1
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Honorato RV, Trellet ME, Jiménez-García B, Schaarschmidt JJ, Giulini M, Reys V, Koukos PI, Rodrigues JPGLM, Karaca E, van Zundert GCP, Roel-Touris J, van Noort CW, Jandová Z, Melquiond ASJ, Bonvin AMJJ. The HADDOCK2.4 web server for integrative modeling of biomolecular complexes. Nat Protoc 2024:10.1038/s41596-024-01011-0. [PMID: 38886530 DOI: 10.1038/s41596-024-01011-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/11/2024] [Indexed: 06/20/2024]
Abstract
Interactions between macromolecules, such as proteins and nucleic acids, are essential for cellular functions. Experimental methods can fail to provide all the information required to fully model biomolecular complexes at atomic resolution, particularly for large and heterogeneous assemblies. Integrative computational approaches have, therefore, gained popularity, complementing traditional experimental methods in structural biology. Here, we introduce HADDOCK2.4, an integrative modeling platform, and its updated web interface ( https://wenmr.science.uu.nl/haddock2.4 ). The platform seamlessly integrates diverse experimental and theoretical data to generate high-quality models of macromolecular complexes. The user-friendly web server offers automated parameter settings, access to distributed computing resources, and pre- and post-processing steps that enhance the user experience. To present the web server's various interfaces and features, we demonstrate two different applications: (i) we predict the structure of an antibody-antigen complex by using NMR data for the antigen and knowledge of the hypervariable loops for the antibody, and (ii) we perform coarse-grained modeling of PRC1 with a nucleosome particle guided by mutagenesis and functional data. The described protocols require some basic familiarity with molecular modeling and the Linux command shell. This new version of our widely used HADDOCK web server allows structural biologists and non-experts to explore intricate macromolecular assemblies encompassing various molecule types.
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Affiliation(s)
- Rodrigo V Honorato
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Mikael E Trellet
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Fluigent, Le Kremlin-Bicêtre, France
| | - Brian Jiménez-García
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Zymvol Biomodeling SL, Barcelona, Spain
| | - Jörg J Schaarschmidt
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Karlsruhe Institute of Technology (KIT), Institute of Nanotechnology, Eggenstein-Leopoldshafen, Germany
| | - Marco Giulini
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Victor Reys
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - João P G L M Rodrigues
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Schrödinger Inc., New York, NY, USA
| | - Ezgi Karaca
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Izmir Biomedicine and Genome Center, Izimir, Turkey
| | - Gydo C P van Zundert
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Schrödinger Inc., New York, NY, USA
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Protein Design and Modeling Lab, Department of Structural Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Barcelona, Spain
| | - Charlotte W van Noort
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Zuzana Jandová
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Boehringer Ingelheim International GmbH, Vienna, Austria
| | - Adrien S J Melquiond
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Utrecht Medical Center, Utrecht, the Netherlands
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
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2
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Di Ianni A, Di Ianni A, Cowan K, Barbero LM, Sirtori FR. Leveraging Cross-Linking Mass Spectrometry for Modeling Antibody-Antigen Complexes. J Proteome Res 2024; 23:1049-1061. [PMID: 38372774 DOI: 10.1021/acs.jproteome.3c00816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Elucidating antibody-antigen complexes at the atomic level is of utmost interest for understanding immune responses and designing better therapies. Cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for mapping protein-protein interactions, suggesting valuable structural insights. However, the use of XL-MS studies to enable epitope/paratope mapping of antibody-antigen complexes is still limited up to now. XL-MS data can be used to drive integrative modeling of antibody-antigen complexes, where cross-links information serves as distance restraints for the precise determination of binding interfaces. In this approach, XL-MS data are employed to identify connections between binding interfaces of the antibody and the antigen, thus informing molecular modeling. Current literature provides minimal input about the impact of XL-MS data on the integrative modeling of antibody-antigen complexes. Here, we applied XL-MS to retrieve information about binding interfaces of three antibody-antigen complexes. We leveraged XL-MS data to perform integrative modeling using HADDOCK (active-passive residues and distance restraints strategies) and AlphaLink2. We then compared these three approaches with initial predictions of investigated antibody-antigen complexes by AlphaFold Multimer. This work emphasizes the importance of cross-linking data in resolving conformational dynamics of antibody-antigen complexes, ultimately enhancing the design of better protein therapeutics and vaccines.
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Affiliation(s)
- Andrea Di Ianni
- NBE-DMPK Innovative BioAnalytics, Merck Serono RBM S.p.A., an Affiliate of Merck KGaA, Darmstadt, Germany, Via Ribes 1, Colleretto Giacosa (TO) 10010, Italy
- University of Turin, Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin 10126, Italy
| | - Alessio Di Ianni
- Martin Luther University Halle-Wittenberg, Department of Pharmaceutical Chemistry and Bioanalytics, Center for Structural Mass Spectrometry, Institute of Pharmacy, Kurt-Mothes-Str. 3, Halle/Saale D-06120, Germany
| | - Kyra Cowan
- New Biological Entities, Drug Metabolism and Pharmacokinetics (NBE-DMPK), Research and Development, Merck KGaA, Frankfurterstrasse 250, Darmstadt 64293, Germany
| | - Luca M Barbero
- NBE-DMPK Innovative BioAnalytics, Merck Serono RBM S.p.A., an Affiliate of Merck KGaA, Darmstadt, Germany, Via Ribes 1, Colleretto Giacosa (TO) 10010, Italy
| | - Federico Riccardi Sirtori
- NBE-DMPK Innovative BioAnalytics, Merck Serono RBM S.p.A., an Affiliate of Merck KGaA, Darmstadt, Germany, Via Ribes 1, Colleretto Giacosa (TO) 10010, Italy
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3
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McCafferty CL, Klumpe S, Amaro RE, Kukulski W, Collinson L, Engel BD. Integrating cellular electron microscopy with multimodal data to explore biology across space and time. Cell 2024; 187:563-584. [PMID: 38306982 DOI: 10.1016/j.cell.2024.01.005] [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: 12/04/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.
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Affiliation(s)
| | - Sven Klumpe
- Research Group CryoEM Technology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.
| | - Rommie E Amaro
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Wanda Kukulski
- Institute of Biochemistry and Molecular Medicine, University of Bern, Bühlstrasse 28, 3012 Bern, Switzerland.
| | - Lucy Collinson
- Electron Microscopy Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.
| | - Benjamin D Engel
- Biozentrum, University of Basel, Spitalstrasse 41, 4056 Basel, Switzerland.
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4
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Guo D, De Sciscio ML, Chi-Fung Ng J, Fraternali F. Modelling the assembly and flexibility of antibody structures. Curr Opin Struct Biol 2024; 84:102757. [PMID: 38118364 DOI: 10.1016/j.sbi.2023.102757] [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: 10/13/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/22/2023]
Abstract
Antibodies are large protein assemblies capable of both specifically recognising antigens and engaging with other proteins and receptors to coordinate immune action. Traditionally, structural studies have been dedicated to antibody variable regions, but efforts to determine and model full-length antibody structures are emerging. Here we review the current knowledge on modelling the structures of antibody assemblies, focusing on their conformational flexibility and the challenge this poses to obtaining and evaluating structural models. Integrative modelling approaches, combining experiments (cryo-electron microscopy, mass spectrometry, etc.) and computational methods (molecular dynamics simulations, deep-learning based approaches, etc.), hold the promise to map the complex conformational landscape of full-length antibody structures.
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Affiliation(s)
- Dongjun Guo
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, WC1E 6BT, United Kingdom; Randall Centre for Cell & Molecular Biophysics, King's College London, New Hunt's House, Guy's Campus, London, SE1 1UL, United Kingdom
| | - Maria Laura De Sciscio
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, WC1E 6BT, United Kingdom; Department of Chemistry, Sapienza University of Rome, P.le A. Moro 5, Rome, 00185, Italy
| | - Joseph Chi-Fung Ng
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, WC1E 6BT, United Kingdom
| | - Franca Fraternali
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, WC1E 6BT, United Kingdom.
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5
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Xu X, Bonvin AMJJ. DeepRank-GNN-esm: a graph neural network for scoring protein-protein models using protein language model. BIOINFORMATICS ADVANCES 2024; 4:vbad191. [PMID: 38213822 PMCID: PMC10782804 DOI: 10.1093/bioadv/vbad191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/19/2023] [Indexed: 01/13/2024]
Abstract
Motivation Protein-Protein interactions (PPIs) play critical roles in numerous cellular processes. By modelling the 3D structures of the correspond protein complexes valuable insights can be obtained, providing, e.g. starting points for drug and protein design. One challenge in the modelling process is however the identification of near-native models from the large pool of generated models. To this end we have previously developed DeepRank-GNN, a graph neural network that integrates structural and sequence information to enable effective pattern learning at PPI interfaces. Its main features are related to the Position Specific Scoring Matrices (PSSMs), which are computationally expensive to generate, significantly limits the algorithm's usability. Results We introduce here DeepRank-GNN-esm that includes as additional features protein language model embeddings from the ESM-2 model. We show that the ESM-2 embeddings can actually replace the PSSM features at no cost in-, or even better performance on two PPI-related tasks: scoring docking poses and detecting crystal artifacts. This new DeepRank version bypasses thus the need of generating PSSM, greatly improving the usability of the software and opening new application opportunities for systems for which PSSM profiles cannot be obtained or are irrelevant (e.g. antibody-antigen complexes). Availability and implementation DeepRank-GNN-esm is freely available from https://github.com/DeepRank/DeepRank-GNN-esm.
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Affiliation(s)
- Xiaotong Xu
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht 3584 CS, The Netherlands
| | - Alexandre M J J Bonvin
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht 3584 CS, The Netherlands
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6
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Kotev M, Diaz Gonzalez C. Molecular Dynamics and Other HPC Simulations for Drug Discovery. Methods Mol Biol 2024; 2716:265-291. [PMID: 37702944 DOI: 10.1007/978-1-0716-3449-3_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.
