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Pokojná H, Kozlíková B, Berry D, Kriglstein S, Furmanová K. Seeing the unseen: Comparison study of representation approaches for biochemical processes in education. PLoS One 2023; 18:e0293592. [PMID: 37930950 PMCID: PMC10627439 DOI: 10.1371/journal.pone.0293592] [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] [Received: 03/19/2023] [Accepted: 10/06/2023] [Indexed: 11/08/2023] Open
Abstract
The representations of biochemical processes must balance visual portrayals with descriptive content to be an effective learning tool. To determine what type of representation is the most suitable for education, we designed five different representations of adenosine triphosphate (ATP) synthesis and examined how they are perceived. Our representations consisted of an overview of the process in a detailed and abstract illustrative format, continuous video formats with and without narration, and a combined illustrative overview with dynamic components. The five representations were evaluated by non-experts who were randomly assigned one of them and experts who viewed and compared all five representations. Subsequently, we conducted a focus group on the outcomes of these evaluations, which gave insight into possible explanations of our results, where the non-experts preferred the detailed static representation and found the narrated video least helpful, in contradiction to the experts who favored the narrated video the most.
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Affiliation(s)
- Hana Pokojná
- Department of Visual Computing, Masaryk University, Brno, Czech Republic
| | - Barbora Kozlíková
- Department of Visual Computing, Masaryk University, Brno, Czech Republic
| | - Drew Berry
- Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
| | - Simone Kriglstein
- Department of Visual Computing, Masaryk University, Brno, Czech Republic
- AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | - Katarína Furmanová
- Department of Visual Computing, Masaryk University, Brno, Czech Republic
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2
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Shmilovich K, Ferguson AL. Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables. J Phys Chem A 2023; 127:3497-3517. [PMID: 37036804 DOI: 10.1021/acs.jpca.3c00505] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Molecular dynamics simulations of microscopic phenomena are limited by the short integration time steps which are required for numerical stability but which limit the practically achievable simulation time scales. Collective variable (CV) enhanced sampling techniques apply biases to predefined collective coordinates to promote barrier crossing, phase space exploration, and sampling of rare events. The efficacy of these techniques is contingent on the selection of good CVs correlated with the molecular motions governing the long-time dynamical evolution of the system. In this work, we introduce Girsanov Reweighting Enhanced Sampling Technique (GREST) as an adaptive sampling scheme that interleaves rounds of data-driven slow CV discovery and enhanced sampling along these coordinates. Since slow CVs are inherently dynamical quantities, a key ingredient in our approach is the use of both thermodynamic and dynamical Girsanov reweighting corrections for rigorous estimation of slow CVs from biased simulation data. We demonstrate our approach on a toy 1D 4-well potential, a simple biomolecular system alanine dipeptide, and the Trp-Leu-Ala-Leu-Leu (WLALL) pentapeptide. In each case GREST learns appropriate slow CVs and drives sampling of all thermally accessible metastable states starting from zero prior knowledge of the system. We make GREST accessible to the community via a publicly available open source Python package.
