1
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Tiemann JKS, Szczuka M, Bouarroudj L, Oussaren M, Garcia S, Howard RJ, Delemotte L, Lindahl E, Baaden M, Lindorff-Larsen K, Chavent M, Poulain P. MDverse, shedding light on the dark matter of molecular dynamics simulations. eLife 2024; 12:RP90061. [PMID: 39212001 PMCID: PMC11364437 DOI: 10.7554/elife.90061] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Abstract
The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.
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Affiliation(s)
- Johanna KS Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of CopenhagenCopenhagenDenmark
| | - Magdalena Szczuka
- Institut de Pharmacologie et Biologie Structurale, CNRS, Université de ToulouseToulouseFrance
| | - Lisa Bouarroudj
- Université Paris Cité, CNRS, Institut Jacques MonodParisFrance
| | | | | | - Rebecca J Howard
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm UniversityStockholmSweden
| | - Lucie Delemotte
- Department of applied physics, Science for Life Laboratory, KTH Royal Institute of TechnologyStockholmSweden
| | - Erik Lindahl
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm UniversityStockholmSweden
- Department of applied physics, Science for Life Laboratory, KTH Royal Institute of TechnologyStockholmSweden
| | - Marc Baaden
- Laboratoire de Biochimie Théorique, CNRS, Université Paris CitéParisFrance
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of CopenhagenCopenhagenDenmark
| | - Matthieu Chavent
- Institut de Pharmacologie et Biologie Structurale, CNRS, Université de ToulouseToulouseFrance
| | - Pierre Poulain
- Université Paris Cité, CNRS, Institut Jacques MonodParisFrance
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2
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Brown CM, Marrink SJ. Modeling membranes in situ. Curr Opin Struct Biol 2024; 87:102837. [PMID: 38744147 DOI: 10.1016/j.sbi.2024.102837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/26/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
Molecular dynamics simulations of cellular membranes have come a long way-from simple model lipid bilayers to multicomponent systems capturing the crowded and complex nature of real cell membranes. In this opinionated minireview, we discuss the current challenge to simulate the dynamics of membranes in their native environment, in situ, with the prospect of reaching the level of whole cells and cell organelles using an integrative modeling framework.
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Affiliation(s)
- Chelsea M Brown
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands. https://twitter.com/chelseabrowncg
| | - Siewert J Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands. s.j.marrinkrug.nl
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3
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Biriukov D, Vácha R. Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050. ACS PHYSICAL CHEMISTRY AU 2024; 4:302-313. [PMID: 39069976 PMCID: PMC11274290 DOI: 10.1021/acsphyschemau.4c00003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 03/15/2024] [Accepted: 03/18/2024] [Indexed: 07/30/2024]
Abstract
In the last quarter-century, the field of molecular dynamics (MD) has undergone a remarkable transformation, propelled by substantial enhancements in software, hardware, and underlying methodologies. In this Perspective, we contemplate the future trajectory of MD simulations and their possible look at the year 2050. We spotlight the pivotal role of artificial intelligence (AI) in shaping the future of MD and the broader field of computational physical chemistry. We outline critical strategies and initiatives that are essential for the seamless integration of such technologies. Our discussion delves into topics like multiscale modeling, adept management of ever-increasing data deluge, the establishment of centralized simulation databases, and the autonomous refinement, cross-validation, and self-expansion of these repositories. The successful implementation of these advancements requires scientific transparency, a cautiously optimistic approach to interpreting AI-driven simulations and their analysis, and a mindset that prioritizes knowledge-motivated research alongside AI-enhanced big data exploration. While history reminds us that the trajectory of technological progress can be unpredictable, this Perspective offers guidance on preparedness and proactive measures, aiming to steer future advancements in the most beneficial and successful direction.