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Affiliation(s)
- Martin Kotev
- Evotec SE, Integrated Drug Discovery, Molecular Architects, Campus Curie, Toulouse, France
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7
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Kern C, Radon C, Wende W, Leitner A, Sträßer K. Cross-linking mass spectrometric analysis of the endogenous TREX complex from Saccharomyces cerevisiae. RNA (NEW YORK, N.Y.) 2023; 29:1870-1880. [PMID: 37699651 PMCID: PMC10653388 DOI: 10.1261/rna.079758.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/22/2023] [Indexed: 09/14/2023]
Abstract
The conserved TREX complex has multiple functions in gene expression such as transcription elongation, 3' end processing, mRNP assembly and nuclear mRNA export as well as the maintenance of genomic stability. In Saccharomyces cerevisiae, TREX is composed of the pentameric THO complex, the DEAD-box RNA helicase Sub2, the nuclear mRNA export adaptor Yra1, and the SR-like proteins Gbp2 and Hrb1. Here, we present the structural analysis of the endogenous TREX complex of S. cerevisiae purified from its native environment. To this end, we used cross-linking mass spectrometry to gain structural information on regions of the complex that are not accessible to classical structural biology techniques. We also used negative-stain electron microscopy to investigate the organization of the cross-linked complex used for XL-MS by comparing our endogenous TREX complex with recently published structural models of recombinant THO-Sub2 complexes. According to our analysis, the endogenous yeast TREX complex preferentially assembles into a dimer.
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Affiliation(s)
- Carina Kern
- Institute of Biochemistry, FB08, Justus Liebig University, 35392 Giessen, Germany
| | - Christin Radon
- Institute of Biochemistry and Biology, Department of Biochemistry, University of Potsdam, 14476 Potsdam-Golm, Germany
| | - Wolfgang Wende
- Institute of Biochemistry, FB08, Justus Liebig University, 35392 Giessen, Germany
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Katja Sträßer
- Institute of Biochemistry, FB08, Justus Liebig University, 35392 Giessen, Germany
- Cardio-Pulmonary Institute (CPI), EXC 2026, 35392 Giessen, Germany
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8
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Laurent H, Hughes MDG, Walko M, Brockwell DJ, Mahmoudi N, Youngs TGA, Headen TF, Dougan L. Visualization of Self-Assembly and Hydration of a β-Hairpin through Integrated Small and Wide-Angle Neutron Scattering. Biomacromolecules 2023; 24:4869-4879. [PMID: 37874935 PMCID: PMC10646990 DOI: 10.1021/acs.biomac.3c00583] [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: 06/14/2023] [Revised: 10/03/2023] [Indexed: 10/26/2023]
Abstract
Fundamental understanding of the structure and assembly of nanoscale building blocks is crucial for the development of novel biomaterials with defined architectures and function. However, accessing self-consistent structural information across multiple length scales is challenging. This limits opportunities to exploit atomic scale interactions to achieve emergent macroscale properties. In this work we present an integrative small- and wide-angle neutron scattering approach coupled with computational modeling to reveal the multiscale structure of hierarchically self-assembled β hairpins in aqueous solution across 4 orders of magnitude in length scale from 0.1 Å to 300 nm. Our results demonstrate the power of this self-consistent cross-length scale approach and allows us to model both the large-scale self-assembly and small-scale hairpin hydration of the model β hairpin CLN025. Using this combination of techniques, we map the hydrophobic/hydrophilic character of this model self-assembled biomolecular surface with atomic resolution. These results have important implications for the multiscale investigation of aqueous peptides and proteins, for the prediction of ligand binding and molecular associations for drug design, and for understanding the self-assembly of peptides and proteins for functional biomaterials.
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Affiliation(s)
- Harrison Laurent
- School
of Physics and Astronomy, University of
Leeds, Leeds, United Kingdom, LS2
9JT
| | - Matt D. G. Hughes
- School
of Physics and Astronomy, University of
Leeds, Leeds, United Kingdom, LS2
9JT
- Astbury
Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom LS2
9JT
| | - Martin Walko
- School
of Chemistry, University of Leeds, Leeds, United
Kingdom, LS2 9JT
| | - David J. Brockwell
- Astbury
Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom LS2
9JT
| | - Najet Mahmoudi
- ISIS
Neutron and Muon Source, Rutherford Appleton
Laboratory, Harwell Oxford, Didcot, United Kingdom, OX11 0QX
| | - Tristan G. A. Youngs
- ISIS
Neutron and Muon Source, Rutherford Appleton
Laboratory, Harwell Oxford, Didcot, United Kingdom, OX11 0QX
| | - Thomas F. Headen
- ISIS
Neutron and Muon Source, Rutherford Appleton
Laboratory, Harwell Oxford, Didcot, United Kingdom, OX11 0QX
| | - Lorna Dougan
- School
of Physics and Astronomy, University of
Leeds, Leeds, United Kingdom, LS2
9JT
- Astbury
Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom LS2
9JT
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9
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Sarnowski C, Götze M, Leitner A. RNxQuest: An Extension to the xQuest Pipeline Enabling Analysis of Protein-RNA Cross-Linking/Mass Spectrometry Data. J Proteome Res 2023; 22:3368-3382. [PMID: 37669508 PMCID: PMC10563164 DOI: 10.1021/acs.jproteome.3c00341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Indexed: 09/07/2023]
Abstract
Cross-linking and mass spectrometry (XL-MS) workflows are increasingly popular techniques for generating low-resolution structural information about interacting biomolecules. xQuest is an established software package for analysis of protein-protein XL-MS data, supporting stable isotope-labeled cross-linking reagents. Resultant paired peaks in mass spectra aid sensitivity and specificity of data analysis. The recently developed cross-linking of isotope-labeled RNA and mass spectrometry (CLIR-MS) approach extends the XL-MS concept to protein-RNA interactions, also employing isotope-labeled cross-link (XL) species to facilitate data analysis. Data from CLIR-MS experiments are broadly compatible with core xQuest functionality, but the required analysis approach for this novel data type presents several technical challenges not optimally served by the original xQuest package. Here we introduce RNxQuest, a Python package extension for xQuest, which automates the analysis approach required for CLIR-MS data, providing bespoke, state-of-the-art processing and visualization functionality for this novel data type. Using functions included with RNxQuest, we evaluate three false discovery rate control approaches for CLIR-MS data. We demonstrate the versatility of the RNxQuest-enabled data analysis pipeline by also reanalyzing published protein-RNA XL-MS data sets that lack isotope-labeled RNA. This study demonstrates that RNxQuest provides a sensitive and specific data analysis pipeline for detection of isotope-labeled XLs in protein-RNA XL-MS experiments.
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Affiliation(s)
- Chris
P. Sarnowski
- Institute
of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zurich, Switzerland
- Systems
Biology PhD Program, University of Zürich
and ETH Zürich, 8093 Zurich, Switzerland
| | - Michael Götze
- Institute
of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Alexander Leitner
- Institute
of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zurich, Switzerland
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10
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Habeck M. Bayesian methods in integrative structure modeling. Biol Chem 2023; 404:741-754. [PMID: 37505205 DOI: 10.1515/hsz-2023-0145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 07/07/2023] [Indexed: 07/29/2023]
Abstract
There is a growing interest in characterizing the structure and dynamics of large biomolecular assemblies and their interactions within the cellular environment. A diverse array of experimental techniques allows us to study biomolecular systems on a variety of length and time scales. These techniques range from imaging with light, X-rays or electrons, to spectroscopic methods, cross-linking mass spectrometry and functional genomics approaches, and are complemented by AI-assisted protein structure prediction methods. A challenge is to integrate all of these data into a model of the system and its functional dynamics. This review focuses on Bayesian approaches to integrative structure modeling. We sketch the principles of Bayesian inference, highlight recent applications to integrative modeling and conclude with a discussion of current challenges and future perspectives.
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Affiliation(s)
- Michael Habeck
- Microscopic Image Analysis Group, Jena University Hospital, D-07743 Jena, Germany
- Max Planck Institute for Multidisciplinary Sciences, d-37077 Göttingen, Germany
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11
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Shahab M, Khan SS, Zulfat M, Bin Jardan YA, Mekonnen AB, Bourhia M, Zheng G. In silico mutagenesis-based designing of oncogenic SHP2 peptide to inhibit cancer progression. Sci Rep 2023; 13:10088. [PMID: 37344519 DOI: 10.1038/s41598-023-37020-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/14/2023] [Indexed: 06/23/2023] Open
Abstract
Cancer is among the top causes of death, accounting for an estimated 9.6 million deaths in 2018, it appeared that approximately 500,000 people die from cancer in the United States alone annually. The SHP2 plays a major role in regulation of cell growth, proliferation, and differentiation, and functional upregulation of this enzyme is linked to oncogenesis and developmental disorders. SHP2 activity has been linked to several cancer types for which no drugs are currently available. In our study, we aimed to design peptide inhibitors against the SHP2 mutant. The crystal structure of the human Src SH2-PQpYEEIPI peptide mutant was downloaded from the protein databank. We generated several peptides from the native wild peptide using an in silico mutagenesis method, which showed that changes (P302W, Y304F, E306Q, and Q303A) might boost the peptide's affinity for binding to SHP2. Furthermore, the dynamical stability and binding affinities of the mutated peptide were confirmed using Molecular dynamics simulation and Molecular Mechanics with Generalized Born and Surface Area Solvation free energy calculations. The proposed substitution greatly enhanced the binding affinity at the residue level, according to a study that decomposed energy into its component residues. Our proposed peptide may prevent the spread of cancer by inhibiting SHP2, according to our detailed analyses of binding affinities.
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Affiliation(s)
- Muhammad Shahab
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Shahin Shah Khan
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Maryam Zulfat
- Department of Chemistry, Computational Medicinal Chemistry Laboratory, UCSS, Abdul Wali Khan University, Mardan, Pakistan
| | - Yousef A Bin Jardan
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | | | - Mohammed Bourhia
- Higher Institute of Nursing Professions and Technical Health, 70000, Laayoune, Morocco
| | - Guojun Zheng
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, China.