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Affiliation(s)
- Kirill Shmilovich
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
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3
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Oliveira MP, Hünenberger PH. Influence of the Lennard-Jones Combination Rules on the Simulated Properties of Organic Liquids at Optimal Force-Field Parametrization. J Chem Theory Comput 2023; 19:2048-2063. [PMID: 36920838 PMCID: PMC10100539 DOI: 10.1021/acs.jctc.2c01170] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
We recently introduced the CombiFF scheme [Oliveira et al., J. Chem. Theory Comput. 2020, 16, 7525], an approach for the automated refinement of force-field parameters against experimental condensed-phase data for large compound families. Using this scheme, once the time-consuming task of target-data selection and curation has been performed, the force-field optimization itself is both straightforward and fast. As a result, CombiFF provides an ideal framework for evaluating the influence of functional-form decisions on the accuracy of a force field at an optimal level of parametrization. We already used this approach to assess the effect of using an all-atom representation compared to united-atom representations in the force field [Oliveira et al., J. Chem. Theory Comput. 2022, 18, 6757]. Here, CombiFF is applied to assess the effect of three Lennard-Jones combination rules, geometric-mean (GM), Lorentz-Berthelot (LB), or Waldman-Hagler (WH), on the simulated properties of organic liquids. The comparison is performed in terms of the experimental liquid density ρliq, vaporization enthalpy ΔHvap, surface-tension coefficient γ, static relative dielectric permittivity ϵ, and self-diffusion coefficient D. The calibrations of the three force-field variants are carried out independently against 2044 experimental values for ρliq, and ΔHvap concerning 1516 compounds. The resulting root-mean-square deviations from experiment are 30.0, 26.9, and 36.7 kg m-3 for ρliq and 2.8, 2.8, and 2.9 kJ mol-1 for ΔHvap, when applying the GM, LB, and WH combination rules, respectively. In terms of these (and the other) properties, the three combination rules perform comparatively well, with the GM and LB results being more similar to each other and slightly more accurate compared to experiment. In contrast, the use of distinct combination rules for the parameter calibration and property calculation leads to much larger errors.
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Affiliation(s)
- Marina P Oliveira
- Laboratorium für Physikalische Chemie, ETH Zürich, CH-8093 Zürich, Switzerland
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4
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Shmilovich K, Stieffenhofer M, Charron NE, Hoffmann M. Temporally Coherent Backmapping of Molecular Trajectories From Coarse-Grained to Atomistic Resolution. J Phys Chem A 2022; 126:9124-9139. [PMID: 36417670 PMCID: PMC9743211 DOI: 10.1021/acs.jpca.2c07716] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Coarse-graining offers a means to extend the achievable time and length scales of molecular dynamics simulations beyond what is practically possible in the atomistic regime. Sampling molecular configurations of interest can be done efficiently using coarse-grained simulations, from which meaningful physicochemical information can be inferred if the corresponding all-atom configurations are reconstructed. However, this procedure of backmapping to reintroduce the lost atomistic detail into coarse-grain structures has proven a challenging task due to the many feasible atomistic configurations that can be associated with one coarse-grain structure. Existing backmapping methods are strictly frame-based, relying on either heuristics to replace coarse-grain particles with atomic fragments and subsequent relaxation or parametrized models to propose atomic coordinates separately and independently for each coarse-grain structure. These approaches neglect information from previous trajectory frames that is critical to ensuring temporal coherence of the backmapped trajectory, while also offering information potentially helpful to producing higher-fidelity atomic reconstructions. In this work, we present a deep learning-enabled data-driven approach for temporally coherent backmapping that explicitly incorporates information from preceding trajectory structures. Our method trains a conditional variational autoencoder to nondeterministically reconstruct atomistic detail conditioned on both the target coarse-grain configuration and the previously reconstructed atomistic configuration. We demonstrate our backmapping approach on two exemplar biomolecular systems: alanine dipeptide and the miniprotein chignolin. We show that our backmapped trajectories accurately recover the structural, thermodynamic, and kinetic properties of the atomistic trajectory data.
