1
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Vallat B, Webb BM, Westbrook JD, Goddard TD, Hanke CA, Graziadei A, Peisach E, Zalevsky A, Sagendorf J, Tangmunarunkit H, Voinea S, Sekharan M, Yu J, Bonvin AAMJJ, DiMaio F, Hummer G, Meiler J, Tajkhorshid E, Ferrin TE, Lawson CL, Leitner A, Rappsilber J, Seidel CAM, Jeffries CM, Burley SK, Hoch JC, Kurisu G, Morris K, Patwardhan A, Velankar S, Schwede T, Trewhella J, Kesselman C, Berman HM, Sali A. IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods. J Mol Biol 2024; 436:168546. [PMID: 38508301 PMCID: PMC11377171 DOI: 10.1016/j.jmb.2024.168546] [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: 01/23/2024] [Revised: 03/11/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024]
Abstract
IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.
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Affiliation(s)
- Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
| | - Benjamin M Webb
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, the Quantitative Biosciences Institute (QBI), and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, University of California, San Francisco, San Francisco, CA 94157, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Thomas D Goddard
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Christian A Hanke
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Andrea Graziadei
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 10623 Berlin, Germany; Human Technopole, 20157 Milan, Italy
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Arthur Zalevsky
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, the Quantitative Biosciences Institute (QBI), and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, University of California, San Francisco, San Francisco, CA 94157, USA
| | - Jared Sagendorf
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, the Quantitative Biosciences Institute (QBI), and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, University of California, San Francisco, San Francisco, CA 94157, USA
| | - Hongsuda Tangmunarunkit
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Serban Voinea
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jian Yu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Alexander A M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany; Institute for Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, 465 21st Avenue South, Nashville, TN 37221, USA; Institute for Drug Discovery, Leipzig University Medical School, 04103 Leipzig, Germany
| | - Emad Tajkhorshid
- NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Thomas E Ferrin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Catherine L Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 10623 Berlin, Germany; Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, UK
| | - Claus A M Seidel
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Cy M Jeffries
- European Molecular Biology Laboratory (EMBL), Hamburg Unit, c/o Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jeffrey C Hoch
- Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, University of Connecticut, Farmington, CT 06030-3305, USA
| | - Genji Kurisu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Kyle Morris
- Electron Microscopy Data Bank, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Ardan Patwardhan
- Electron Microscopy Data Bank, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland; Computational Structural Biology & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia; Department of Chemistry, University of Utah, Salt Lake City, UT 84112, USA
| | - Carl Kesselman
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Helen M Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles CA 90089, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, the Quantitative Biosciences Institute (QBI), and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, University of California, San Francisco, San Francisco, CA 94157, USA
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2
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Muscat S, Martino G, Manigrasso J, Marcia M, De Vivo M. On the Power and Challenges of Atomistic Molecular Dynamics to Investigate RNA Molecules. J Chem Theory Comput 2024. [PMID: 39150960 DOI: 10.1021/acs.jctc.4c00773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2024]
Abstract
RNA molecules play a vital role in biological processes within the cell, with significant implications for science and medicine. Notably, the biological functions exerted by specific RNA molecules are often linked to the RNA conformational ensemble. However, the experimental characterization of such three-dimensional RNA structures is challenged by the structural heterogeneity of RNA and by its multiple dynamic interactions with binding partners such as small molecules, proteins, and metal ions. Consequently, our current understanding of the structure-function relationship of RNA molecules is still limited. In this context, we highlight molecular dynamics (MD) simulations as a powerful tool to complement experimental efforts on RNAs. Despite the recognized limitations of current force fields for RNA MD simulations, examining the dynamics of selected RNAs has provided valuable functional insights into their structures.