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12
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Mathy CJP, Kortemme T. Emerging maps of allosteric regulation in cellular networks. Curr Opin Struct Biol 2023; 80:102602. [PMID: 37150039 PMCID: PMC10960510 DOI: 10.1016/j.sbi.2023.102602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/24/2023] [Accepted: 04/04/2023] [Indexed: 05/09/2023]
Abstract
Allosteric regulation is classically defined as action at a distance, where a perturbation outside of a protein active site affects function. While this definition has motivated many studies of allosteric mechanisms at the level of protein structure, translating these insights to the allosteric regulation of entire cellular processes - and their crosstalk - has received less attention, despite the broad importance of allostery for cellular regulation foreseen by Jacob and Monod. Here, we revisit an evolutionary model for the widespread emergence of allosteric regulation in colocalized proteins, describe supporting evidence, and discuss emerging advances in mapping allostery in cellular networks that link precise and often allosteric perturbations at the molecular level to functional changes at the pathway and systems levels.
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Affiliation(s)
- Christopher J P Mathy
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, 94158, USA; Quantitative Biosciences Institute, University of California, San Francisco, CA, 94158, USA; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, 94158, USA.
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, 94158, USA; Quantitative Biosciences Institute, University of California, San Francisco, CA, 94158, USA; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
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13
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Chang L, Mondal A, MacCallum JL, Perez A. CryoFold 2.0: Cryo-EM Structure Determination with MELD. J Phys Chem A 2023; 127:3906-3913. [PMID: 37084537 DOI: 10.1021/acs.jpca.3c01731] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023]
Abstract
Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.0, an integrative physics-based approach that combines Bayesian inference and the ability to handle multiple data sources with the molecular dynamics flexible fitting (MDFF) approach to determine the structures of macromolecules by using cryo-EM data. CryoFold 2.0 is incorporated into the MELD (modeling employing limited data) plugin, resulting in a pipeline that is more computationally efficient and accurate than running MELD or MDFF alone. The approach requires fewer computational resources and shorter simulation times than the original CryoFold, and it minimizes manual intervention. We demonstrate the effectiveness of the approach on eight different systems, highlighting its various benefits.
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Affiliation(s)
- Liwei Chang
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Justin L MacCallum
- Department of Chemistry, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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14
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Zamel J, Chen J, Zaer S, Harris PD, Drori P, Lebendiker M, Kalisman N, Dokholyan NV, Lerner E. Structural and dynamic insights into α-synuclein dimer conformations. Structure 2023; 31:411-423.e6. [PMID: 36809765 PMCID: PMC10081966 DOI: 10.1016/j.str.2023.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/12/2023] [Accepted: 01/26/2023] [Indexed: 02/22/2023]
Abstract
Parkinson disease is associated with the aggregation of the protein α-synuclein. While α-synuclein can exist in multiple oligomeric states, the dimer has been a subject of extensive debates. Here, using an array of biophysical approaches, we demonstrate that α-synuclein in vitro exhibits primarily a monomer-dimer equilibrium in nanomolar concentrations and up to a few micromolars. We then use spatial information from hetero-isotopic cross-linking mass spectrometry experiments as restrains in discrete molecular dynamics simulations to obtain the ensemble structure of dimeric species. Out of eight structural sub-populations of dimers, we identify one that is compact, stable, abundant, and exhibits partially exposed β-sheet structures. This compact dimer is the only one where the hydroxyls of tyrosine 39 are in proximity that may promote dityrosine covalent linkage upon hydroxyl radicalization, which is implicated in α-synuclein amyloid fibrils. We propose that this α-synuclein dimer features etiological relevance to Parkinson disease.
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Affiliation(s)
- Joanna Zamel
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Jiaxing Chen
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA 17033, USA
| | - Sofia Zaer
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Paul David Harris
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Paz Drori
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Mario Lebendiker
- Wolfson Centre for Applied Structural Biology, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, The Edmond J. Safra Campus, Givat Ram, Jerusalem, 9190401, Israel
| | - Nir Kalisman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Nikolay V Dokholyan
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA 17033, USA; Department of Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, PA 17033, USA; Departments of Chemistry and Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, USA.
| | - Eitan Lerner
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel; The Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel.
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15
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Mittal S, Dutta S, Shukla D. Reconciling membrane protein simulations with experimental DEER spectroscopy data. Phys Chem Chem Phys 2023; 25:6253-6262. [PMID: 36757376 DOI: 10.1039/d2cp02890e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Spectroscopy experiments are crucial to study membrane proteins for which traditional structure determination methods still prove challenging. Double electron-electron resonance (DEER) spectroscopy experiments provide protein residue-pair distance distributions that are indicative of their conformational heterogeneity. Atomistic molecular dynamics (MD) simulations are another tool that have been proven to be vital to study the structural dynamics of membrane proteins such as to identify inward-open, occluded, and outward-open conformations of transporter membrane proteins, among other partially open or closed states of the protein. Yet, studies have reported that there is no direct consensus between the distributional data from DEER experiments and MD simulations, which has challenged validation of structures obtained from long-timescale simulations and using simulations to design experiments. Current coping strategies for comparisons rely on heuristics, such as mapping the nearest matching peaks between two ensembles or biased simulations. Here we examine the differences in residue-pair distance distributions arising due to the choice of membranes around the protein and covalent modification of a pair of residues to nitroxide spin labels in DEER experiments. Through comparing MD simulations of two proteins, PepTSo and LeuT-both of which have been characterized using DEER experiments previously-we show that the proteins' dynamics are similar despite the choice of the detergent micelle as a membrane mimetic in DEER experiments. On the other hand, covalently modified residues show slight local differences in their dynamics and a huge divergence when the oxygen atom pair distances between spin labeled residues are measured rather than protein backbone distances. Given the computational expense associated with pairwise MTSSL labeled MD simulations, we examine the use of biased simulations to explore the conformational dynamics of the spin labels only to reveal that such simulations alter the underlying protein dynamics. Our study identifies the main cause for the mismatch between DEER experiments and MD simulations and will accelerate the development of potential mitigation strategies to improve the match.
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Affiliation(s)
- Shriyaa Mittal
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Soumajit Dutta
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Diwakar Shukla
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA. .,Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,NIH Center for Macromolecular Modeling and Bioinformatics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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16
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Jung Y, Geng C, Bonvin AMJJ, Xue LC, Honavar VG. MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations. Biomolecules 2023; 13:121. [PMID: 36671507 PMCID: PMC9855734 DOI: 10.3390/biom13010121] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/22/2022] [Accepted: 12/26/2022] [Indexed: 01/11/2023] Open
Abstract
Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining the 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking-the so-called scoring problem-still has considerable room for improvement. We present MetaScore, a new machine-learning-based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using their protein-protein interfacial features. The features include physicochemical properties, energy terms, interaction-propensity-based features, geometric properties, interface topology features, evolutionary conservation, and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of the nine traditional SFs included in this work in terms of success rate and hit rate evaluated over conformations ranked among the top 10; (ii) an ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by using machine learning to judiciously leverage protein-protein interfacial features and by using ensemble methods to combine multiple scoring functions.
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Affiliation(s)
- Yong Jung
- Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, PA 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
| | - Cunliang Geng
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Li C. Xue
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
- Center for Molecular and Biomolecular Informatics, Radboudumc, Greet Grooteplein 26-28, 6525 GA Nijmegen, The Netherlands
| | - Vasant G. Honavar
- Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, PA 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
- Clinical and Translational Sciences Institute, Pennsylvania State University, University Park, PA 16802, USA
- College of Information Sciences & Technology, Pennsylvania State University, University Park, PA 16802, USA
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA
- Center for Big Data Analytics and Discovery Informatics, Pennsylvania State University, University Park, PA 16823, USA
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17
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Scietti L, Forneris F. Modeling of Protein Complexes. Methods Mol Biol 2023; 2627:349-371. [PMID: 36959458 DOI: 10.1007/978-1-0716-2974-1_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
The recent advances in structural biology, combined with continuously increasing computational capabilities and development of advanced softwares, have drastically simplified the workflow for protein homology modeling. Modeling of individual proteins is nowadays quick and straightforward for a large variety of protein targets, thanks to guided pipelines relying on advanced computational tools and user-friendly interfaces, which have extended and promoted the use of modeling also to scientists not focusing on molecular structures of proteins. Nevertheless, construction of models of multi-protein complexes remains quite challenging for the non-experts, often due to the usage of specific procedures depending on the system under investigation and the need for experimental validation approaches to strengthen the generated output.In this chapter, we provide a brief overview of the approaches enabling generation of multi-protein complex models starting from homology models of individual protein components. Using real-life examples, we include two examples to guide the reader in the generation of homomeric and heteromeric protein models.
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Affiliation(s)
- Luigi Scietti
- Department of Biology and Biotechnology, The Armenise-Harvard Laboratory of Structural Biology, University of Pavia, Pavia, Italy.
| | - Federico Forneris
- Department of Biology and Biotechnology, The Armenise-Harvard Laboratory of Structural Biology, University of Pavia, Pavia, Italy.
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18
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Bryant P, Pozzati G, Zhu W, Shenoy A, Kundrotas P, Elofsson A. Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search. Nat Commun 2022; 13:6028. [PMID: 36224222 PMCID: PMC9556563 DOI: 10.1038/s41467-022-33729-4] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/29/2022] [Indexed: 11/30/2022] Open
Abstract
AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb. The accuracy of AlphaFold decreases with the number of protein chains and the available GPU memory limits the size of protein complexes that can be predicted. Here, the authors show that complexes with 10–30 chains can be assembled from predicted subcomponents using Monte Carlo tree search.