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Affiliation(s)
- Denys Biriukov
- CEITEC
− Central European Institute of Technology, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- National
Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
| | - Robert Vácha
- CEITEC
− Central European Institute of Technology, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- National
Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- Department
of Condensed Matter Physics, Faculty of Science, Masaryk University, Kotlářská 267/2, 611 37 Brno, Czech
Republic
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4
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Bayarri G, Andrio P, Gelpí JL, Hospital A, Orozco M. Using interactive Jupyter Notebooks and BioConda for FAIR and reproducible biomolecular simulation workflows. PLoS Comput Biol 2024; 20:e1012173. [PMID: 38900779 PMCID: PMC11189206 DOI: 10.1371/journal.pcbi.1012173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2024] Open
Abstract
Interactive Jupyter Notebooks in combination with Conda environments can be used to generate FAIR (Findable, Accessible, Interoperable and Reusable/Reproducible) biomolecular simulation workflows. The interactive programming code accompanied by documentation and the possibility to inspect intermediate results with versatile graphical charts and data visualization is very helpful, especially in iterative processes, where parameters might be adjusted to a particular system of interest. This work presents a collection of FAIR notebooks covering various areas of the biomolecular simulation field, such as molecular dynamics (MD), protein-ligand docking, molecular checking/modeling, molecular interactions, and free energy perturbations. Workflows can be launched with myBinder or easily installed in a local system. The collection of notebooks aims to provide a compilation of demonstration workflows, and it is continuously updated and expanded with examples using new methodologies and tools.
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Affiliation(s)
- Genís Bayarri
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Pau Andrio
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Josep Lluís Gelpí
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Department of Biochemistry and Biomedicine, University of Barcelona, Barcelona, Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Barcelona, Spain
- Department of Biochemistry and Biomedicine, University of Barcelona, Barcelona, Spain
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5
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Tiemann JKS, Szczuka M, Bouarroudj L, Oussaren M, Garcia S, Howard RJ, Delemotte L, Lindahl E, Baaden M, Lindorff-Larsen K, Chavent M, Poulain P. MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.02.538537. [PMID: 37205542 PMCID: PMC10187166 DOI: 10.1101/2023.05.02.538537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2,000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.
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6
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Belghit H, Spivak M, Dauchez M, Baaden M, Jonquet-Prevoteau J. From complex data to clear insights: visualizing molecular dynamics trajectories. FRONTIERS IN BIOINFORMATICS 2024; 4:1356659. [PMID: 38665177 PMCID: PMC11043564 DOI: 10.3389/fbinf.2024.1356659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/14/2024] [Indexed: 04/28/2024] Open
Abstract
Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.
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Affiliation(s)
- Hayet Belghit
- Université de Reims Champagne-Ardenne, CNRS, MEDYC, Reims, France
| | - Mariano Spivak
- Université Paris Cité, CNRS, Laboratoire de Biochimie Théorique, Paris, France
| | - Manuel Dauchez
- Université de Reims Champagne-Ardenne, CNRS, MEDYC, Reims, France
| | - Marc Baaden
- Université Paris Cité, CNRS, Laboratoire de Biochimie Théorique, Paris, France
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7
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Caparotta M, Perez A. Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology. J Phys Chem B 2024; 128:2219-2227. [PMID: 38418288 DOI: 10.1021/acs.jpcb.3c04823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.
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Affiliation(s)
- Marcelo Caparotta
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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8
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Schmitt S, Kanagalingam G, Fleckenstein F, Froescher D, Hasse H, Stephan S. Extension of the MolMod Database to Transferable Force Fields. J Chem Inf Model 2023; 63:7148-7158. [PMID: 37947503 DOI: 10.1021/acs.jcim.3c01484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
MolMod, a web-based database for classical force fields for molecular simulations of fluids [Mol. Sim. 45, 10 (2019), 806-814], was extended to transferable force fields. Eight transferable force fields, including all-atom and united-atom type force fields, were implemented in the MolMod database: OPLS-UA, OPLS-AA, COMPASS, CHARMM, GROMOS, TraPPE, Potoff, and TAMie. These transferable force fields cover a large variety of chemical substance classes. The system is designed such that new transferable force fields can be readily integrated. A graphical user interface was implemented that enables the construction of molecules. The MolMod database compiles the force field for the specified component and force field type and provides the corresponding data and meta data as well as ready-to-use input files for the molecule for different simulation engines. This helps the user to flexibly choose molecular models and integrate them swiftly in their individual workflows, reducing risks of input errors in molecular simulations.
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Affiliation(s)
- Sebastian Schmitt
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern 67663, Germany
| | - Gajanan Kanagalingam
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern 67663, Germany
| | - Florian Fleckenstein
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern 67663, Germany
| | - Daniel Froescher
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern 67663, Germany
| | - Hans Hasse
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern 67663, Germany
| | - Simon Stephan
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern 67663, Germany
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9
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Kouba P, Kohout P, Haddadi F, Bushuiev A, Samusevich R, Sedlar J, Damborsky J, Pluskal T, Sivic J, Mazurenko S. Machine Learning-Guided Protein Engineering. ACS Catal 2023; 13:13863-13895. [PMID: 37942269 PMCID: PMC10629210 DOI: 10.1021/acscatal.3c02743] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/20/2023] [Indexed: 11/10/2023]
Abstract
Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.