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Affiliation(s)
- Stefano Muscat
- Laboratory of Molecular Modelling and Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
| | - Gianfranco Martino
- Laboratory of Molecular Modelling and Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
| | - Jacopo Manigrasso
- Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, 431 50 Mölndal, Sweden
| | - Marco Marcia
- European Molecular Biology Laboratory Grenoble, 71 Avenue des Martyrs, 38042 Grenoble, France
- Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, 751 23 Uppsala, Sweden
- Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
| | - Marco De Vivo
- Laboratory of Molecular Modelling and Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
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3
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Liu X, Zhang Y, Wen Z, Hao Y, Banks CAS, Cesare J, Bhattacharya S, Arvindekar S, Lange JJ, Xie Y, Garcia BA, Slaughter BD, Unruh JR, Viswanath S, Florens L, Workman JL, Washburn MP. An integrated structural model of the DNA damage-responsive H3K4me3 binding WDR76:SPIN1 complex with the nucleosome. Proc Natl Acad Sci U S A 2024; 121:e2318601121. [PMID: 39116123 PMCID: PMC11331135 DOI: 10.1073/pnas.2318601121] [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: 11/06/2023] [Accepted: 06/21/2024] [Indexed: 08/10/2024] Open
Abstract
Serial capture affinity purification (SCAP) is a powerful method to isolate a specific protein complex. When combined with cross-linking mass spectrometry and computational approaches, one can build an integrated structural model of the isolated complex. Here, we applied SCAP to dissect a subpopulation of WDR76 in complex with SPIN1, a histone reader that recognizes trimethylated histone H3 lysine4 (H3K4me3). In contrast to a previous SCAP analysis of the SPIN1:SPINDOC complex, histones and the H3K4me3 mark were enriched with the WDR76:SPIN1 complex. Next, interaction network analysis of copurifying proteins and microscopy analysis revealed a potential role of the WDR76:SPIN1 complex in the DNA damage response. Since we detected 149 pairs of cross-links between WDR76, SPIN1, and histones, we then built an integrated structural model of the complex where SPIN1 recognized the H3K4me3 epigenetic mark while interacting with WDR76. Finally, we used the powerful Bayesian Integrative Modeling approach as implemented in the Integrative Modeling Platform to build a model of WDR76 and SPIN1 bound to the nucleosome.
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Affiliation(s)
- Xingyu Liu
- Stowers Institute for Medical Research, Kansas City, MO 64110
| | - Ying Zhang
- Stowers Institute for Medical Research, Kansas City, MO 64110
| | - Zhihui Wen
- Stowers Institute for Medical Research, Kansas City, MO 64110
| | - Yan Hao
- Stowers Institute for Medical Research, Kansas City, MO 64110
| | | | - Joseph Cesare
- Stowers Institute for Medical Research, Kansas City, MO 64110
- Medical Scientist Training Program, Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS 66150
| | | | - Shreyas Arvindekar
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India
| | - Jeffrey J Lange
- Stowers Institute for Medical Research, Kansas City, MO 64110
| | - Yixuan Xie
- Department of Biochemistry and Molecular Biophysics, Washington University St. Louis School of Medicine, St. Louis, MO 63110
| | - Benjamin A Garcia
- Department of Biochemistry and Molecular Biophysics, Washington University St. Louis School of Medicine, St. Louis, MO 63110
| | | | - Jay R Unruh
- Stowers Institute for Medical Research, Kansas City, MO 64110
| | - Shruthi Viswanath
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India
| | | | - Jerry L Workman
- Stowers Institute for Medical Research, Kansas City, MO 64110
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4
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Gao J, Tong M, Lee C, Gaertig J, Legal T, Bui KH. DomainFit: Identification of protein domains in cryo-EM maps at intermediate resolution using AlphaFold2-predicted models. Structure 2024; 32:1248-1259.e5. [PMID: 38754431 PMCID: PMC11316655 DOI: 10.1016/j.str.2024.04.017] [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: 11/28/2023] [Revised: 03/18/2024] [Accepted: 04/19/2024] [Indexed: 05/18/2024]
Abstract
Cryoelectron microscopy (cryo-EM) has revolutionized the structural determination of macromolecular complexes. With the paradigm shift to structure determination of highly complex endogenous macromolecular complexes ex vivo and in situ structural biology, there are an increasing number of structures of native complexes. These complexes often contain unidentified proteins, related to different cellular states or processes. Identifying proteins at resolutions lower than 4 Å remains challenging because side chains cannot be visualized reliably. Here, we present DomainFit, a program for semi-automated domain-level protein identification from cryo-EM maps, particularly at resolutions lower than 4 Å. By fitting domains from AlphaFold2-predicted models into cryo-EM maps, the program performs statistical analyses and attempts to identify the domains and protein candidates forming the density. Using DomainFit, we identified two microtubule inner proteins, one of which contains a CCDC81 domain and is exclusively localized in the proximal region of the doublet microtubule in Tetrahymena thermophila.
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Affiliation(s)
- Jerry Gao
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada
| | - Maxwell Tong
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada
| | - Chinkyu Lee
- Department of Cellular Biology, University of Georgia, Athens 30602-2607, GA, USA
| | - Jacek Gaertig
- Department of Cellular Biology, University of Georgia, Athens 30602-2607, GA, USA
| | - Thibault Legal
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada.
| | - Khanh Huy Bui
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada.