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Affiliation(s)
- Patrick Bryant
- Science for Life Laboratory, 172 21, Solna, Sweden. .,Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden.
| | - Gabriele Pozzati
- Science for Life Laboratory, 172 21, Solna, Sweden.,Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden
| | - Wensi Zhu
- Science for Life Laboratory, 172 21, Solna, Sweden.,Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden
| | - Aditi Shenoy
- Science for Life Laboratory, 172 21, Solna, Sweden.,Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden
| | - Petras Kundrotas
- Science for Life Laboratory, 172 21, Solna, Sweden.,Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA
| | - Arne Elofsson
- Science for Life Laboratory, 172 21, Solna, Sweden.,Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden
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19
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Sarnowski CP, Bikaki M, Leitner A. Cross-linking and mass spectrometry as a tool for studying the structural biology of ribonucleoproteins. Structure 2022; 30:441-461. [PMID: 35366400 DOI: 10.1016/j.str.2022.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 02/03/2022] [Accepted: 03/01/2022] [Indexed: 11/17/2022]
Abstract
Cross-linking and mass spectrometry (XL-MS) workflows represent an increasingly popular technique for low-resolution structural studies of macromolecular complexes. Cross-linking reactions take place in the solution state, capturing contact sites between components of a complex that represent the native, functionally relevant structure. Protein-protein XL-MS protocols are widely adopted, providing precise localization of cross-linking sites to single amino acid positions within a pair of cross-linked peptides. In contrast, protein-RNA XL-MS workflows are evolving rapidly and differ in their ability to localize interaction regions within the RNA sequence. Here, we review protein-protein and protein-RNA XL-MS workflows, and discuss their applications in studies of protein-RNA complexes. The examples highlight the complementary value of XL-MS in structural studies of protein-RNA complexes, where more established high-resolution techniques might be unable to produce conclusive data.
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Affiliation(s)
- Chris P Sarnowski
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zurich, Switzerland; Systems Biology PhD Program, University of Zürich and ETH Zürich, Zurich, Switzerland
| | - Maria Bikaki
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Alexander Leitner
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zurich, Switzerland.
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20
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Exploring cryo-electron microscopy with molecular dynamics. Biochem Soc Trans 2022; 50:569-581. [PMID: 35212361 DOI: 10.1042/bst20210485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 11/17/2022]
Abstract
Single particle analysis cryo-electron microscopy (EM) and molecular dynamics (MD) have been complimentary methods since cryo-EM was first applied to the field of structural biology. The relationship started by biasing structural models to fit low-resolution cryo-EM maps of large macromolecular complexes not amenable to crystallization. The connection between cryo-EM and MD evolved as cryo-EM maps improved in resolution, allowing advanced sampling algorithms to simultaneously refine backbone and sidechains. Moving beyond a single static snapshot, modern inferencing approaches integrate cryo-EM and MD to generate structural ensembles from cryo-EM map data or directly from the particle images themselves. We summarize the recent history of MD innovations in the area of cryo-EM modeling. The merits for the myriad of MD based cryo-EM modeling methods are discussed, as well as, the discoveries that were made possible by the integration of molecular modeling with cryo-EM. Lastly, current challenges and potential opportunities are reviewed.
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21
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Korn SM, Schlundt A. Structures and nucleic acid-binding preferences of the eukaryotic ARID domain. Biol Chem 2022; 403:731-747. [PMID: 35119801 DOI: 10.1515/hsz-2021-0404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/17/2022] [Indexed: 12/28/2022]
Abstract
The DNA-binding AT-rich interactive domain (ARID) exists in a wide range of proteins throughout eukaryotic kingdoms. ARID domain-containing proteins are involved in manifold biological processes, such as transcriptional regulation, cell cycle control and chromatin remodeling. Their individual domain composition allows for a sub-classification within higher mammals. ARID is categorized as binder of double-stranded AT-rich DNA, while recent work has suggested ARIDs as capable of binding other DNA motifs and also recognizing RNA. Despite a broad variability on the primary sequence level, ARIDs show a highly conserved fold, which consists of six α-helices and two loop regions. Interestingly, this minimal core domain is often found extended by helices at the N- and/or C-terminus with potential roles in target specificity and, subsequently function. While high-resolution structural information from various types of ARIDs has accumulated over two decades now, there is limited access to ARID-DNA complex structures. We thus find ourselves left at the beginning of understanding ARID domain target specificities and the role of accompanying domains. Here, we systematically summarize ARID domain conservation and compare the various types with a focus on their structural differences and DNA-binding preferences, including the context of multiple other motifs within ARID domain containing proteins.
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Affiliation(s)
- Sophie Marianne Korn
- Institute for Molecular Biosciences and Center for Biomolecular Magnetic Resonance (BMRZ), Goethe-University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
| | - Andreas Schlundt
- Institute for Molecular Biosciences and Center for Biomolecular Magnetic Resonance (BMRZ), Goethe-University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
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22
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Soares BS, Rocha SLG, Bastos VA, Lima DB, Carvalho PC, Gozzo FC, Demeler B, Williams TL, Arnold J, Henrickson A, Jørgensen TJD, Souza TACB, Perales J, Valente RH, Lomonte B, Gomes-Neto F, Neves-Ferreira AGC. Molecular Architecture of the Antiophidic Protein DM64 and its Binding Specificity to Myotoxin II From Bothrops asper Venom. Front Mol Biosci 2022; 8:787368. [PMID: 35155563 PMCID: PMC8830425 DOI: 10.3389/fmolb.2021.787368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/07/2021] [Indexed: 01/11/2023] Open
Abstract
DM64 is a toxin-neutralizing serum glycoprotein isolated from Didelphis aurita, an ophiophagous marsupial naturally resistant to snake envenomation. This 64 kDa antitoxin targets myotoxic phospholipases A2, which account for most local tissue damage of viperid snakebites. We investigated the noncovalent complex formed between native DM64 and myotoxin II, a myotoxic phospholipase-like protein from Bothrops asper venom. Analytical ultracentrifugation (AUC) and size exclusion chromatography indicated that DM64 is monomeric in solution and binds equimolar amounts of the toxin. Attempts to crystallize native DM64 for X-ray diffraction were unsuccessful. Obtaining recombinant protein to pursue structural studies was also challenging. Classical molecular modeling techniques were impaired by the lack of templates with more than 25% sequence identity with DM64. An integrative structural biology approach was then applied to generate a three-dimensional model of the inhibitor bound to myotoxin II. I-TASSER individually modeled the five immunoglobulin-like domains of DM64. Distance constraints generated by cross-linking mass spectrometry of the complex guided the docking of DM64 domains to the crystal structure of myotoxin II, using Rosetta. AUC, small-angle X-ray scattering (SAXS), molecular modeling, and molecular dynamics simulations indicated that the DM64-myotoxin II complex is structured, shows flexibility, and has an anisotropic shape. Inter-protein cross-links and limited hydrolysis analyses shed light on the inhibitor's regions involved with toxin interaction, revealing the critical participation of the first, third, and fifth domains of DM64. Our data showed that the fifth domain of DM64 binds to myotoxin II amino-terminal and beta-wing regions. The third domain of the inhibitor acts in a complementary way to the fifth domain. Their binding to these toxin regions presumably precludes dimerization, thus interfering with toxicity, which is related to the quaternary structure of the toxin. The first domain of DM64 interacts with the functional site of the toxin putatively associated with membrane anchorage. We propose that both mechanisms concur to inhibit myotoxin II toxicity by DM64 binding. The present topological characterization of this toxin-antitoxin complex constitutes an essential step toward the rational design of novel peptide-based antivenom therapies targeting snake venom myotoxins.
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Affiliation(s)
- Barbara S. Soares
- Laboratory of Toxinology, Oswaldo Cruz Institute, Rio de Janeiro, Brazil
| | | | - Viviane A. Bastos
- Laboratory of Toxinology, Oswaldo Cruz Institute, Rio de Janeiro, Brazil
| | - Diogo B. Lima
- Department of Chemical Biology, Leibniz Forschungsinstitut für Molekulare Pharmakologie (FMP), Berlin, Germany
| | - Paulo C. Carvalho
- Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Curitiba, Brazil
| | - Fabio C. Gozzo
- Dalton Mass Spectrometry Laboratory, University of Campinas, Campinas, Brazil
| | - Borries Demeler
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
- Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB, Canada
- Department of Chemistry and Biochemistry, University of Montana, Missoula, MT, United States
| | - Tayler L. Williams
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Janelle Arnold
- Department of Environmental Science, Princeton University, Princeton, NJ, United States
| | - Amy Henrickson
- Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB, Canada
| | - Thomas J. D. Jørgensen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Tatiana A. C. B. Souza
- Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Curitiba, Brazil
| | - Jonas Perales
- Laboratory of Toxinology, Oswaldo Cruz Institute, Rio de Janeiro, Brazil
| | - Richard H. Valente
- Laboratory of Toxinology, Oswaldo Cruz Institute, Rio de Janeiro, Brazil
| | - Bruno Lomonte
- Clodomiro Picado Institute, University of Costa Rica, San José, Costa Rica
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23
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Bustamante C, Muskus C, Ochoa R. Rational computational approaches to predict novel drug candidates against leishmaniasis. ANNUAL REPORTS IN MEDICINAL CHEMISTRY 2022. [DOI: 10.1016/bs.armc.2022.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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24
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Petrotchenko EV, Borchers CH. Protein Chemistry Combined with Mass Spectrometry for Protein Structure Determination. Chem Rev 2021; 122:7488-7499. [PMID: 34968047 DOI: 10.1021/acs.chemrev.1c00302] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The advent of soft-ionization mass spectrometry for biomolecules has opened up new possibilities for the structural analysis of proteins. Combining protein chemistry methods with modern mass spectrometry has led to the emergence of the distinct field of structural proteomics. Multiple protein chemistry approaches, such as surface modification, limited proteolysis, hydrogen-deuterium exchange, and cross-linking, provide diverse and often orthogonal structural information on the protein systems studied. Combining experimental data from these various structural proteomics techniques provides a more comprehensive examination of the protein structure and increases confidence in the ultimate findings. Here, we review various types of experimental data from structural proteomics approaches with an emphasis on the use of multiple complementary mass spectrometric approaches to provide experimental constraints for the solving of protein structures.