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Affiliation(s)
- Kirill Shmilovich
- Pritzker
School of Molecular Engineering, University
of Chicago, Chicago, Illinois60637, United States,E-mail:
| | | | - Nicholas E. Charron
- Weiss
School of Natural Sciences, Department of Physics and Astronomy, Rice University, Houston, Texas77005, United States,Department
of Physics, Freie Universität Berlin, Berlin14195, Germany
| | - Moritz Hoffmann
- Fachbereich
Mathematik und Informatik, Freie Universität
Berlin, Berlin14195, Germany
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5
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Oliveira MP, Gonçalves YMH, Ol Gheta SK, Rieder SR, Horta BAC, Hünenberger PH. Comparison of the United- and All-Atom Representations of (Halo)alkanes Based on Two Condensed-Phase Force Fields Optimized against the Same Experimental Data Set. J Chem Theory Comput 2022; 18:6757-6778. [PMID: 36190354 DOI: 10.1021/acs.jctc.2c00524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The level of accuracy that can be achieved by a force field is influenced by choices made in the interaction-function representation and in the relevant simulation parameters. These choices, referred to here as functional-form variants (FFVs), include for example the model resolution, the charge-derivation procedure, the van der Waals combination rules, the cutoff distance, and the treatment of the long-range interactions. Ideally, assessing the effect of a given FFV on the intrinsic accuracy of the force-field representation requires that only the specific FFV is changed and that this change is performed at an optimal level of parametrization, a requirement that may prove extremely challenging to achieve in practice. Here, we present a first attempt at such a comparison for one specific FFV, namely the choice of a united-atom (UA) versus an all-atom (AA) resolution in a force field for saturated acyclic (halo)alkanes. Two force-field versions (UA vs AA) are optimized in an automated way using the CombiFF approach against 961 experimental values for the pure-liquid densities ρliq and vaporization enthalpies ΔHvap of 591 compounds. For the AA force field, the torsional and third-neighbor Lennard-Jones parameters are also refined based on quantum-mechanical rotational-energy profiles. The comparison between the UA and AA resolutions is also extended to properties that have not been included as parameterization targets, namely the surface-tension coefficient γ, the isothermal compressibility κT, the isobaric thermal-expansion coefficient αP, the isobaric heat capacity cP, the static relative dielectric permittivity ϵ, the self-diffusion coefficient D, the shear viscosity η, the hydration free energy ΔGwat, and the free energy of solvation ΔGche in cyclohexane. For the target properties ρliq and ΔHvap, the UA and AA resolutions reach very similar levels of accuracy after optimization. For the nine other properties, the AA representation leads to more accurate results in terms of η; comparably accurate results in terms of γ, κT, αP, ϵ, D, and ΔGche; and less accurate results in terms of cP and ΔGwat. This work also represents a first step toward the calibration of a GROMOS-compatible force field at the AA resolution.
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Affiliation(s)
- Marina P Oliveira
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Yan M H Gonçalves
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - S Kashef Ol Gheta
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Salomé R Rieder
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Bruno A C Horta
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Philippe H Hünenberger
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
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6
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Sakshuwong S, Weir H, Raucci U, Martínez TJ. Bringing chemical structures to life with augmented reality, machine learning, and quantum chemistry. J Chem Phys 2022; 156:204801. [DOI: 10.1063/5.0090482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Visualizing 3D molecular structures is crucial to understanding and predicting their chemical behavior. However, static 2D hand-drawn skeletal structures remain the preferred method of chemical communication. Here, we combine cutting-edge technologies in augmented reality (AR), machine learning, and computational chemistry to develop MolAR, an open-source mobile application for visualizing molecules in AR directly from their hand-drawn chemical structures. Users can also visualize any molecule or protein directly from its name or protein data bank ID and compute chemical properties in real time via quantum chemistry cloud computing. MolAR provides an easily accessible platform for the scientific community to visualize and interact with 3D molecular structures in an immersive and engaging way.