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Affiliation(s)
- Petr Kouba
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Faculty of
Electrical Engineering, Czech Technical
University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic
| | - Pavel Kohout
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Faraneh Haddadi
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Anton Bushuiev
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Raman Samusevich
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Jiri Sedlar
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Jiri Damborsky
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Tomas Pluskal
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Josef Sivic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Stanislav Mazurenko
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
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10
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Aci-Sèche S, Bourg S, Bonnet P, Rebehmed J, de Brevern AG, Diharce J. A perspective on the sharing of docking data. Data Brief 2023; 49:109386. [PMID: 37492229 PMCID: PMC10365938 DOI: 10.1016/j.dib.2023.109386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 05/17/2023] [Accepted: 07/03/2023] [Indexed: 07/27/2023] Open
Abstract
Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.
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Affiliation(s)
- Samia Aci-Sèche
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311, Université d'Orléans BP 6759, Orléans Cedex 2, 45067, France
| | - Stéphane Bourg
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311, Université d'Orléans BP 6759, Orléans Cedex 2, 45067, France
| | - Pascal Bonnet
- Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d'Orléans 7311, Université d'Orléans BP 6759, Orléans Cedex 2, 45067, France
| | - Joseph Rebehmed
- Department of Computer Science and Mathematics, Lebanese, American University, Beirut, Lebanon
| | - Alexandre G. de Brevern
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, Biologie Intégrée du Globule Rouge, UMR_S 1134, DSIMB Bioinformatics team, 75014 Paris, France
| | - Julien Diharce
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, Biologie Intégrée du Globule Rouge, UMR_S 1134, DSIMB Bioinformatics team, 75014 Paris, France
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11
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Kanagalingam G, Schmitt S, Fleckenstein F, Stephan S. Data scheme and data format for transferable force fields for molecular simulation. Sci Data 2023; 10:495. [PMID: 37500652 PMCID: PMC10374650 DOI: 10.1038/s41597-023-02369-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 07/07/2023] [Indexed: 07/29/2023] Open
Abstract
A generalized data scheme for transferable classical force fields used in molecular simulations, i.e. molecular dynamics and Monte Carlo simulation, is presented. The data scheme is implemented in an SQL-based data format. The data scheme and data format is machine readable, re-usable, and interoperable. A transferable force field is a chemical construction plan specifying intermolecular and intramolecular interactions between different types of atoms or different chemical groups and can be used for building a model for a given component. The data scheme proposed in this work (named TUK-FFDat) formalizes digitally these chemical construction plans, i.e. transferable force fields. It can be applied to all-atom as well as united-atom transferable force fields. The general applicability of the data scheme is demonstrated for different types of force fields (TraPPE, OPLS-AA, and Potoff). Furthermore, conversion tools for translating the data scheme between .xls spread sheet format and the SQL-based data format are provided. The data format can readily be integrated in existing workflows, simulation engines, and force field databases as well as for linking such.
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Affiliation(s)
- Gajanan Kanagalingam
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Sebastian Schmitt
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Florian Fleckenstein
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Simon Stephan
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern, 67663, Germany.
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12
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Käser S, Vazquez-Salazar LI, Meuwly M, Töpfer K. Neural network potentials for chemistry: concepts, applications and prospects. DIGITAL DISCOVERY 2023; 2:28-58. [PMID: 36798879 PMCID: PMC9923808 DOI: 10.1039/d2dd00102k] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
Artificial Neural Networks (NN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on a larger scale.