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Affiliation(s)
- Evgeniy V Petrotchenko
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec H3T 1E2, Canada.,Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Christoph H Borchers
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec H3T 1E2, Canada.,Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia.,Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal, Quebec H3T 1E2, Canada
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25
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Graziadei A, Rappsilber J. Leveraging crosslinking mass spectrometry in structural and cell biology. Structure 2021; 30:37-54. [PMID: 34895473 DOI: 10.1016/j.str.2021.11.007] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/11/2021] [Accepted: 11/17/2021] [Indexed: 12/18/2022]
Abstract
Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.
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Affiliation(s)
- Andrea Graziadei
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany; Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, UK.
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26
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Integrative structural modeling of macromolecular complexes using Assembline. Nat Protoc 2021; 17:152-176. [PMID: 34845384 DOI: 10.1038/s41596-021-00640-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 09/30/2021] [Indexed: 11/08/2022]
Abstract
Integrative modeling enables structure determination of macromolecular complexes by combining data from multiple experimental sources such as X-ray crystallography, electron microscopy or cross-linking mass spectrometry. It is particularly useful for complexes not amenable to high-resolution electron microscopy-complexes that are flexible, heterogeneous or imaged in cells with cryo-electron tomography. We have recently developed an integrative modeling protocol that allowed us to model multi-megadalton complexes as large as the nuclear pore complex. Here, we describe the Assembline software package, which combines multiple programs and libraries with our own algorithms in a streamlined modeling pipeline. Assembline builds ensembles of models satisfying data from atomic structures or homology models, electron microscopy maps and other experimental data, and provides tools for their analysis. Compared with other methods, Assembline enables efficient sampling of conformational space through a multistep procedure, provides new modeling restraints and includes a unique configuration system for setting up the modeling project. Our protocol achieves exhaustive sampling in less than 100-1,000 CPU-hours even for complexes in the megadalton range. For larger complexes, resources available in institutional or public computer clusters are needed and sufficient to run the protocol. We also provide step-by-step instructions for preparing the input, running the core modeling steps and assessing modeling performance at any stage.
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27
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Piersimoni L, Kastritis PL, Arlt C, Sinz A. Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons. Chem Rev 2021; 122:7500-7531. [PMID: 34797068 DOI: 10.1021/acs.chemrev.1c00786] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Mass spectrometry (MS) has become one of the key technologies of structural biology. In this review, the contributions of chemical cross-linking combined with mass spectrometry (XL-MS) for studying three-dimensional structures of proteins and for investigating protein-protein interactions are outlined. We summarize the most important cross-linking reagents, software tools, and XL-MS workflows and highlight prominent examples for characterizing proteins, their assemblies, and interaction networks in vitro and in vivo. Computational modeling plays a crucial role in deriving 3D-structural information from XL-MS data. Integrating XL-MS with other techniques of structural biology, such as cryo-electron microscopy, has been successful in addressing biological questions that to date could not be answered. XL-MS is therefore expected to play an increasingly important role in structural biology in the future.
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Affiliation(s)
- Lolita Piersimoni
- Department of Pharmaceutical Chemistry & Bioanalytics, Institute of Pharmacy, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Center for Structural Mass Spectrometry, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany
| | - Panagiotis L Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Kurt-Mothes-Strasse 3a, D-06120 Halle (Saale), Germany.,Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Biozentrum, Weinbergweg 22, D-06120 Halle (Saale), Germany
| | - Christian Arlt
- Department of Pharmaceutical Chemistry & Bioanalytics, Institute of Pharmacy, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Center for Structural Mass Spectrometry, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany
| | - Andrea Sinz
- Department of Pharmaceutical Chemistry & Bioanalytics, Institute of Pharmacy, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Center for Structural Mass Spectrometry, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany
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28
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Edwards T, Foloppe N, Harris SA, Wells G. The future of biomolecular simulation in the pharmaceutical industry: what we can learn from aerodynamics modelling and weather prediction. Part 1. understanding the physical and computational complexity of in silico drug design. Acta Crystallogr D Struct Biol 2021; 77:1348-1356. [PMID: 34726163 PMCID: PMC8561735 DOI: 10.1107/s2059798321009712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 09/17/2021] [Indexed: 02/04/2023] Open
Abstract
The predictive power of simulation has become embedded in the infrastructure of modern economies. Computer-aided design is ubiquitous throughout industry. In aeronautical engineering, built infrastructure and materials manufacturing, simulations are routinely used to compute the performance of potential designs before construction. The ability to predict the behaviour of products is a driver of innovation by reducing the cost barrier to new designs, but also because radically novel ideas can be piloted with relatively little risk. Accurate weather forecasting is essential to guide domestic and military flight paths, and therefore the underpinning simulations are critical enough to have implications for national security. However, in the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology. Over the last 30 years, molecular-modelling tools have gradually gained a degree of acceptance in the pharmaceutical industry. Drug discovery has begun to benefit from physics-based simulations. While such simulations have great potential for improved molecular design, much scepticism remains about their value. The motivations for such reservations in industry and areas where simulations show promise for efficiency gains in preclinical research are discussed. In this, the first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1). In Part 2, the status of computer simulations in pharma is contrasted with aerodynamics modelling and weather forecasting, and comments are made on the cultural changes needed for equivalent computational technologies to become integrated into life-science industries.
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Affiliation(s)
- Tom Edwards
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
| | | | - Sarah Anne Harris
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
- School of Physics and Astronomy, University of Leeds, Leeds, United Kingdom
| | - Geoff Wells
- School of Pharmacy, University College London, London, United Kingdom
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29
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Sacquin-Mora S, Prévost C. When Order Meets Disorder: Modeling and Function of the Protein Interface in Fuzzy Complexes. Biomolecules 2021; 11:1529. [PMID: 34680162 PMCID: PMC8533853 DOI: 10.3390/biom11101529] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/11/2021] [Accepted: 10/12/2021] [Indexed: 11/30/2022] Open
Abstract
The degree of proteins structural organization ranges from highly structured, compact folding to intrinsic disorder, where each degree of self-organization corresponds to specific functions: well-organized structural motifs in enzymes offer a proper environment for precisely positioned functional groups to participate in catalytic reactions; at the other end of the self-organization spectrum, intrinsically disordered proteins act as binding hubs via the formation of multiple, transient and often non-specific interactions. This review focusses on cases where structurally organized proteins or domains associate with highly disordered protein chains, leading to the formation of interfaces with varying degrees of fuzziness. We present a review of the computational methods developed to provide us with information on such fuzzy interfaces, and how they integrate experimental information. The discussion focusses on two specific cases, microtubules and homologous recombination nucleoprotein filaments, where a network of intrinsically disordered tails exerts regulatory function in recruiting partner macromolecules, proteins or DNA and tuning the atomic level association. Notably, we show how computational approaches such as molecular dynamics simulations can bring new knowledge to help bridging the gap between experimental analysis, that mostly concerns ensemble properties, and the behavior of individual disordered protein chains that contribute to regulation functions.
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Affiliation(s)
- Sophie Sacquin-Mora
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 Rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
| | - Chantal Prévost
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 Rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
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30
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Britt HM, Cragnolini T, Thalassinos K. Integration of Mass Spectrometry Data for Structural Biology. Chem Rev 2021; 122:7952-7986. [PMID: 34506113 DOI: 10.1021/acs.chemrev.1c00356] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies.
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Affiliation(s)
- Hannah M Britt
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
| | - Konstantinos Thalassinos
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
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31
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Slavin M, Zamel J, Zohar K, Eliyahu T, Braitbard M, Brielle E, Baraz L, Stolovich-Rain M, Friedman A, Wolf DG, Rouvinski A, Linial M, Schneidman-Duhovny D, Kalisman N. Targeted in situ cross-linking mass spectrometry and integrative modeling reveal the architectures of three proteins from SARS-CoV-2. Proc Natl Acad Sci U S A 2021; 118:e2103554118. [PMID: 34373319 PMCID: PMC8403911 DOI: 10.1073/pnas.2103554118] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Atomic structures of several proteins from the coronavirus family are still partial or unavailable. A possible reason for this gap is the instability of these proteins outside of the cellular context, thereby prompting the use of in-cell approaches. In situ cross-linking and mass spectrometry (in situ CLMS) can provide information on the structures of such proteins as they occur in the intact cell. Here, we applied targeted in situ CLMS to structurally probe Nsp1, Nsp2, and nucleocapsid (N) proteins from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and obtained cross-link sets with an average density of one cross-link per 20 residues. We then employed integrative modeling that computationally combined the cross-linking data with domain structures to determine full-length atomic models. For the Nsp2, the cross-links report on a complex topology with long-range interactions. Integrative modeling with structural prediction of individual domains by the AlphaFold2 system allowed us to generate a single consistent all-atom model of the full-length Nsp2. The model reveals three putative metal binding sites and suggests a role for Nsp2 in zinc regulation within the replication-transcription complex. For the N protein, we identified multiple intra- and interdomain cross-links. Our integrative model of the N dimer demonstrates that it can accommodate three single RNA strands simultaneously, both stereochemically and electrostatically. For the Nsp1, cross-links with the 40S ribosome were highly consistent with recent cryogenic electron microscopy structures. These results highlight the importance of cellular context for the structural probing of recalcitrant proteins and demonstrate the effectiveness of targeted in situ CLMS and integrative modeling.