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Affiliation(s)
- Sukolsak Sakshuwong
- Department of Management Science and Engineering, Stanford University, Stanford, California 94305, USA
| | - Hayley Weir
- Stanford PULSE Institute, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Umberto Raucci
- Stanford PULSE Institute, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Todd J. Martínez
- Stanford PULSE Institute, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
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7
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Oliveira MP, Andrey M, Rieder SR, Kern L, Hahn DF, Riniker S, Horta BAC, Hünenberger PH. Systematic Optimization of a Fragment-Based Force Field against Experimental Pure-Liquid Properties Considering Large Compound Families: Application to Saturated Haloalkanes. J Chem Theory Comput 2020; 16:7525-7555. [DOI: 10.1021/acs.jctc.0c00683] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Marina P. Oliveira
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Maurice Andrey
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Salomé R. Rieder
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Leyla Kern
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - David F. Hahn
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Sereina Riniker
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Bruno A. C. Horta
- Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
| | - Philippe H. Hünenberger
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Honggerberg, HCI, CH-8093 Zürich, Switzerland
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8
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Kashefolgheta S, Oliveira MP, Rieder SR, Horta BAC, Acree WE, Hünenberger PH. Evaluating Classical Force Fields against Experimental Cross-Solvation Free Energies. J Chem Theory Comput 2020; 16:7556-7580. [DOI: 10.1021/acs.jctc.0c00688] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Sadra Kashefolgheta
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Marina P. Oliveira
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Salomé R. Rieder
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Bruno A. C. Horta
- Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
| | - William E. Acree
- Department of Chemistry, University of North Texas, 1155 Union Circle Drive #305070, Denton, Texas 76203, United States
| | - Philippe H. Hünenberger
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
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9
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Abriata LA. Building blocks for commodity augmented reality-based molecular visualization and modeling in web browsers. PeerJ Comput Sci 2020; 6:e260. [PMID: 33816912 PMCID: PMC7924717 DOI: 10.7717/peerj-cs.260] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 01/22/2020] [Indexed: 06/12/2023]
Abstract
For years, immersive interfaces using virtual and augmented reality (AR) for molecular visualization and modeling have promised a revolution in the way how we teach, learn, communicate and work in chemistry, structural biology and related areas. However, most tools available today for immersive modeling require specialized hardware and software, and are costly and cumbersome to set up. These limitations prevent wide use of immersive technologies in education and research centers in a standardized form, which in turn prevents large-scale testing of the actual effects of such technologies on learning and thinking processes. Here, I discuss building blocks for creating marker-based AR applications that run as web pages on regular computers, and explore how they can be exploited to develop web content for handling virtual molecular systems in commodity AR with no more than a webcam- and internet-enabled computer. Examples span from displaying molecules, electron microscopy maps and molecular orbitals with minimal amounts of HTML code, to incorporation of molecular mechanics, real-time estimation of experimental observables and other interactive resources using JavaScript. These web apps provide virtual alternatives to physical, plastic-made molecular modeling kits, where the computer augments the experience with information about spatial interactions, reactivity, energetics, etc. The ideas and prototypes introduced here should serve as starting points for building active content that everybody can utilize online at minimal cost, providing novel interactive pedagogic material in such an open way that it could enable mass-testing of the effect of immersive technologies on chemistry education.
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Affiliation(s)
- Luciano A. Abriata
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
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10
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Kubincová A, Riniker S, Hünenberger PH. Reaction-field electrostatics in molecular dynamics simulations: development of a conservative scheme compatible with an atomic cutoff. Phys Chem Chem Phys 2020; 22:26419-26437. [DOI: 10.1039/d0cp03835k] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Shifting and switching schemes are developed to enable strict energy conservation in molecular dynamics simulations relying on reaction-field electrostatic (as well as Lennard-Jones) interactions with an atom-based cutoff truncation.