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5
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Singh D, Soni N, Hutchings J, Echeverria I, Shaikh F, Duquette M, Suslov S, Li Z, van Eeuwen T, Molloy K, Shi Y, Wang J, Guo Q, Chait BT, Fernandez-Martinez J, Rout MP, Sali A, Villa E. The molecular architecture of the nuclear basket. Cell 2024:S0092-8674(24)00780-3. [PMID: 39127037 DOI: 10.1016/j.cell.2024.07.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/24/2024] [Accepted: 07/12/2024] [Indexed: 08/12/2024]
Abstract
The nuclear pore complex (NPC) is the sole mediator of nucleocytoplasmic transport. Despite great advances in understanding its conserved core architecture, the peripheral regions can exhibit considerable variation within and between species. One such structure is the cage-like nuclear basket. Despite its crucial roles in mRNA surveillance and chromatin organization, an architectural understanding has remained elusive. Using in-cell cryo-electron tomography and subtomogram analysis, we explored the NPC's structural variations and the nuclear basket across fungi (yeast; S. cerevisiae), mammals (mouse; M. musculus), and protozoa (T. gondii). Using integrative structural modeling, we computed a model of the basket in yeast and mammals that revealed how a hub of nucleoporins (Nups) in the nuclear ring binds to basket-forming Mlp/Tpr proteins: the coiled-coil domains of Mlp/Tpr form the struts of the basket, while their unstructured termini constitute the basket distal densities, which potentially serve as a docking site for mRNA preprocessing before nucleocytoplasmic transport.
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Affiliation(s)
- Digvijay Singh
- School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Neelesh Soni
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joshua Hutchings
- School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Farhaz Shaikh
- School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Madeleine Duquette
- School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sergey Suslov
- School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhixun Li
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, School of Life Sciences, Peking University, Beijing 100871, P.R. China
| | - Trevor van Eeuwen
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA
| | - Kelly Molloy
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Yi Shi
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Junjie Wang
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Qiang Guo
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, School of Life Sciences, Peking University, Beijing 100871, P.R. China
| | - Brian T Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Javier Fernandez-Martinez
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA; Ikerbasque, Basque Foundation for Science, 48013 Bilbao, Spain; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, 48940 Leioa, Spain.
| | - Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA.
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Elizabeth Villa
- School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Howard Hughes Medical Institute, University of California, San Diego, La Jolla, CA 92093, USA.
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6
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Frasnetti E, Magni A, Castelli M, Serapian SA, Moroni E, Colombo G. Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence. Curr Opin Struct Biol 2024; 87:102835. [PMID: 38744148 DOI: 10.1016/j.sbi.2024.102835] [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: 01/23/2024] [Revised: 04/14/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods can bridge a wide range of length- and time-scales typically involved in such processes. Lately, automated learning and artificial intelligence methods have shown the potential to expand the reach of physics-based approaches, ushering in the possibility to model and even design complex protein architectures. The synergy between atomistic simulations and AI methods is an emerging frontier with a huge potential for advances in structural biology. Herein, we explore various examples and frameworks for these approaches, providing select instances and applications that illustrate their impact on fundamental biomolecular problems.
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Affiliation(s)
- Elena Frasnetti
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Andrea Magni
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Matteo Castelli
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Stefano A Serapian
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | | | - Giorgio Colombo
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy.
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7
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Shor B, Schneidman-Duhovny D. Integrative modeling meets deep learning: Recent advances in modeling protein assemblies. Curr Opin Struct Biol 2024; 87:102841. [PMID: 38795564 DOI: 10.1016/j.sbi.2024.102841] [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: 02/29/2024] [Revised: 04/24/2024] [Accepted: 04/27/2024] [Indexed: 05/28/2024]
Abstract
Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the success in predicting complex structures, large macromolecular assemblies still require specialized approaches. Here we describe recent advances in modeling macromolecular assemblies using integrative and hierarchical approaches. We highlight applications that predict protein-protein interactions and challenges in modeling complexes based on the interaction networks, including the prediction of complex stoichiometry and heterogeneity.