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Affiliation(s)
- Silvan Käser
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
| | | | - Markus Meuwly
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
| | - Kai Töpfer
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
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13
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Wordom update 2: A user-friendly program for the analysis of molecular structures and conformational ensembles. Comput Struct Biotechnol J 2023; 21:1390-1402. [PMID: 36817953 PMCID: PMC9929209 DOI: 10.1016/j.csbj.2023.01.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 01/29/2023] Open
Abstract
We present the second update of Wordom, a user-friendly and efficient program for manipulation and analysis of conformational ensembles from molecular simulations. The actual update expands some of the existing modules and adds 21 new modules to the update 1 published in 2011. The new adds can be divided into three sets that: 1) analyze atomic fluctuations and structural communication; 2) explore ion-channel conformational dynamics and ionic translocation; and 3) compute geometrical indices of structural deformation. Set 1 serves to compute correlations of motions, find geometrically stable domains, identify a dynamically invariant core, find changes in domain-domain separation and mutual orientation, perform wavelet analysis of large-scale simulations, process the output of principal component analysis of atomic fluctuations, perform functional mode analysis, infer regions of mechanical rigidity, analyze overall fluctuations, and perform the perturbation response scanning. Set 2 includes modules specific for ion channels, which serve to monitor the pore radius as well as water or ion fluxes, and measure functional collective motions like receptor twisting or tilting angles. Finally, set 3 includes tools to monitor structural deformations by computing angles, perimeter, area, volume, β-sheet curvature, radial distribution function, and center of mass. The ring perception module is also included, helpful to monitor supramolecular self-assemblies. This update places Wordom among the most suitable, complete, user-friendly, and efficient software for the analysis of biomolecular simulations. The source code of Wordom and the relative documentation are available under the GNU general public license at http://wordom.sf.net.
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14
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Iwasa JH, Lyons B, Johnson GT. The dawn of interoperating spatial models in cell biology. Curr Opin Biotechnol 2022; 78:102838. [PMID: 36402095 DOI: 10.1016/j.copbio.2022.102838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 06/01/2022] [Accepted: 10/07/2022] [Indexed: 11/18/2022]
Abstract
Spatial simulations are becoming an increasingly ubiquitous component in the cycle of discovery, experimentation, and communication across the sciences. In cell biology, many researchers share a vision of developing multiscale models that recapitulate observable behaviors spanning from atoms to cells to tissues. For this dream to become a reality, however, simulation technologies must provide a means for integration and interoperability as they advance. Already, the field has developed numerous methods that span scales of length, time, and complexity to create an extensive body of effective simulation approaches, and although these approaches rarely interoperate, they collectively cover a large spectrum of knowledge that future models may handle in a more unified manner. Here, we discuss the importance of making the data, workflows, and outputs of spatial simulations shareable and interoperable; and how democratization could encourage diverse biologists to participate more easily in developing models to advance our understanding of biological systems.
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Affiliation(s)
| | - Blair Lyons
- Visualization & Data Integration, Allen Institute for Cell Science, USA
| | - Graham T Johnson
- Visualization & Data Integration, Allen Institute for Cell Science, USA.
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15
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Shamaprasad P, Frame CO, Moore TC, Yang A, Iacovella CR, Bouwstra JA, Bunge AL, McCabe C. Using molecular simulation to understand the skin barrier. Prog Lipid Res 2022; 88:101184. [PMID: 35988796 PMCID: PMC10116345 DOI: 10.1016/j.plipres.2022.101184] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 11/15/2022]
Abstract
Skin's effectiveness as a barrier to permeation of water and other chemicals rests almost entirely in the outermost layer of the epidermis, the stratum corneum (SC), which consists of layers of corneocytes surrounded by highly organized lipid lamellae. As the only continuous path through the SC, transdermal permeation necessarily involves diffusion through these lipid layers. The role of the SC as a protective barrier is supported by its exceptional lipid composition consisting of ceramides (CERs), cholesterol (CHOL), and free fatty acids (FFAs) and the complete absence of phospholipids, which are present in most biological membranes. Molecular simulation, which provides molecular level detail of lipid configurations that can be connected with barrier function, has become a popular tool for studying SC lipid systems. We review this ever-increasing body of literature with the goals of (1) enabling the experimental skin community to understand, interpret and use the information generated from the simulations, (2) providing simulation experts with a solid background in the chemistry of SC lipids including the composition, structure and organization, and barrier function, and (3) presenting a state of the art picture of the field of SC lipid simulations, highlighting the difficulties and best practices for studying these systems, to encourage the generation of robust reproducible studies in the future. This review describes molecular simulation methodology and then critically examines results derived from simulations using atomistic and then coarse-grained models.