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Affiliation(s)
| | - Sereina Riniker
- Laboratory of Physical Chemistry
- ETH Zurich
- 8093 Zurich
- Switzerland
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11
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Hildebrand PW, Rose AS, Tiemann JK. Bringing Molecular Dynamics Simulation Data into View. Trends Biochem Sci 2019; 44:902-913. [DOI: 10.1016/j.tibs.2019.06.004] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 06/05/2019] [Accepted: 06/12/2019] [Indexed: 12/14/2022]
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12
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Martinez X, Krone M, Alharbi N, Rose AS, Laramee RS, O'Donoghue S, Baaden M, Chavent M. Molecular Graphics: Bridging Structural Biologists and Computer Scientists. Structure 2019; 27:1617-1623. [PMID: 31564470 DOI: 10.1016/j.str.2019.09.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 08/02/2019] [Accepted: 09/10/2019] [Indexed: 01/20/2023]
Abstract
Visualization of molecular structures is one of the most common tasks carried out by structural biologists, typically using software, such as Chimera, COOT, PyMOL, or VMD. In this Perspective article, we outline how past developments in computer graphics and data visualization have expanded the understanding of biomolecular function, and we summarize recent advances that promise to further transform structural biology. We also highlight how progress in molecular graphics has been impeded by communication barriers between two communities: the computer scientists driving these advances, and the structural and computational biologists who stand to benefit. By pointing to canonical papers and explaining technical progress underlying new graphical developments in simple terms, we aim to improve communication between these communities; this, in turn, would help shape future developments in molecular graphics.
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Affiliation(s)
- Xavier Martinez
- Laboratoire de Biochimie Théorique, CNRS, UPR9080, Institut de Biologie Physico-Chimique, Paris, France
| | - Michael Krone
- Big Data Visual Analytics in Life Sciences, University of Tübingen, Tübingen, Germany
| | - Naif Alharbi
- Department of Computer Science, Swansea University, Swansea, Wales, United Kingdom
| | - Alexander S Rose
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, USA
| | - Robert S Laramee
- Department of Computer Science, Swansea University, Swansea, Wales, United Kingdom
| | - Sean O'Donoghue
- Garvan Institute of Medical Research, Sydney, Australia; University of New South Wales (UNSW), Sydney, Australia; CSIRO Data61, Sydney, Australia
| | - Marc Baaden
- Laboratoire de Biochimie Théorique, CNRS, UPR9080, Institut de Biologie Physico-Chimique, Paris, France
| | - Matthieu Chavent
- Institut de Pharmacologie et de Biologie Structurale IPBS, Université de Toulouse, CNRS, UPS, Toulouse, France.
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13
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Cicaloni V, Trezza A, Pettini F, Spiga O. Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions. Curr Top Med Chem 2019; 19:534-554. [PMID: 30836920 DOI: 10.2174/1568026619666190304153901] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 01/02/2019] [Accepted: 01/25/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention. OBJECTIVE Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases. METHODS Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures. RESULTS In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules. CONCLUSION A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.
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Affiliation(s)
- Vittoria Cicaloni
- Department of Biotechnology, Chemistry and Pharmacy (Dept. of Excellence 2018-2022), University of Siena, via A. Moro 2, 53100 Siena, Italy.,Toscana Life Sciences Foundation, via Fiorentina 1, 53100 Siena, Italy
| | - Alfonso Trezza
- Department of Biotechnology, Chemistry and Pharmacy (Dept. of Excellence 2018-2022), University of Siena, via A. Moro 2, 53100 Siena, Italy
| | - Francesco Pettini
- Department of Biotechnology, Chemistry and Pharmacy (Dept. of Excellence 2018-2022), University of Siena, via A. Moro 2, 53100 Siena, Italy
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy (Dept. of Excellence 2018-2022), University of Siena, via A. Moro 2, 53100 Siena, Italy
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14
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Puzzarini C, Bloino J, Tasinato N, Barone V. Accuracy and Interpretability: The Devil and the Holy Grail. New Routes across Old Boundaries in Computational Spectroscopy. Chem Rev 2019; 119:8131-8191. [DOI: 10.1021/acs.chemrev.9b00007] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Cristina Puzzarini
- Dipartimento di Chimica “Giacomo Ciamician”, Università di Bologna, Via F. Selmi 2, I-40126 Bologna, Italy
| | - Julien Bloino