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Affiliation(s)
- Ben Shor
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. https://twitter.com/ben_shor
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
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8
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Chen G, Zhang Z. IDRWalker: A Random Walk Based Tool for Generating Intrinsically Disordered Regions in Large Protein Complexes. ACS OMEGA 2024; 9:32059-32065. [PMID: 39072126 PMCID: PMC11270708 DOI: 10.1021/acsomega.4c04161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/16/2024] [Accepted: 06/27/2024] [Indexed: 07/30/2024]
Abstract
Intrinsically disordered regions (IDRs), which may be functionally important, are common in proteins. However, the structures of IDRs are often missing due to their highly dynamic nature. In the study of IDRs, integrative modeling combining computational simulations and experimental data is a common approach, for which initial structures of the IDRs need to be built. However, applying this method to large protein complexes is challenging because existing structure generation tools are sometimes unsuitable for IDRs in large systems. To facilitate convenient and rapid structure generation of IDRs in large protein complexes, we developed a computational tool named IDRWalker based on self-avoiding random walks. Three protein complexes were used to illustrate the efficiency of the tool, and it was found that IDRs in more than 800 chains of the nuclear pore complex could be generated in minutes. These structures of large protein complexes with added IDRs can be further used to run computational simulations for integrative modeling.
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Affiliation(s)
- Guanglin Chen
- Department
of Physics, University of Science and Technology
of China, Hefei, Anhui 230026, PR China
| | - Zhiyong Zhang
- Department
of Physics, University of Science and Technology
of China, Hefei, Anhui 230026, PR China
- MOE
Key Laboratory for Cellular Dynamics, University of Science and Technology
of China, Hefei, Anhui 230026, PR
China
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9
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Clark T, Mohan J, Schaffer L, Obernier K, Al Manir S, Churas CP, Dailamy A, Doctor Y, Forget A, Hansen JN, Hu M, Lenkiewicz J, Levinson MA, Marquez C, Nourreddine S, Niestroy J, Pratt D, Qian G, Thaker S, Bélisle-Pipon JC, Brandt C, Chen J, Ding Y, Fodeh S, Krogan N, Lundberg E, Mali P, Payne-Foster P, Ratcliffe S, Ravitsky V, Sali A, Schulz W, Ideker T. Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.589311. [PMID: 38826258 PMCID: PMC11142054 DOI: 10.1101/2024.05.21.589311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools , status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Health's (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.
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10
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Jagielnicki M, Kucharska I, Bennett BC, Harris AL, Yeager M. Connexin Gap Junction Channels and Hemichannels: Insights from High-Resolution Structures. BIOLOGY 2024; 13:298. [PMID: 38785780 PMCID: PMC11117596 DOI: 10.3390/biology13050298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 03/29/2024] [Accepted: 04/08/2024] [Indexed: 05/25/2024]
Abstract
Connexins (Cxs) are a family of integral membrane proteins, which function as both hexameric hemichannels (HCs) and dodecameric gap junction channels (GJCs), behaving as conduits for the electrical and molecular communication between cells and between cells and the extracellular environment, respectively. Their proper functioning is crucial for many processes, including development, physiology, and response to disease and trauma. Abnormal GJC and HC communication can lead to numerous pathological states including inflammation, skin diseases, deafness, nervous system disorders, and cardiac arrhythmias. Over the last 15 years, high-resolution X-ray and electron cryomicroscopy (cryoEM) structures for seven Cx isoforms have revealed conservation in the four-helix transmembrane (TM) bundle of each subunit; an αβ fold in the disulfide-bonded extracellular loops and inter-subunit hydrogen bonding across the extracellular gap that mediates end-to-end docking to form a tight seal between hexamers in the GJC. Tissue injury is associated with cellular Ca2+ overload. Surprisingly, the binding of 12 Ca2+ ions in the Cx26 GJC results in a novel electrostatic gating mechanism that blocks cation permeation. In contrast, acidic pH during tissue injury elicits association of the N-terminal (NT) domains that sterically blocks the pore in a "ball-and-chain" fashion. The NT domains under physiologic conditions display multiple conformational states, stabilized by protein-protein and protein-lipid interactions, which may relate to gating mechanisms. The cryoEM maps also revealed putative lipid densities within the pore, intercalated among transmembrane α-helices and between protomers, the functions of which are unknown. For the future, time-resolved cryoEM of isolated Cx channels as well as cryotomography of GJCs and HCs in cells and tissues will yield a deeper insight into the mechanisms for channel regulation. The cytoplasmic loop (CL) and C-terminal (CT) domains are divergent in sequence and length, are likely involved in channel regulation, but are not visualized in the high-resolution X-ray and cryoEM maps presumably due to conformational flexibility. We expect that the integrated use of synergistic physicochemical, spectroscopic, biophysical, and computational methods will reveal conformational dynamics relevant to functional states. We anticipate that such a wealth of results under different pathologic conditions will accelerate drug discovery related to Cx channel modulation.