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Affiliation(s)
- Parashara Shamaprasad
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235-1604, United States of America; Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, TN 37235-1604, United States of America
| | - Chloe O Frame
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235-1604, United States of America; Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, TN 37235-1604, United States of America
| | - Timothy C Moore
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235-1604, United States of America; Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, TN 37235-1604, United States of America
| | - Alexander Yang
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235-1604, United States of America; Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, TN 37235-1604, United States of America
| | - Christopher R Iacovella
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235-1604, United States of America; Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, TN 37235-1604, United States of America
| | - Joke A Bouwstra
- Division of BioTherapeutics, LACDR, Leiden University, 2333 CC Leiden, the Netherlands
| | - Annette L Bunge
- Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO 80401, United States of America
| | - Clare McCabe
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235-1604, United States of America; Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, TN 37235-1604, United States of America; School of Engineering and Physical Science, Heriot-Watt University, Edinburgh, United Kingdom.
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16
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Antila HS, Kav B, Miettinen MS, Martinez-Seara H, Jungwirth P, Ollila OHS. Emerging Era of Biomolecular Membrane Simulations: Automated Physically-Justified Force Field Development and Quality-Evaluated Databanks. J Phys Chem B 2022. [DOI: 10.1021/acs.jpcb.2c01954] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Hanne S. Antila
- Department of Biomaterials, Max Planck Institute of Colloids and Interfaces, 14424 Potsdam, Germany
| | - Batuhan Kav
- Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum
Jülich, Wilhelm-Johnen-Str., 52425 Jülich, Germany
| | - Markus S. Miettinen
- Computational Biology Unit, Department of Informatics, University of Bergen, 5008 Bergen, Norway
- Department of Chemistry, University of Bergen, 5020 Bergen, Norway
| | - Hector Martinez-Seara
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo nam. 2, 16000 Prague 6, Czech Republic
| | - Pavel Jungwirth
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo nam. 2, 16000 Prague 6, Czech Republic
| | - O. H. Samuli Ollila
- Institute of Biotechonology, University of Helsinki, Helsinki 00014, Finland
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17
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Kampfrath M, Staritzbichler R, Hernández GP, Rose AS, Tiemann JKS, Scheuermann G, Wiegreffe D, Hildebrand PW. MDsrv: visual sharing and analysis of molecular dynamics simulations. Nucleic Acids Res 2022; 50:W483-W489. [PMID: 35639717 PMCID: PMC9252803 DOI: 10.1093/nar/gkac398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/02/2022] [Accepted: 05/05/2022] [Indexed: 11/24/2022] Open
Abstract
Molecular dynamics simulation is a proven technique for computing and visualizing the time-resolved motion of macromolecules at atomic resolution. The MDsrv is a tool that streams MD trajectories and displays them interactively in web browsers without requiring advanced skills, facilitating interactive exploration and collaborative visual analysis. We have now enhanced the MDsrv to further simplify the upload and sharing of MD trajectories and improve their online viewing and analysis. With the new instance, the MDsrv simplifies the creation of sessions, which allows the exchange of MD trajectories with preset representations and perspectives. An important innovation is that the MDsrv can now access and visualize trajectories from remote datasets, which greatly expands its applicability and use, as the data no longer needs to be accessible on a local server. In addition, initial analyses such as sequence or structure alignments, distance measurements, or RMSD calculations have been implemented, which optionally support visual analysis. Finally, based on Mol*, MDsrv now provides faster and more efficient visualization of even large trajectories compared to its predecessor tool NGL.
