combination of all of these biophysical techniques, both experimental and computational.
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Affiliation(s)
- Alan Gray
- The Edinburgh Parallel Computing Centre, The University of Edinburgh, Edinburgh EH9 3JZ, Scotland
| | - Oliver G. Harlen
- School of Mathematics, University of Leeds, Leeds LS2 9JT, England
| | - Sarah A. Harris
- School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, England
- Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds LS2 9JT, England
- Correspondence e-mail:
| | - Syma Khalid
- Faculty of Natural and Environmental Sciences, University of Southampton, Southampton SO17 1BJ, England
| | - Yuk Ming Leung
- Faculty of Natural and Environmental Sciences, University of Southampton, Southampton SO17 1BJ, England
| | - Richard Lonsdale
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
- Department of Chemistry, Philipps-Universität Marburg, Hans-Meerwein Strasse, 35032 Marburg, Germany
| | - Adrian J. Mulholland
- Centre for Computational Chemistry, University of Bristol, Bristol BS8 1TS, England
| | - Arwen R. Pearson
- Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds LS2 9JT, England
- Hamburg Centre for Ultrafast Imaging, University of Hamburg, Hamburg, Germany
| | - Daniel J. Read
- School of Mathematics, University of Leeds, Leeds LS2 9JT, England
| | - Robin A. Richardson
- School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, England
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11
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Abstract
Multibody software designed for mechanical engineering has been successfully employed in biomedical research for many years. For real time operation some biomedical researchers have also adapted game physics engines. However, these tools were built for other purposes and do not fully address the needs of biomedical researchers using them to analyze the dynamics of biological structures and make clinically meaningful recommendations. We are addressing this problem through the development of an open source, extensible, high performance toolkit including a multibody mechanics library aimed at the needs of biomedical researchers. The resulting code, Simbody, supports research in a variety of fields including neuromuscular, prosthetic, and biomolecular simulation, and related research such as biologically-inspired design and control of humanoid robots and avatars. Simbody is the dynamics engine behind OpenSim, a widely used biomechanics simulation application. This article reviews issues that arise uniquely in biomedical research, and reports on the architecture, theory, and computational methods Simbody uses to address them. By addressing these needs explicitly Simbody provides a better match to the needs of researchers than can be obtained by adaptation of mechanical engineering or gaming codes. Simbody is a community resource, free for any purpose. We encourage wide adoption and invite contributions to the code base at https://simtk.org/home/simbody.
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Affiliation(s)
| | - Ajay Seth
- Bioengineering, Stanford University, Stanford, CA, USA
| | - Scott L Delp
- Bioengineering, Stanford University, Stanford, CA, USA ; Mechanical Engineering, Stanford University, Stanford, CA, USA
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12
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Brooks B, Brooks C, MacKerell A, Nilsson L, Petrella R, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner A, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor R, Post C, Pu J, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York D, Karplus M. CHARMM: the biomolecular simulation program. J Comput Chem 2009; 30:1545-614. [PMID: 19444816 PMCID: PMC2810661 DOI: 10.1002/jcc.21287] [Citation(s) in RCA: 5858] [Impact Index Per Article: 390.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983.
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Affiliation(s)
- B.R. Brooks
- Laboratory of Computational Biology, National Heart, Lung, and
Blood Institute, National Institutes of Health, Bethesda, MD 20892
| | - C.L. Brooks
- Departments of Chemistry & Biophysics, University of
Michigan, Ann Arbor, MI 48109
| | - A.D. MacKerell
- Department of Pharmaceutical Sciences, School of Pharmacy,
University of Maryland, Baltimore, MD, 21201
| | - L. Nilsson
- Karolinska Institutet, Department of Biosciences and Nutrition,
SE-141 57, Huddinge, Sweden
| | - R.J. Petrella
- Department of Chemistry and Chemical Biology, Harvard University,
Cambridge, MA 02138
- Department of Medicine, Harvard Medical School, Boston, MA
02115
| | - B. Roux
- Department of Biochemistry and Molecular Biology, University of
Chicago, Gordon Center for Integrative Science, Chicago, IL 60637
| | - Y. Won
- Department of Chemistry, Hanyang University, Seoul
133–792 Korea
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - M. Karplus
- Department of Chemistry and Chemical Biology, Harvard University,
Cambridge, MA 02138
- Laboratoire de Chimie Biophysique, ISIS, Université de
Strasbourg, 67000 Strasbourg France
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13
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van der Kamp MW, Shaw KE, Woods CJ, Mulholland AJ. Biomolecular simulation and modelling: status, progress and prospects. J R Soc Interface 2008; 5 Suppl 3:S173-90. [PMID: 18611844 PMCID: PMC2706107 DOI: 10.1098/rsif.2008.0105.focus] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2008] [Revised: 06/05/2008] [Accepted: 06/06/2008] [Indexed: 11/12/2022] Open
Abstract
Molecular simulation is increasingly demonstrating its practical value in the investigation of biological systems. Computational modelling of biomolecular systems is an exciting and rapidly developing area, which is expanding significantly in scope. A range of simulation methods has been developed that can be applied to study a wide variety of problems in structural biology and at the interfaces between physics, chemistry and biology. Here, we give an overview of methods and some recent developments in atomistic biomolecular simulation. Some recent applications and theoretical developments are highlighted.
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Affiliation(s)
| | | | | | - Adrian J. Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of BristolBristol BS8 1TS, UK
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14
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Abstract
NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.
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Affiliation(s)
- James C Phillips
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Zacharias M. Protein-protein docking with a reduced protein model accounting for side-chain flexibility. Protein Sci 2003; 12:1271-82. [PMID: 12761398 PMCID: PMC2323887 DOI: 10.1110/ps.0239303] [Citation(s) in RCA: 243] [Impact Index Per Article: 11.6] [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] [Received: 11/15/2002] [Revised: 02/07/2003] [Accepted: 02/28/2003] [Indexed: 10/27/2022]
Abstract
A protein-protein docking approach has been developed based on a reduced protein representation with up to three pseudo atoms per amino acid residue. Docking is performed by energy minimization in rotational and translational degrees of freedom. The reduced protein representation allows an efficient search for docking minima on the protein surfaces within. During docking, an effective energy function between pseudo atoms has been used based on amino acid size and physico-chemical character. Energy minimization of protein test complexes in the reduced representation results in geometries close to experiment with backbone root mean square deviations (RMSDs) of approximately 1 to 3 A for the mobile protein partner from the experimental geometry. For most test cases, the energy-minimized experimental structure scores among the top five energy minima in systematic docking studies when using both partners in their bound conformations. To account for side-chain conformational changes in case of using unbound protein conformations, a multicopy approach has been used to select the most favorable side-chain conformation during the docking process. The multicopy approach significantly improves the docking performance, using unbound (apo) binding partners without a significant increase in computer time. For most docking test systems using unbound partners, and without accounting for any information about the known binding geometry, a solution within approximately 2 to 3.5 A RMSD of the full mobile partner from the experimental geometry was found among the 40 top-scoring complexes. The approach could be extended to include protein loop flexibility, and might also be useful for docking of modeled protein structures.
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Affiliation(s)
- Martin Zacharias
- Computational Biology, School of Engineering and Science, International University Bremen, 28759 Bremen, Germany.
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