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Affiliation(s)
- Moriya Slavin
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Joanna Zamel
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Keren Zohar
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Tsiona Eliyahu
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Merav Braitbard
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Esther Brielle
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Leah Baraz
- Hadassah Academic College Jerusalem, Jerusalem 9101001, Israel
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, The Kuvin Center for the Study of Infectious and Tropical Diseases, The Hebrew University-Hadassah Medical School, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Miri Stolovich-Rain
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, The Kuvin Center for the Study of Infectious and Tropical Diseases, The Hebrew University-Hadassah Medical School, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Ahuva Friedman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, The Kuvin Center for the Study of Infectious and Tropical Diseases, The Hebrew University-Hadassah Medical School, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Dana G Wolf
- Clinical Virology Unit, Hadassah Hebrew University Medical Center, 9190401 Jerusalem, Israel
| | - Alexander Rouvinski
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, The Kuvin Center for the Study of Infectious and Tropical Diseases, The Hebrew University-Hadassah Medical School, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
| | - Dina Schneidman-Duhovny
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Nir Kalisman
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel;
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32
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Masrati G, Landau M, Ben-Tal N, Lupas A, Kosloff M, Kosinski J. Integrative Structural Biology in the Era of Accurate Structure Prediction. J Mol Biol 2021; 433:167127. [PMID: 34224746 DOI: 10.1016/j.jmb.2021.167127] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/28/2021] [Accepted: 06/28/2021] [Indexed: 11/16/2022]
Abstract
Characterizing the three-dimensional structure of macromolecules is central to understanding their function. Traditionally, structures of proteins and their complexes have been determined using experimental techniques such as X-ray crystallography, NMR, or cryo-electron microscopy-applied individually or in an integrative manner. Meanwhile, however, computational methods for protein structure prediction have been improving their accuracy, gradually, then suddenly, with the breakthrough advance by AlphaFold2, whose models of monomeric proteins are often as accurate as experimental structures. This breakthrough foreshadows a new era of computational methods that can build accurate models for most monomeric proteins. Here, we envision how such accurate modeling methods can combine with experimental structural biology techniques, enhancing integrative structural biology. We highlight the challenges that arise when considering multiple structural conformations, protein complexes, and polymorphic assemblies. These challenges will motivate further developments, both in modeling programs and in methods to solve experimental structures, towards better and quicker investigation of structure-function relationships.
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Affiliation(s)
- Gal Masrati
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Meytal Landau
- Department of Biology, Technion-Israel Institute of Technology, Haifa 3200003, Israel; European Molecular Biology Laboratory (EMBL), Hamburg 22607, Germany
| | - Nir Ben-Tal
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Andrei Lupas
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany.
| | - Mickey Kosloff
- Department of Human Biology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mt. Carmel, 3498838 Haifa, Israel.
| | - Jan Kosinski
- European Molecular Biology Laboratory (EMBL), Hamburg 22607, Germany; Centre for Structural Systems Biology (CSSB), Hamburg 22607, Germany; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
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33
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van Noort CW, Honorato RV, Bonvin AMJJ. Information-driven modeling of biomolecular complexes. Curr Opin Struct Biol 2021; 70:70-77. [PMID: 34139639 DOI: 10.1016/j.sbi.2021.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 05/10/2021] [Indexed: 11/15/2022]
Abstract
Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.
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Affiliation(s)
- Charlotte W van Noort
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584CH, Netherlands
| | - Rodrigo V Honorato
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584CH, Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584CH, Netherlands.
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34
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Prévost C, Sacquin-Mora S. Moving pictures: Reassessing docking experiments with a dynamic view of protein interfaces. Proteins 2021; 89:1315-1323. [PMID: 34038009 DOI: 10.1002/prot.26152] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/22/2021] [Accepted: 05/19/2021] [Indexed: 11/06/2022]
Abstract
The modeling of protein assemblies at the atomic level remains a central issue in structural biology, as protein interactions play a key role in numerous cellular processes. This problem is traditionally addressed using docking tools, where the quality of the models is based on their similarity to a single reference experimental structure. However, using a static reference does not take into account the dynamic quality of the protein interface. Here, we used all-atom classical Molecular Dynamics simulations to investigate the stability of the reference interface for three complexes that previously served as targets in the CAPRI competition. For each one of these targets, we also ran MD simulations for ten models that are distributed over the High, Medium and Acceptable accuracy categories. To assess the quality of these models from a dynamic perspective, we set up new criteria which take into account the stability of the reference experimental protein interface. We show that, when the protein interfaces are allowed to evolve along time, the original ranking based on the static CAPRI criteria no longer holds as over 50% of the docking models undergo a category change (which can be either toward a better or a lower accuracy group) when reassessing their quality using dynamic information.
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Affiliation(s)
- Chantal Prévost
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, Paris, France.,Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, Paris, France
| | - Sophie Sacquin-Mora
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, Paris, France.,Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, Paris, France
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35
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Machado MR, Pantano S. Fighting viruses with computers, right now. Curr Opin Virol 2021; 48:91-99. [PMID: 33975154 DOI: 10.1016/j.coviro.2021.04.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/20/2021] [Accepted: 04/06/2021] [Indexed: 10/21/2022]
Abstract
The synergistic conjunction of various technological revolutions with the accumulated knowledge and workflows is rapidly transforming several scientific fields. Particularly, Virology can now feed from accurate physical models, polished computational tools, and massive computational power to readily integrate high-resolution structures into biological representations of unprecedented detail. That preparedness allows for the first time to get crucial information for vaccine and drug design from in-silico experiments against emerging pathogens of worldwide concern at relevant action windows. The present work reviews some of the main milestones leading to these breakthroughs in Computational Virology, providing an outlook for future developments in capacity building and accessibility to computational resources.
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Affiliation(s)
- Matías R Machado
- Biomolecular Simulations Group, Institut Pasteur de Montevideo, Mataojo 2020, Montevideo, 11400, Uruguay.
| | - Sergio Pantano
- Biomolecular Simulations Group, Institut Pasteur de Montevideo, Mataojo 2020, Montevideo, 11400, Uruguay.
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36
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Kozicka Z, Thomä NH. Haven't got a glue: Protein surface variation for the design of molecular glue degraders. Cell Chem Biol 2021; 28:1032-1047. [PMID: 33930325 DOI: 10.1016/j.chembiol.2021.04.009] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/22/2021] [Accepted: 04/08/2021] [Indexed: 12/13/2022]
Abstract
Molecular glue degraders are small, drug-like compounds that induce interactions between an E3 ubiquitin ligase and a target, which result in ubiquitination and subsequent degradation of the recruited protein. In recent years, serendipitous discoveries revealed that some preclinical and clinical compounds already work as molecular glue degraders, with many more postulated to destabilize their targets through indirect or yet unresolved mechanisms. Here we review strategies by which E3 ubiquitin ligases can be reprogrammed by monovalent degraders, with a focus on molecular glues hijacking cullin-RING ubiquitin ligases. We argue that such drugs exploit the intrinsic property of proteins to form higher-order assemblies, a phenomenon previously seen with disease-causing sequence variations. Modifications of the protein surface by a bound small molecule can change the interactome of the target protein. By inducing interactions between a ligase and a substrate, molecular glue degraders offer an exciting path for the development of novel therapeutics.
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Affiliation(s)
- Zuzanna Kozicka
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland; University of Basel, Basel, Switzerland
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37
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Quignot C, Granger P, Chacón P, Guerois R, Andreani J. Atomic-level evolutionary information improves protein-protein interface scoring. Bioinformatics 2021; 37:3175-3181. [PMID: 33901284 DOI: 10.1093/bioinformatics/btab254] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 03/20/2021] [Accepted: 04/19/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION The crucial role of protein interactions and the difficulty in characterising them experimentally strongly motivates the development of computational approaches for structural prediction. Even when protein-protein docking samples correct models, current scoring functions struggle to discriminate them from incorrect decoys. The previous incorporation of conservation and coevolution information has shown promise for improving protein-protein scoring. Here, we present a novel strategy to integrate atomic-level evolutionary information into different types of scoring functions to improve their docking discrimination. RESULTS : We applied this general strategy to our residue-level statistical potential from InterEvScore and to two atomic-level scores, SOAP-PP and Rosetta interface score (ISC). Including evolutionary information from as few as ten homologous sequences improves the top 10 success rates of individual atomic-level scores SOAP-PP and Rosetta ISC by respectively 6 and 13.5 percentage points, on a large benchmark of 752 docking cases. The best individual homology-enriched score reaches a top 10 success rate of 34.4%. A consensus approach based on the complementarity between different homology-enriched scores further increases the top 10 success rate to 40%. AVAILABILITY All data used for benchmarking and scoring results, as well as a Singularity container of the pipeline, are available at http://biodev.cea.fr/interevol/interevdata/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chloé Quignot
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Pierre Granger
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Pablo Chacón
- Department of Biological Chemical Physics, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain
| | - Raphael Guerois
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
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38
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Lerner E, Barth A, Hendrix J, Ambrose B, Birkedal V, Blanchard SC, Börner R, Sung Chung H, Cordes T, Craggs TD, Deniz AA, Diao J, Fei J, Gonzalez RL, Gopich IV, Ha T, Hanke CA, Haran G, Hatzakis NS, Hohng S, Hong SC, Hugel T, Ingargiola A, Joo C, Kapanidis AN, Kim HD, Laurence T, Lee NK, Lee TH, Lemke EA, Margeat E, Michaelis J, Michalet X, Myong S, Nettels D, Peulen TO, Ploetz E, Razvag Y, Robb NC, Schuler B, Soleimaninejad H, Tang C, Vafabakhsh R, Lamb DC, Seidel CAM, Weiss S. FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices. eLife 2021; 10:e60416. [PMID: 33779550 PMCID: PMC8007216 DOI: 10.7554/elife.60416] [Citation(s) in RCA: 125] [Impact Index Per Article: 41.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 02/09/2021] [Indexed: 12/18/2022] Open
Abstract
Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current 'state of the art' from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of 'soft recommendations' about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage 'open science' practices.