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Affiliation(s)
- Michelle Kampfrath
- Image and Signal Processing Group, Department of Computer Science, Leipzig University, Augustusplatz 10, 04109 Leipzig, Germany
| | - René Staritzbichler
- Institute for Medical Physics and Biophysics, Medical Faculty, Leipzig University, Härtelstraße 16-18, 04107 Leipzig, Germany
| | - Guillermo Pérez Hernández
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Physics and Biophysics, Berlin, Germany
| | | | - Johanna K S Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen N., Denmark
| | - Gerik Scheuermann
- Image and Signal Processing Group, Department of Computer Science, Leipzig University, Augustusplatz 10, 04109 Leipzig, Germany
| | - Daniel Wiegreffe
- Image and Signal Processing Group, Department of Computer Science, Leipzig University, Augustusplatz 10, 04109 Leipzig, Germany
| | - Peter W Hildebrand
- Institute for Medical Physics and Biophysics, Medical Faculty, Leipzig University, Härtelstraße 16-18, 04107 Leipzig, Germany.,Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Physics and Biophysics, Berlin, Germany.,Berlin Institute of Health, 10178 Berlin, Germany
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18
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Lemay-St-Denis C, Doucet N, Pelletier JN. Integrating dynamics into enzyme engineering. Protein Eng Des Sel 2022; 35:6842866. [PMID: 36416215 DOI: 10.1093/protein/gzac015] [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] [Received: 06/21/2022] [Revised: 11/02/2022] [Accepted: 11/06/2022] [Indexed: 11/24/2022] Open
Abstract
Enzyme engineering has become a widely adopted practice in research labs and industry. In parallel, the past decades have seen tremendous strides in characterizing the dynamics of proteins, using a growing array of methodologies. Importantly, links have been established between the dynamics of proteins and their function. Characterizing the dynamics of an enzyme prior to, and following, its engineering is beginning to inform on the potential of 'dynamic engineering', i.e. the rational modification of protein dynamics to alter enzyme function. Here we examine the state of knowledge at the intersection of enzyme engineering and protein dynamics, describe current challenges and highlight pioneering work in the nascent area of dynamic engineering.
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Affiliation(s)
- Claudèle Lemay-St-Denis
- PROTEO, The Québec Network for Research on Protein, Function, Engineering and Applications, Quebec, QC, Canada
- CGCC, Center in Green Chemistry and Catalysis, Montreal, QC, Canada
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada
| | - Nicolas Doucet
- PROTEO, The Québec Network for Research on Protein, Function, Engineering and Applications, Quebec, QC, Canada
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique (INRS), Université du Québec, Laval, QC, Canada
| | - Joelle N Pelletier
- PROTEO, The Québec Network for Research on Protein, Function, Engineering and Applications, Quebec, QC, Canada
- CGCC, Center in Green Chemistry and Catalysis, Montreal, QC, Canada
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada
- Chemistry Department, Université de Montréal, Montreal, QC, Canada
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19
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Stephan S, Dyga M, Alabd Alhafez I, Lenhard J, Urbassek HM, Hasse H. Reproducibility of atomistic friction computer experiments: a molecular dynamics simulation study. MOLECULAR SIMULATION 2021. [DOI: 10.1080/08927022.2021.1987430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Simon Stephan
- Laboratory of Engineering Thermodynamics (LTD), TU Kaiserslautern, Kaiserslautern, Germany
| | - Maximilian Dyga
- Laboratory of Engineering Thermodynamics (LTD), TU Kaiserslautern, Kaiserslautern, Germany
| | - Iyad Alabd Alhafez
- Physics Department and Research Center (OPTIMAS), TU Kaiserslautern, Kaiserslautern, Germany
| | - Johannes Lenhard
- Laboratory of Engineering Thermodynamics (LTD), TU Kaiserslautern, Kaiserslautern, Germany
| | - Herbert M. Urbassek
- Physics Department and Research Center (OPTIMAS), TU Kaiserslautern, Kaiserslautern, Germany
| | - Hans Hasse
- Laboratory of Engineering Thermodynamics (LTD), TU Kaiserslautern, Kaiserslautern, Germany
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20
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Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller KR, Tkatchenko A. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems. Chem Rev 2021; 121:9816-9872. [PMID: 34232033 PMCID: PMC8391798 DOI: 10.1021/acs.chemrev.1c00107] [Citation(s) in RCA: 211] [Impact Index Per Article: 70.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Indexed: 12/23/2022]
Abstract
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
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Affiliation(s)
- John A. Keith
- Department
of Chemical and Petroleum Engineering Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Valentin Vassilev-Galindo
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Bingqing Cheng
- Accelerate
Programme for Scientific Discovery, Department
of Computer Science and Technology, 15 J. J. Thomson Avenue, Cambridge CB3 0FD, United Kingdom
| | - Stefan Chmiela
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Michael Gastegger
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Klaus-Robert Müller
- Machine
Learning Group, Technische Universität
Berlin, 10587, Berlin, Germany
- Department
of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Korea
- Max-Planck-Institut für Informatik, 66123 Saarbrücken, Germany
- Google Research, Brain Team, 10117 Berlin, Germany
| | - Alexandre Tkatchenko
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
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21
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Martinez X, Baaden M. UnityMol prototype for FAIR sharing of molecular-visualization experiences: from pictures in the cloud to collaborative virtual reality exploration in immersive 3D environments. Acta Crystallogr D Struct Biol 2021; 77:746-754. [PMID: 34076589 PMCID: PMC8171070 DOI: 10.1107/s2059798321002941] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 03/19/2021] [Indexed: 11/14/2022] Open
Abstract
Motivated by the current COVID-19 pandemic, which has spurred a substantial flow of structural data, the use of molecular-visualization experiences to make these data sets accessible to a broad audience is described. Using a variety of technology vectors related to the cloud, 3D and virtual reality gear, how to share curated visualizations of structural biology, modeling and/or bioinformatics data sets for interactive and collaborative exploration is examined. FAIR is discussed as an overarching principle for sharing such visualizations. Four initial example scenes related to recent COVID-19 structural data are provided, together with a ready-to-use (and share) implementation in the UnityMol software.