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Affiliation(s)
- Maciej Jagielnicki
- The Phillip and Patricia Frost Institute for Chemistry and Molecular Science, Department of Chemistry, University of Miami, 1201 Memorial Drive, Miami, FL 33146, USA; (M.J.); (I.K.)
| | - Iga Kucharska
- The Phillip and Patricia Frost Institute for Chemistry and Molecular Science, Department of Chemistry, University of Miami, 1201 Memorial Drive, Miami, FL 33146, USA; (M.J.); (I.K.)
| | - Brad C. Bennett
- Department of Biological and Environmental Sciences, Howard College of Arts and Sciences, Samford University, Birmingham, AL 35229, USA;
| | - Andrew L. Harris
- Rutgers New Jersey Medical School, Department of Pharmacology, Physiology and Neuroscience, Newark, NJ 07103, USA;
| | - Mark Yeager
- The Phillip and Patricia Frost Institute for Chemistry and Molecular Science, Department of Chemistry, University of Miami, 1201 Memorial Drive, Miami, FL 33146, USA; (M.J.); (I.K.)
- The Phillip and Patricia Frost Institute for Chemistry and Molecular Science, Department of Biochemistry and Molecular Biology, University of Miami, Miami, FL 33146, USA
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11
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Harju J, Broedersz CP. Physical models of bacterial chromosomes. Mol Microbiol 2024. [PMID: 38578226 DOI: 10.1111/mmi.15257] [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: 12/14/2023] [Revised: 03/12/2024] [Accepted: 03/18/2024] [Indexed: 04/06/2024]
Abstract
The interplay between bacterial chromosome organization and functions such as transcription and replication can be studied in increasing detail using novel experimental techniques. Interpreting the resulting quantitative data, however, can be theoretically challenging. In this minireview, we discuss how connecting experimental observations to biophysical theory and modeling can give rise to new insights on bacterial chromosome organization. We consider three flavors of models of increasing complexity: simple polymer models that explore how physical constraints, such as confinement or plectoneme branching, can affect bacterial chromosome organization; bottom-up mechanistic models that connect these constraints to their underlying causes, for instance, chromosome compaction to macromolecular crowding, or supercoiling to transcription; and finally, data-driven methods for inferring interpretable and quantitative models directly from complex experimental data. Using recent examples, we discuss how biophysical models can both deepen our understanding of how bacterial chromosomes are structured and give rise to novel predictions about bacterial chromosome organization.
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Affiliation(s)
- Janni Harju
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Chase P Broedersz
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Physics, Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Ludwig-Maximilian-University Munich, Munich, Germany
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12
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Singh D, Soni N, Hutchings J, Echeverria I, Shaikh F, Duquette M, Suslov S, Li Z, van Eeuwen T, Molloy K, Shi Y, Wang J, Guo Q, Chait BT, Fernandez-Martinez J, Rout MP, Sali A, Villa E. The Molecular Architecture of the Nuclear Basket. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587068. [PMID: 38586009 PMCID: PMC10996695 DOI: 10.1101/2024.03.27.587068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The nuclear pore complex (NPC) is the sole mediator of nucleocytoplasmic transport. Despite great advances in understanding its conserved core architecture, the peripheral regions can exhibit considerable variation within and between species. One such structure is the cage-like nuclear basket. Despite its crucial roles in mRNA surveillance and chromatin organization, an architectural understanding has remained elusive. Using in-cell cryo-electron tomography and subtomogram analysis, we explored the NPC's structural variations and the nuclear basket across fungi (yeast; S. cerevisiae), mammals (mouse; M. musculus), and protozoa (T. gondii). Using integrative structural modeling, we computed a model of the basket in yeast and mammals that revealed how a hub of Nups in the nuclear ring binds to basket-forming Mlp/Tpr proteins: the coiled-coil domains of Mlp/Tpr form the struts of the basket, while their unstructured termini constitute the basket distal densities, which potentially serve as a docking site for mRNA preprocessing before nucleocytoplasmic transport.