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Affiliation(s)
- Eitan Lerner
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, and The Center for Nanoscience and Nanotechnology, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of JerusalemJerusalemIsrael
| | - Anders Barth
- Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-UniversitätDüsseldorfGermany
| | - Jelle Hendrix
- Dynamic Bioimaging Lab, Advanced Optical Microscopy Centre and Biomedical Research Institute (BIOMED), Hasselt UniversityDiepenbeekBelgium
| | - Benjamin Ambrose
- Department of Chemistry, University of SheffieldSheffieldUnited Kingdom
| | - Victoria Birkedal
- Department of Chemistry and iNANO center, Aarhus UniversityAarhusDenmark
| | - Scott C Blanchard
- Department of Structural Biology, St. Jude Children's Research HospitalMemphisUnited States
| | - Richard Börner
- Laserinstitut HS Mittweida, University of Applied Science MittweidaMittweidaGermany
| | - Hoi Sung Chung
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of HealthBethesdaUnited States
| | - Thorben Cordes
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität MünchenPlanegg-MartinsriedGermany
| | - Timothy D Craggs
- Department of Chemistry, University of SheffieldSheffieldUnited Kingdom
| | - Ashok A Deniz
- Department of Integrative Structural and Computational Biology, The Scripps Research InstituteLa JollaUnited States
| | - Jiajie Diao
- Department of Cancer Biology, University of Cincinnati School of MedicineCincinnatiUnited States
| | - Jingyi Fei
- Department of Biochemistry and Molecular Biology and The Institute for Biophysical Dynamics, University of ChicagoChicagoUnited States
| | - Ruben L Gonzalez
- Department of Chemistry, Columbia UniversityNew YorkUnited States
| | - Irina V Gopich
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of HealthBethesdaUnited States
| | - Taekjip Ha
- Department of Biophysics and Biophysical Chemistry, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Howard Hughes Medical InstituteBaltimoreUnited States
| | - Christian A Hanke
- Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-UniversitätDüsseldorfGermany
| | - Gilad Haran
- Department of Chemical and Biological Physics, Weizmann Institute of ScienceRehovotIsrael
| | - Nikos S Hatzakis
- Department of Chemistry & Nanoscience Centre, University of CopenhagenCopenhagenDenmark
- Denmark Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
| | - Sungchul Hohng
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National UniversitySeoulRepublic of Korea
| | - Seok-Cheol Hong
- Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science and Department of Physics, Korea UniversitySeoulRepublic of Korea
| | - Thorsten Hugel
- Institute of Physical Chemistry and Signalling Research Centres BIOSS and CIBSS, University of FreiburgFreiburgGermany
| | - Antonino Ingargiola
- Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Chirlmin Joo
- Department of BioNanoScience, Kavli Institute of Nanoscience, Delft University of TechnologyDelftNetherlands
| | - Achillefs N Kapanidis
- Biological Physics Research Group, Clarendon Laboratory, Department of Physics, University of OxfordOxfordUnited Kingdom
| | - Harold D Kim
- School of Physics, Georgia Institute of TechnologyAtlantaUnited States
| | - Ted Laurence
- Physical and Life Sciences Directorate, Lawrence Livermore National LaboratoryLivermoreUnited States
| | - Nam Ki Lee
- School of Chemistry, Seoul National UniversitySeoulRepublic of Korea
| | - Tae-Hee Lee
- Department of Chemistry, Pennsylvania State UniversityUniversity ParkUnited States
| | - Edward A Lemke
- Departments of Biology and Chemistry, Johannes Gutenberg UniversityMainzGermany
- Institute of Molecular Biology (IMB)MainzGermany
| | - Emmanuel Margeat
- Centre de Biologie Structurale (CBS), CNRS, INSERM, Universitié de MontpellierMontpellierFrance
| | | | - Xavier Michalet
- Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Sua Myong
- Department of Biophysics, Johns Hopkins UniversityBaltimoreUnited States
| | - Daniel Nettels
- Department of Biochemistry and Department of Physics, University of ZurichZurichSwitzerland
| | - Thomas-Otavio Peulen
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Evelyn Ploetz
- Physical Chemistry, Department of Chemistry, Center for Nanoscience (CeNS), Center for Integrated Protein Science Munich (CIPSM) and Nanosystems Initiative Munich (NIM), Ludwig-Maximilians-UniversitätMünchenGermany
| | - Yair Razvag
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, and The Center for Nanoscience and Nanotechnology, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of JerusalemJerusalemIsrael
| | - Nicole C Robb
- Warwick Medical School, University of WarwickCoventryUnited Kingdom
| | - Benjamin Schuler
- Department of Biochemistry and Department of Physics, University of ZurichZurichSwitzerland
| | - Hamid Soleimaninejad
- Biological Optical Microscopy Platform (BOMP), University of MelbourneParkvilleAustralia
| | - Chun Tang
- College of Chemistry and Molecular Engineering, PKU-Tsinghua Center for Life Sciences, Beijing National Laboratory for Molecular Sciences, Peking UniversityBeijingChina
| | - Reza Vafabakhsh
- Department of Molecular Biosciences, Northwestern UniversityEvanstonUnited States
| | - Don C Lamb
- Physical Chemistry, Department of Chemistry, Center for Nanoscience (CeNS), Center for Integrated Protein Science Munich (CIPSM) and Nanosystems Initiative Munich (NIM), Ludwig-Maximilians-UniversitätMünchenGermany
| | - Claus AM Seidel
- Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-UniversitätDüsseldorfGermany
| | - Shimon Weiss
- Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los AngelesLos AngelesUnited States
- Department of Physiology, CaliforniaNanoSystems Institute, University of California, Los AngelesLos AngelesUnited States
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Prestegard JH. A perspective on the PDB's impact on the field of glycobiology. J Biol Chem 2021; 296:100556. [PMID: 33744289 PMCID: PMC8058564 DOI: 10.1016/j.jbc.2021.100556] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 12/12/2022] Open
Abstract
Structures deposited in the Protein Data Bank (PDB) facilitate our understanding of many biological processes including those that fall under the general category of glycobiology. However, structure-based studies of how glycans affect protein structure, how they are synthesized, and how they regulate other biological processes remain challenging. Despite the abundant presence of glycans on proteins and the dense layers of glycans that surround most of our cells, structures containing glycans are underrepresented in the PDB. There are sound reasons for this, including difficulties in producing proteins with well-defined glycosylation and the tendency of mobile and heterogeneous glycans to inhibit crystallization. Nevertheless, the structures we do find in the PDB, even some of the earliest deposited structures, have had an impact on our understanding of function. I highlight a few examples in this review and point to some promises for the future. Promises include new structures from methodologies, such as cryo-EM, that are less affected by the presence of glycans and experiment-aided computational methods that build on existing structures to provide insight into the many ways glycans affect biological function.
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Affiliation(s)
- James H Prestegard
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia, USA.
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Gopinath T, Weber D, Wang S, Larsen E, Veglia G. Solid-State NMR of Membrane Proteins in Lipid Bilayers: To Spin or Not To Spin? Acc Chem Res 2021; 54:1430-1439. [PMID: 33655754 DOI: 10.1021/acs.accounts.0c00670] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Membrane proteins mediate a plethora of cellular functions and represent important targets for drug development. Unlike soluble proteins, membrane proteins require native-like environments to fold correctly and be active. Therefore, modern structural biology techniques have aimed to determine the structure and dynamics of these membrane proteins at physiological temperature and in liquid crystalline lipid bilayers. With the flourishing of new NMR methodologies and improvements in sample preparations, magic angle spinning (MAS) and oriented sample solid-state NMR (OS-ssNMR) spectroscopy of membrane proteins is experiencing a new renaissance. Born as antagonistic approaches, these techniques nowadays offer complementary information on the structural topology and dynamics of membrane proteins reconstituted in lipid membranes. By spinning biosolid samples at the magic angle (θ = 54.7°), MAS NMR experiments remove the intrinsic anisotropy of the NMR interactions, increasing spectral resolution. Internuclear spin interactions (spin exchange) are reintroduced by RF pulses, providing distances and torsion angles to determine secondary, tertiary, and quaternary structures of membrane proteins. OS-ssNMR, on the other hand, directly detects anisotropic NMR parameters such as dipolar couplings (DC) and anisotropic chemical shifts (CS), providing orientational constraints to determine the architecture (i.e., topology) of membrane proteins relative to the lipid membrane. Defining the orientation of membrane proteins and their interactions with lipid membranes is of paramount importance since lipid-protein interactions can shape membrane protein conformations and ultimately define their functional states.In this Account, we report selected studies from our group integrating MAS and OS-ssNMR techniques to give a comprehensive view of the biological processes occurring at cellular membranes. We focus on the main experiments for both techniques, with an emphasis on new implementation to increase both sensitivity and spectral resolution. We also describe how the structural constraints derived from both isotropic and anisotropic NMR parameters are integrated into dynamic structural modeling using replica-averaged orientational-restrained molecular dynamics simulations (RAOR-MD). We showcase small membrane proteins that are involved in Ca2+ transport and regulate cardiac and skeletal muscle contractility: phospholamban (PLN, 6 kDa), sarcolipin (SLN, 4 kDa), and DWORF (4 kDa). We summarize our results for the structures of these polypeptides free and in complex with the sarcoplasmic reticulum (SR) Ca2+-ATPase (SERCA, 110 kDa). Additionally, we illustrate the progress toward the determination of the structural topology of a six transmembrane protein associated with succinate and acetate transport (SatP, hexamer 120 kDa). From these examples, the integrated MAS and OS-ssNMR approach, in combination with modern computational methods, emerges as a way to overcome the challenges posed by studying large membrane protein systems.
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Zhang Y, Krieger J, Mikulska-Ruminska K, Kaynak B, Sorzano COS, Carazo JM, Xing J, Bahar I. State-dependent sequential allostery exhibited by chaperonin TRiC/CCT revealed by network analysis of Cryo-EM maps. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 160:104-120. [PMID: 32866476 PMCID: PMC7914283 DOI: 10.1016/j.pbiomolbio.2020.08.006] [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] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 06/25/2020] [Accepted: 08/16/2020] [Indexed: 12/17/2022]
Abstract
The eukaryotic chaperonin TRiC/CCT plays a major role in assisting the folding of many proteins through an ATP-driven allosteric cycle. Recent structures elucidated by cryo-electron microscopy provide a broad view of the conformations visited at various stages of the chaperonin cycle, including a sequential activation of its subunits in response to nucleotide binding. But we lack a thorough mechanistic understanding of the structure-based dynamics and communication properties that underlie the TRiC/CCT machinery. In this study, we present a computational methodology based on elastic network models adapted to cryo-EM density maps to gain a deeper understanding of the structure-encoded allosteric dynamics of this hexadecameric machine. We have analysed several structures of the chaperonin resolved in different states toward mapping its conformational landscape. Our study indicates that the overall architecture intrinsically favours cooperative movements that comply with the structural variabilities observed in experiments. Furthermore, the individual subunits CCT1-CCT8 exhibit state-dependent sequential events at different states of the allosteric cycle. For example, in the ATP-bound state, subunits CCT5 and CCT4 selectively initiate the lid closure motions favoured by the overall architecture; whereas in the apo form of the heteromer, the subunit CCT7 exhibits the highest predisposition to structural change. The changes then propagate through parallel fluxes of allosteric signals to neighbours on both rings. The predicted state-dependent mechanisms of sequential activation provide new insights into TRiC/CCT intra- and inter-ring signal transduction events.