- Scuola Normale Superiore, Piazza dei Cavalieri 7, I-56126 Pisa, Italy
| | - Nicola Tasinato
- Scuola Normale Superiore, Piazza dei Cavalieri 7, I-56126 Pisa, Italy
| | - Vincenzo Barone
- Scuola Normale Superiore, Piazza dei Cavalieri 7, I-56126 Pisa, Italy
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15
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Visualizing Biological Membrane Organization and Dynamics. J Mol Biol 2019; 431:1889-1919. [DOI: 10.1016/j.jmb.2019.02.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 02/02/2019] [Accepted: 02/13/2019] [Indexed: 11/22/2022]
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16
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Salvadori A, Fusè M, Mancini G, Rampino S, Barone V. Diving into chemical bonding: An immersive analysis of the electron charge rearrangement through virtual reality. J Comput Chem 2018; 39:2607-2617. [DOI: 10.1002/jcc.25523] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 06/15/2018] [Accepted: 06/18/2018] [Indexed: 11/11/2022]
Affiliation(s)
- Andrea Salvadori
- SMART Laboratory, Scuola Normale Superiore, Piazza dei Cavalieri 7; 56126 Pisa Italy
| | - Marco Fusè
- SMART Laboratory, Scuola Normale Superiore, Piazza dei Cavalieri 7; 56126 Pisa Italy
| | - Giordano Mancini
- SMART Laboratory, Scuola Normale Superiore, Piazza dei Cavalieri 7; 56126 Pisa Italy
| | - Sergio Rampino
- SMART Laboratory, Scuola Normale Superiore, Piazza dei Cavalieri 7; 56126 Pisa Italy
| | - Vincenzo Barone
- SMART Laboratory, Scuola Normale Superiore, Piazza dei Cavalieri 7; 56126 Pisa Italy
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Trellet M, Férey N, Flotyński J, Baaden M, Bourdot P. Semantics for an Integrative and Immersive Pipeline Combining Visualization and Analysis of Molecular Data. J Integr Bioinform 2018; 15:/j/jib.ahead-of-print/jib-2018-0004/jib-2018-0004.xml. [PMID: 29982236 PMCID: PMC6167042 DOI: 10.1515/jib-2018-0004] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 05/11/2018] [Indexed: 12/14/2022] Open
Abstract
The advances made in recent years in the field of structural biology significantly increased the throughput and complexity of data that scientists have to deal with. Combining and analyzing such heterogeneous amounts of data became a crucial time consumer in the daily tasks of scientists. However, only few efforts have been made to offer scientists an alternative to the standard compartmentalized tools they use to explore their data and that involve a regular back and forth between them. We propose here an integrated pipeline especially designed for immersive environments, promoting direct interactions on semantically linked 2D and 3D heterogeneous data, displayed in a common working space. The creation of a semantic definition describing the content and the context of a molecular scene leads to the creation of an intelligent system where data are (1) combined through pre-existing or inferred links present in our hierarchical definition of the concepts, (2) enriched with suitable and adaptive analyses proposed to the user with respect to the current task and (3) interactively presented in a unique working environment to be explored.
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Affiliation(s)
- Mikael Trellet
- Faculty of Science, Chemistry, Computational Structural Biology group, Bijvoet Center for Biomolecular Research, Padualaan 9, 3584CH Utrecht, Netherlands
| | - Nicolas Férey
- VENISE Group, LIMSI, CNRS, Université Paris Sud, Orsay, France
| | | | - Marc Baaden
- Laboratoire de Biochimie Théorique, CNRS, UPR9080, Institut de Biologie Physico-Chimique, Univ Paris Diderot, Sorbonne Paris Cité, PSL Research University, Paris, France
| | - Patrick Bourdot
- VENISE Group, LIMSI, CNRS, Université Paris Sud, Orsay, France
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Effects of site-directed mutagenesis of L469 in helix-5 of human papillomavirus 16 L1 on pentamer formation. Chem Res Chin Univ 2017. [DOI: 10.1007/s40242-017-6357-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Salvadori A, Del Frate G, Pagliai M, Mancini G, Barone V. Immersive virtual reality in computational chemistry: Applications to the analysis of QM and MM data. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY 2016; 116:1731-1746. [PMID: 27867214 PMCID: PMC5101850 DOI: 10.1002/qua.25207] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 06/21/2016] [Accepted: 06/24/2016] [Indexed: 05/31/2023]
Abstract
The role of Virtual Reality (VR) tools in molecular sciences is analyzed in this contribution through the presentation of the Caffeine software to the quantum chemistry community. Caffeine, developed at Scuola Normale Superiore, is specifically tailored for molecular representation and data visualization with VR systems, such as VR theaters and helmets. Usefulness and advantages that can be gained by exploiting VR are here reported, considering few examples specifically selected to illustrate different level of theory and molecular representation.