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Affiliation(s)
- Digvijay Singh
- School of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Neelesh Soni
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Joshua Hutchings
- School of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Farhaz Shaikh
- School of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Madeleine Duquette
- School of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Sergey Suslov
- School of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Zhixun Li
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, School of Life Sciences, Peking University, Beijing 100871, P. R. China
| | - Trevor van Eeuwen
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA
| | - Kelly Molloy
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Yi Shi
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Junjie Wang
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Qiang Guo
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, School of Life Sciences, Peking University, Beijing 100871, P. R. China
| | - Brian T Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY 10065, USA
| | - Javier Fernandez-Martinez
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA
- Ikerbasque, Basque Foundation for Science, 48013 Bilbao, Spain
- Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, 48940 Leioa, Spain
| | - Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Elizabeth Villa
- School of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92093, USA
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13
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Pasani S, Menon KS, Viswanath S. The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.13.544884. [PMID: 37398295 PMCID: PMC10312763 DOI: 10.1101/2023.06.13.544884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Desmosomes mediate cell-cell adhesion and are prevalent in tissues under mechanical stress. However, their detailed structural characterization is not available. Here, we characterized the molecular architecture of the desmosomal outer dense plaque (ODP) using Bayesian integrative structural modeling via the Integrative Modeling Platform. Starting principally from the structural interpretation of an electron cryo-tomogram, we integrated information from X-ray crystallography, an immuno-electron microscopy study, biochemical assays, in-silico predictions of transmembrane and disordered regions, homology modeling, and stereochemistry information. The integrative structure was validated by information from imaging, tomography, and biochemical studies that were not used in modeling. The ODP resembles a densely packed cylinder with a PKP layer and a PG layer; the desmosomal cadherins and PKP span these two layers. Our integrative approach allowed us to localize disordered regions, such as N-PKP and PG-C. We refined previous protein-protein interactions between desmosomal proteins and provided possible structural hypotheses for defective cell-cell adhesion in several diseases by mapping disease-related mutations on the structure. Finally, we point to features of the structure that could confer resilience to mechanical stress. Our model provides a basis for generating experimentally verifiable hypotheses on the structure and function of desmosomal proteins in normal and disease states.
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Affiliation(s)
- Satwik Pasani
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bengaluru 560065, India
| | - Kavya S Menon
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bengaluru 560065, India
| | - Shruthi Viswanath
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bengaluru 560065, India
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14
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Arvindekar S, Pathak AS, Majila K, Viswanath S. Optimizing representations for integrative structural modeling using Bayesian model selection. Bioinformatics 2024; 40:btae106. [PMID: 38391029 PMCID: PMC10924281 DOI: 10.1093/bioinformatics/btae106] [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: 12/12/2023] [Revised: 02/03/2024] [Accepted: 02/21/2024] [Indexed: 02/24/2024] Open
Abstract
MOTIVATION Integrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually made ad hoc, manually. RESULTS Here, we report NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies. AVAILABILITY AND IMPLEMENTATION NestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available at https://github.com/isblab/nestor. Data for the benchmark is at https://www.doi.org/10.5281/zenodo.10360718.
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Affiliation(s)
- Shreyas Arvindekar
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India
| | - Aditi S Pathak
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India
| | - Kartik Majila
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India
| | - Shruthi Viswanath
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India
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15
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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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16
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Shor B, Schneidman-Duhovny D. CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2. Nat Methods 2024; 21:477-487. [PMID: 38326495 PMCID: PMC10927564 DOI: 10.1038/s41592-024-02174-0] [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: 05/17/2023] [Accepted: 01/09/2024] [Indexed: 02/09/2024]
Abstract
Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score >0.7) 72% of the complexes among the top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding Protein Data Bank entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.
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Affiliation(s)
- Ben Shor
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
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17
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Beck M, Covino R, Hänelt I, Müller-McNicoll M. Understanding the cell: Future views of structural biology. Cell 2024; 187:545-562. [PMID: 38306981 DOI: 10.1016/j.cell.2023.12.017] [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: 10/04/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 02/04/2024]
Abstract
Determining the structure and mechanisms of all individual functional modules of cells at high molecular detail has often been seen as equal to understanding how cells work. Recent technical advances have led to a flush of high-resolution structures of various macromolecular machines, but despite this wealth of detailed information, our understanding of cellular function remains incomplete. Here, we discuss present-day limitations of structural biology and highlight novel technologies that may enable us to analyze molecular functions directly inside cells. We predict that the progression toward structural cell biology will involve a shift toward conceptualizing a 4D virtual reality of cells using digital twins. These will capture cellular segments in a highly enriched molecular detail, include dynamic changes, and facilitate simulations of molecular processes, leading to novel and experimentally testable predictions. Transferring biological questions into algorithms that learn from the existing wealth of data and explore novel solutions may ultimately unveil how cells work.
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Affiliation(s)
- Martin Beck
- Max Planck Institute of Biophysics, Max-von-Laue-Straße 3, 60438 Frankfurt am Main, Germany; Goethe University Frankfurt, Frankfurt, Germany.
| | - Roberto Covino
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany.
| | - Inga Hänelt
- Goethe University Frankfurt, Frankfurt, Germany.