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Affiliation(s)
- Xavier Martinez
- CNRS, Université de Paris, UPR 9080, Laboratoire de Biochimie Théorique, 13 Rue Pierre et Marie Curie, 75005 Paris, France
- Institut de Biologie Physico-Chimique–Fondation Edmond de Rothschild, PSL Research University, Paris, France
| | - Marc Baaden
- CNRS, Université de Paris, UPR 9080, Laboratoire de Biochimie Théorique, 13 Rue Pierre et Marie Curie, 75005 Paris, France
- Institut de Biologie Physico-Chimique–Fondation Edmond de Rothschild, PSL Research University, Paris, France
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22
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Antila HS, M. Ferreira T, Ollila OHS, Miettinen MS. Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics. J Chem Inf Model 2021; 61:938-949. [PMID: 33496579 PMCID: PMC7903423 DOI: 10.1021/acs.jcim.0c01299] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Indexed: 01/08/2023]
Abstract
Molecular dynamics (MD) simulations are widely used to monitor time-resolved motions of biomacromolecules, although it often remains unknown how closely the conformational dynamics correspond to those occurring in real life. Here, we used a large set of open-access MD trajectories of phosphatidylcholine (PC) lipid bilayers to benchmark the conformational dynamics in several contemporary MD models (force fields) against nuclear magnetic resonance (NMR) data available in the literature: effective correlation times and spin-lattice relaxation rates. We found none of the tested MD models to fully reproduce the conformational dynamics. That said, the dynamics in CHARMM36 and Slipids are more realistic than in the Amber Lipid14, OPLS-based MacRog, and GROMOS-based Berger force fields, whose sampling of the glycerol backbone conformations is too slow. The performance of CHARMM36 persists when cholesterol is added to the bilayer, and when the hydration level is reduced. However, for conformational dynamics of the PC headgroup, both with and without cholesterol, Slipids provides the most realistic description because CHARMM36 overestimates the relative weight of ∼1 ns processes in the headgroup dynamics. We stress that not a single new simulation was run for the present work. This demonstrates the worth of open-access MD trajectory databanks for the indispensable step of any serious MD study: benchmarking the available force fields. We believe this proof of principle will inspire other novel applications of MD trajectory databanks and thus aid in developing biomolecular MD simulations into a true computational microscope-not only for lipid membranes but for all biomacromolecular systems.
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Affiliation(s)
- Hanne S. Antila
- Department
of Theory and Bio-Systems, Max Planck Institute
of Colloids and Interfaces, 14424 Potsdam, Germany
| | - Tiago M. Ferreira
- NMR
Group−Institute for Physics, Martin-Luther
University Halle-Wittenberg, 06120 Halle (Saale), Germany
| | | | - Markus S. Miettinen
- Department
of Theory and Bio-Systems, Max Planck Institute
of Colloids and Interfaces, 14424 Potsdam, Germany
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23
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Sehnal D, Svobodová R, Berka K, Rose AS, Burley SK, Velankar S, Koča J. High-performance macromolecular data delivery and visualization for the web. Acta Crystallogr D Struct Biol 2020; 76:1167-1173. [PMID: 33263322 PMCID: PMC7709201 DOI: 10.1107/s2059798320014515] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/01/2020] [Indexed: 11/11/2022] Open
Abstract
Biomacromolecular structural data make up a vital and crucial scientific resource that has grown not only in terms of its amount but also in its size and complexity. Furthermore, these data are accompanied by large and increasing amounts of experimental data. Additionally, the macromolecular data are enriched with value-added annotations describing their biological, physicochemical and structural properties. Today, the scientific community requires fast and fully interactive web visualization to exploit this complex structural information. This article provides a survey of the available cutting-edge web services that address this challenge. Specifically, it focuses on data-delivery problems, discusses the visualization of a single structure, including experimental data and annotations, and concludes with a focus on the results of molecular-dynamics simulations and the visualization of structural ensembles.