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Affiliation(s)
- Andrea Salvadori
- Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa I-56126 Italy
| | | | - Marco Pagliai
- Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa I-56126 Italy
| | - Giordano Mancini
- Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa I-56126 Italy
| | - Vincenzo Barone
- Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa I-56126 Italy
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Craveur P, Joseph AP, Esque J, Narwani TJ, Noël F, Shinada N, Goguet M, Leonard S, Poulain P, Bertrand O, Faure G, Rebehmed J, Ghozlane A, Swapna LS, Bhaskara RM, Barnoud J, Téletchéa S, Jallu V, Cerny J, Schneider B, Etchebest C, Srinivasan N, Gelly JC, de Brevern AG. Protein flexibility in the light of structural alphabets. Front Mol Biosci 2015; 2:20. [PMID: 26075209 PMCID: PMC4445325 DOI: 10.3389/fmolb.2015.00020] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Accepted: 04/30/2015] [Indexed: 01/01/2023] Open
Abstract
Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e., the classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g., B-factor) and computational methodology (e.g., Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g., to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases.
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Affiliation(s)
- Pierrick Craveur
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Agnel P Joseph
- Rutherford Appleton Laboratory, Science and Technology Facilities Council Didcot, UK
| | - Jeremy Esque
- Institut National de la Santé et de la Recherche Médicale U964,7 UMR Centre National de la Recherche Scientifique 7104, IGBMC, Université de Strasbourg Illkirch, France
| | - Tarun J Narwani
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Floriane Noël
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Nicolas Shinada
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Matthieu Goguet
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Sylvain Leonard
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Pierre Poulain
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Ets Poulain Pointe-Noire, Congo
| | - Olivier Bertrand
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Guilhem Faure
- National Library of Medicine, National Center for Biotechnology Information, National Institutes of Health Bethesda, MD, USA
| | - Joseph Rebehmed
- Centre National de la Recherche Scientifique UMR7590, Sorbonne Universités, Université Pierre et Marie Curie - MNHN - IRD - IUC Paris, France
| | | | - Lakshmipuram S Swapna
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore Bangalore, India ; Hospital for Sick Children, and Departments of Biochemistry and Molecular Genetics, University of Toronto Toronto, ON, Canada
| | - Ramachandra M Bhaskara
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore Bangalore, India ; Department of Theoretical Biophysics, Max Planck Institute of Biophysics Frankfurt, Germany
| | - Jonathan Barnoud
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Laboratoire de Physique, École Normale Supérieure de Lyon, Université de Lyon, Centre National de la Recherche Scientifique UMR 5672 Lyon, France
| | - Stéphane Téletchéa
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Faculté des Sciences et Techniques, Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines, Centre National de la Recherche Scientifique UMR 6286, Université Nantes Nantes, France
| | - Vincent Jallu
- Platelet Unit, Institut National de la Transfusion Sanguine Paris, France
| | - Jiri Cerny
- Institute of Biotechnology, The Czech Academy of Sciences Prague, Czech Republic
| | - Bohdan Schneider
- Institute of Biotechnology, The Czech Academy of Sciences Prague, Czech Republic
| | - Catherine Etchebest
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | | | - Jean-Christophe Gelly
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Alexandre G de Brevern
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
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