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18
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Hoff SE, Zinke M, Izadi-Pruneyre N, Bonomi M. Bonds and bytes: The odyssey of structural biology. Curr Opin Struct Biol 2024; 84:102746. [PMID: 38101027 DOI: 10.1016/j.sbi.2023.102746] [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: 10/02/2023] [Revised: 11/20/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023]
Abstract
Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of structural biology, no single method comprehensively reveals the behavior of biological systems across various spatiotemporal scales. Instead, we have a versatile toolkit of techniques, each contributing a piece to the overall puzzle. Integrative structural biology combines different techniques to create accurate and precise multi-scale models that expand our understanding of complex biological systems. This review outlines recent advancements in computational and experimental methods in structural biology, with special focus on recent Artificial Intelligence techniques, emphasizes integrative approaches that combine different types of data for precise spatiotemporal modeling, and provides an outlook into future directions of this field.
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Affiliation(s)
- S E Hoff
- Institut Pasteur, Université Paris Cité, CNRS UMR 3528, Structural Bioinformatics Unit, Paris, France
| | - M Zinke
- Institut Pasteur, Université Paris Cité, CNRS UMR 3528, Bacterial Transmembrane Systems Unit, Paris, France. https://twitter.com/ZinkeMaximilian
| | - N Izadi-Pruneyre
- Institut Pasteur, Université Paris Cité, CNRS UMR 3528, Bacterial Transmembrane Systems Unit, Paris, France.
| | - M Bonomi
- Institut Pasteur, Université Paris Cité, CNRS UMR 3528, Structural Bioinformatics Unit, Paris, France.
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19
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Liu T, Qiu QT, Hua KJ, Ma BG. Chromosome structure modeling tools and their evaluation in bacteria. Brief Bioinform 2024; 25:bbae044. [PMID: 38385874 PMCID: PMC10883143 DOI: 10.1093/bib/bbae044] [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: 10/30/2023] [Revised: 12/31/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
The three-dimensional (3D) structure of bacterial chromosomes is crucial for understanding chromosome function. With the growing availability of high-throughput chromosome conformation capture (3C/Hi-C) data, the 3D structure reconstruction algorithms have become powerful tools to study bacterial chromosome structure and function. It is highly desired to have a recommendation on the chromosome structure reconstruction tools to facilitate the prokaryotic 3D genomics. In this work, we review existing chromosome 3D structure reconstruction algorithms and classify them based on their underlying computational models into two categories: constraint-based modeling and thermodynamics-based modeling. We briefly compare these algorithms utilizing 3C/Hi-C datasets and fluorescence microscopy data obtained from Escherichia coli and Caulobacter crescentus, as well as simulated datasets. We discuss current challenges in the 3D reconstruction algorithms for bacterial chromosomes, primarily focusing on software usability. Finally, we briefly prospect future research directions for bacterial chromosome structure reconstruction algorithms.
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Affiliation(s)
- Tong Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Qin-Tian Qiu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Kang-Jian Hua
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Bin-Guang Ma
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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20
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Wu T, Cheng AY, Zhang Y, Xu J, Wu J, Wen L, Li X, Liu B, Dou X, Wang P, Zhang L, Fei J, Li J, Ouyang Z, He C. KARR-seq reveals cellular higher-order RNA structures and RNA-RNA interactions. Nat Biotechnol 2024:10.1038/s41587-023-02109-8. [PMID: 38238480 PMCID: PMC11255127 DOI: 10.1038/s41587-023-02109-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 12/15/2023] [Indexed: 02/12/2024]
Abstract
RNA fate and function are affected by their structures and interactomes. However, how RNA and RNA-binding proteins (RBPs) assemble into higher-order structures and how RNA molecules may interact with each other to facilitate functions remain largely unknown. Here we present KARR-seq, which uses N3-kethoxal labeling and multifunctional chemical crosslinkers to covalently trap and determine RNA-RNA interactions and higher-order RNA structures inside cells, independent of local protein binding to RNA. KARR-seq depicts higher-order RNA structure and detects widespread intermolecular RNA-RNA interactions with high sensitivity and accuracy. Using KARR-seq, we show that translation represses mRNA compaction under native and stress conditions. We determined the higher-order RNA structures of respiratory syncytial virus (RSV) and vesicular stomatitis virus (VSV) and identified RNA-RNA interactions between the viruses and the host RNAs that potentially regulate viral replication.