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Affiliation(s)
- David Sehnal
- CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
| | - Radka Svobodová
- CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
| | - Karel Berka
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacký University Olomouc, Šlechtitelů 241/27, 779 00 Olomouc, Czech Republic
| | - Alexander S. Rose
- Research Collaboratory for Structural Bioinformatics (RCSB), San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, San Diego, CA 92093-0743, USA
| | - Stephen K. Burley
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854-8076, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, 195 Little Albany Street, New Brunswick, NJ 08903-2681, USA
- RCSB Protein Data Bank, San Diego Supercomputer Center and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0654, USA
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
| | - Jaroslav Koča
- CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
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24
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Torrens-Fontanals M, Stepniewski TM, Aranda-García D, Morales-Pastor A, Medel-Lacruz B, Selent J. How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs. Int J Mol Sci 2020; 21:E5933. [PMID: 32824756 PMCID: PMC7460635 DOI: 10.3390/ijms21165933] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/11/2020] [Accepted: 08/15/2020] [Indexed: 01/08/2023] Open
Abstract
G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.
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Affiliation(s)
- Mariona Torrens-Fontanals
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Tomasz Maciej Stepniewski
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
- InterAx Biotech AG, PARK innovAARE, 5234 Villigen, Switzerland
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-093 Warsaw, Poland
| | - David Aranda-García
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Adrián Morales-Pastor
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Brian Medel-Lacruz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
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25
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Stephan S, Hasse H. Enrichment at vapour–liquid interfaces of mixtures: establishing a link between nanoscopic and macroscopic properties. INT REV PHYS CHEM 2020. [DOI: 10.1080/0144235x.2020.1777705] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Simon Stephan
- Laboratory of Engineering Thermodynamics (LTD), TU Kaiserslautern, Kaiserslautern, Germany
| | - Hans Hasse
- Laboratory of Engineering Thermodynamics (LTD), TU Kaiserslautern, Kaiserslautern, Germany
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26
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Gygli G, Pleiss J. Simulation Foundry: Automated and F.A.I.R. Molecular Modeling. J Chem Inf Model 2020; 60:1922-1927. [DOI: 10.1021/acs.jcim.0c00018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Gudrun Gygli
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Juergen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
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27
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Abriata LA, Lepore R, Dal Peraro M. About the need to make computational models of biological macromolecules available and discoverable. Bioinformatics 2020; 36:2952-2954. [DOI: 10.1093/bioinformatics/btaa086] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 01/13/2020] [Accepted: 02/06/2020] [Indexed: 12/19/2022] Open
Affiliation(s)
- Luciano A Abriata
- Laboratory for Biomolecular Modeling
- Protein Production and Structure Core Facility, School of Life Sciences, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland
| | - Rosalba Lepore
- BSC-CNS Barcelona Supercomputing Center, Barcelona, Spain
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Hospital A, Battistini F, Soliva R, Gelpí JL, Orozco M. Surviving the deluge of biosimulation data. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1449] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Adam Hospital
- Institut de Recerca Biomèdica, IRB Barcelona, The Barcelona Institute of Science and Technology Joint IRB‐BSC Program in Computational Biology Barcelona Spain
| | - Federica Battistini
- Institut de Recerca Biomèdica, IRB Barcelona, The Barcelona Institute of Science and Technology Joint IRB‐BSC Program in Computational Biology Barcelona Spain
| | | | - Josep Lluis Gelpí
- Barcelona Supercomputing Center Join IRB‐BSC Program in Computational Biology Barcelona Spain
- Departament de Bioquímica i Biomedicina Universitat de Barcelona Barcelona Spain
| | - Modesto Orozco
- Institut de Recerca Biomèdica, IRB Barcelona, The Barcelona Institute of Science and Technology Joint IRB‐BSC Program in Computational Biology Barcelona Spain
- Departament de Bioquímica i Biomedicina Universitat de Barcelona Barcelona Spain
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