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Affiliation(s)
- Yan Zhang
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA
| | - James Krieger
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA
| | - Karolina Mikulska-Ruminska
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA
| | - Burak Kaynak
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA
| | | | - José-María Carazo
- Centro Nacional de Biotecnología (CSIC), Darwin, 3, 28049, Madrid, Spain
| | - Jianhua Xing
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA
| | - Ivet Bahar
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA.
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42
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Visualizing protein structures - tools and trends. Biochem Soc Trans 2021; 48:499-506. [PMID: 32196545 DOI: 10.1042/bst20190621] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Revised: 03/01/2020] [Accepted: 03/04/2020] [Indexed: 02/06/2023]
Abstract
Molecular visualization is fundamental in the current scientific literature, textbooks and dissemination materials. It provides an essential support for presenting results, reasoning on and formulating hypotheses related to molecular structure. Tools for visual exploration of structural data have become easily accessible on a broad variety of platforms thanks to advanced software tools that render a great service to the scientific community. These tools are often developed across disciplines bridging computer science, biology and chemistry. This mini-review was written as a short and compact overview for scientists who need to visualize protein structures and want to make an informed decision which tool they should use. Here, we first describe a few 'Swiss Army knives' geared towards protein visualization for everyday use with an existing large user base, then focus on more specialized tools for peculiar needs that are not yet as broadly known. Our selection is by no means exhaustive, but reflects a diverse snapshot of scenarios that we consider informative for the reader. We end with an account of future trends and perspectives.
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43
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Appadurai R, Nagesh J, Srivastava A. High resolution ensemble description of metamorphic and intrinsically disordered proteins using an efficient hybrid parallel tempering scheme. Nat Commun 2021; 12:958. [PMID: 33574233 PMCID: PMC7878814 DOI: 10.1038/s41467-021-21105-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 01/08/2021] [Indexed: 12/26/2022] Open
Abstract
Mapping free energy landscapes of complex multi-funneled metamorphic proteins and weakly-funneled intrinsically disordered proteins (IDPs) remains challenging. While rare-event sampling molecular dynamics simulations can be useful, they often need to either impose restraints or reweigh the generated data to match experiments. Here, we present a parallel-tempering method that takes advantage of accelerated water dynamics and allows efficient and accurate conformational sampling across a wide variety of proteins. We demonstrate the improved sampling efficiency by benchmarking against standard model systems such as alanine di-peptide, TRP-cage and β-hairpin. The method successfully scales to large metamorphic proteins such as RFA-H and to highly disordered IDPs such as Histatin-5. Across the diverse proteins, the calculated ensemble averages match well with the NMR, SAXS and other biophysical experiments without the need to reweigh. By allowing accurate sampling across different landscapes, the method opens doors for sampling free energy landscape of complex uncharted proteins.
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Affiliation(s)
- Rajeswari Appadurai
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Jayashree Nagesh
- Solid State & Structural Chemistry Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Anand Srivastava
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India.
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44
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Hevler JF, Lukassen MV, Cabrera-Orefice A, Arnold S, Pronker MF, Franc V, Heck AJR. Selective cross-linking of coinciding protein assemblies by in-gel cross-linking mass spectrometry. EMBO J 2021; 40:e106174. [PMID: 33459420 PMCID: PMC7883291 DOI: 10.15252/embj.2020106174] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 12/03/2020] [Accepted: 12/10/2020] [Indexed: 12/18/2022] Open
Abstract
Cross-linking mass spectrometry has developed into an important method to study protein structures and interactions. The in-solution cross-linking workflows involve time and sample consuming steps and do not provide sensible solutions for differentiating cross-links obtained from co-occurring protein oligomers, complexes, or conformers. Here we developed a cross-linking workflow combining blue native PAGE with in-gel cross-linking mass spectrometry (IGX-MS). This workflow circumvents steps, such as buffer exchange and cross-linker concentration optimization. Additionally, IGX-MS enables the parallel analysis of co-occurring protein complexes using only small amounts of sample. Another benefit of IGX-MS, demonstrated by experiments on GroEL and purified bovine heart mitochondria, is the substantial reduction of undesired over-length cross-links compared to in-solution cross-linking. We next used IGX-MS to investigate the complement components C5, C6, and their hetero-dimeric C5b6 complex. The obtained cross-links were used to generate a refined structural model of the complement component C6, resembling C6 in its inactivated state. This finding shows that IGX-MS can provide new insights into the initial stages of the terminal complement pathway.
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Affiliation(s)
- Johannes F Hevler
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands.,Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Marie V Lukassen
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands.,Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Alfredo Cabrera-Orefice
- Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Susanne Arnold
- Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Matti F Pronker
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands.,Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Vojtech Franc
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands.,Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands.,Netherlands Proteomics Center, Utrecht, The Netherlands
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45
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Sali A. From integrative structural biology to cell biology. J Biol Chem 2021; 296:100743. [PMID: 33957123 PMCID: PMC8203844 DOI: 10.1016/j.jbc.2021.100743] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/09/2021] [Accepted: 04/30/2021] [Indexed: 12/16/2022] Open
Abstract
Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.
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Affiliation(s)
- Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, the Department of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.
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46
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Integrative modeling of membrane-associated protein assemblies. Nat Commun 2020; 11:6210. [PMID: 33277503 PMCID: PMC7718903 DOI: 10.1038/s41467-020-20076-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/13/2020] [Indexed: 01/03/2023] Open
Abstract
Membrane proteins are among the most challenging systems to study with experimental structural biology techniques. The increased number of deposited structures of membrane proteins has opened the route to modeling their complexes by methods such as docking. Here, we present an integrative computational protocol for the modeling of membrane-associated protein assemblies. The information encoded by the membrane is represented by artificial beads, which allow targeting of the docking toward the binding-competent regions. It combines efficient, artificial intelligence-based rigid-body docking by LightDock with a flexible final refinement with HADDOCK to remove potential clashes at the interface. We demonstrate the performance of this protocol on eighteen membrane-associated complexes, whose interface lies between the membrane and either the cytosolic or periplasmic regions. In addition, we provide a comparison to another state-of-the-art docking software, ZDOCK. This protocol should shed light on the still dark fraction of the interactome consisting of membrane proteins.
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47
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Medeiros Selegato D, Bracco C, Giannelli C, Parigi G, Luchinat C, Sgheri L, Ravera E. Comparison of Different Reweighting Approaches for the Calculation of Conformational Variability of Macromolecules from Molecular Simulations. Chemphyschem 2020; 22:127-138. [DOI: 10.1002/cphc.202000714] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 09/14/2020] [Indexed: 11/07/2022]
Affiliation(s)
- Denise Medeiros Selegato
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
- Present address: Fundación MEDINA, Centro de Excelentia en Investigación de Medicamentos Innovadores and Andalucía MSD España Granada Spain
| | - Cesare Bracco
- Dipartimento di Matematica e Informatica “U. Dini” Università degli Studi di Firenze Viale Morgagni 67/a 50134 Florence Italy
| | - Carlotta Giannelli
- Dipartimento di Matematica e Informatica “U. Dini” Università degli Studi di Firenze Viale Morgagni 67/a 50134 Florence Italy
| | - Giacomo Parigi
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
| | - Luca Sgheri
- Istituto per le Applicazioni del Calcolo (CNR) sede di Firenze via Madonna del Piano 10 50019 Sesto Fiorentino Italy
| | - Enrico Ravera
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
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48
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Engen JR, Botzanowski T, Peterle D, Georgescauld F, Wales TE. Developments in Hydrogen/Deuterium Exchange Mass Spectrometry. Anal Chem 2020; 93:567-582. [DOI: 10.1021/acs.analchem.0c04281] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- John R. Engen
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Thomas Botzanowski
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Daniele Peterle
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Florian Georgescauld
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Thomas E. Wales
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
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Cárdenas R, Martínez-Seoane J, Amero C. Combining Experimental Data and Computational Methods for the Non-Computer Specialist. Molecules 2020; 25:E4783. [PMID: 33081072 PMCID: PMC7594097 DOI: 10.3390/molecules25204783] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/25/2020] [Accepted: 08/28/2020] [Indexed: 01/01/2023] Open
Abstract
Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.
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Affiliation(s)
| | | | - Carlos Amero
- Laboratorio de Bioquímica y Resonancia Magnética Nuclear, Centro de Investigaciones Químicas, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (R.C.); (J.M.-S.)
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Aderinwale T, Christoffer CW, Sarkar D, Alnabati E, Kihara D. Computational structure modeling for diverse categories of macromolecular interactions. Curr Opin Struct Biol 2020; 64:1-8. [PMID: 32599506 PMCID: PMC7665979 DOI: 10.1016/j.sbi.2020.05.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 05/06/2020] [Accepted: 05/21/2020] [Indexed: 01/23/2023]
Abstract
Computational protein-protein docking is one of the most intensively studied topics in structural bioinformatics. The field has made substantial progress through over three decades of development. The development began with methods for rigid-body docking of two proteins, which have now been extended in different directions to cover the various macromolecular interactions observed in a cell. Here, we overview the recent developments of the variations of docking methods, including multiple protein docking, peptide-protein docking, and disordered protein docking methods.
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Affiliation(s)
- Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | | | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Eman Alnabati
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
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