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Affiliation(s)
- Tong Wu
- Department of Chemistry, University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, Chicago, IL, USA
| | - Anthony Youzhi Cheng
- Department of Genetics and Genome Sciences and Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Yuexiu Zhang
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Jiayu Xu
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Jinjun Wu
- Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA
| | - Li Wen
- Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA
| | - Xiao Li
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Bei Liu
- Department of Chemistry, University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, Chicago, IL, USA
| | - Xiaoyang Dou
- Department of Chemistry, University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, Chicago, IL, USA
| | - Pingluan Wang
- Department of Chemistry, University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, Chicago, IL, USA
| | - Linda Zhang
- Department of Chemistry, University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, Chicago, IL, USA
| | - Jingyi Fei
- Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA
| | - Jianrong Li
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Zhengqing Ouyang
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA.
| | - Chuan He
- Department of Chemistry, University of Chicago, Chicago, IL, USA.
- Howard Hughes Medical Institute, Chicago, IL, USA.
- Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA.
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21
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Raveh B, Eliasian R, Rashkovits S, Russel D, Hayama R, Sparks SE, Singh D, Lim R, Villa E, Rout MP, Cowburn D, Sali A. Integrative spatiotemporal map of nucleocytoplasmic transport. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.31.573409. [PMID: 38260487 PMCID: PMC10802240 DOI: 10.1101/2023.12.31.573409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The Nuclear Pore Complex (NPC) facilitates rapid and selective nucleocytoplasmic transport of molecules as large as ribosomal subunits and viral capsids. It is not clear how key emergent properties of this transport arise from the system components and their interactions. To address this question, we constructed an integrative coarse-grained Brownian dynamics model of transport through a single NPC, followed by coupling it with a kinetic model of Ran-dependent transport in an entire cell. The microscopic model parameters were fitted to reflect experimental data and theoretical information regarding the transport, without making any assumptions about its emergent properties. The resulting reductionist model is validated by reproducing several features of transport not used for its construction, such as the morphology of the central transporter, rates of passive and facilitated diffusion as a function of size and valency, in situ radial distributions of pre-ribosomal subunits, and active transport rates for viral capsids. The model suggests that the NPC functions essentially as a virtual gate whose flexible phenylalanine-glycine (FG) repeat proteins raise an entropy barrier to diffusion through the pore. Importantly, this core functionality is greatly enhanced by several key design features, including 'fuzzy' and transient interactions, multivalency, redundancy in the copy number of FG nucleoporins, exponential coupling of transport kinetics and thermodynamics in accordance with the transition state theory, and coupling to the energy-reliant RanGTP concentration gradient. These design features result in the robust and resilient rate and selectivity of transport for a wide array of cargo ranging from a few kilodaltons to megadaltons in size. By dissecting these features, our model provides a quantitative starting point for rationally modulating the transport system and its artificial mimics.
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22
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Goodsell DS, Autin L. Integrative modeling of JCVI-Syn3A nucleoids with a modular approach. Curr Res Struct Biol 2023; 7:100121. [PMID: 38221989 PMCID: PMC10784680 DOI: 10.1016/j.crstbi.2023.100121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 01/16/2024] Open
Abstract
A lattice-based method is presented for creating 3D models of entire bacterial nucleoids integrating ultrastructural information cryoelectron tomography, genomic and proteomic data, and experimental atomic structures of biomolecules and assemblies. The method is used to generate models of the minimal genome bacterium JCVI-Syn3A, producing a series of models that test hypotheses about transcription, condensation, and overall distribution of the genome. Lattice models are also used to generate atomic models of an entire JCVI-Syn3A cell.
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Affiliation(s)
- David S. Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
- Research Collaboratory for Structural Bioinformatics Protein Data and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
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23
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Arvindekar S, Pathak AS, Majila K, Viswanath S. Optimizing representations for integrative structural modeling using Bayesian model selection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571227. [PMID: 38168172 PMCID: PMC10760022 DOI: 10.1101/2023.12.12.571227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Motivation Integrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually made ad hoc, manually. Results Here, we report NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies. Availability NestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available at https://github.com/isblab/nestor.
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Affiliation(s)
- Shreyas Arvindekar
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065
| | - Aditi S. Pathak
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065
| | - Kartik Majila
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065
| | - Shruthi Viswanath
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065
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24
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Akey CW, Echeverria I, Ouch C, Nudelman I, Shi Y, Wang J, Chait BT, Sali A, Fernandez-Martinez J, Rout MP. Implications of a multiscale structure of the yeast nuclear pore complex. Mol Cell 2023; 83:3283-3302.e5. [PMID: 37738963 PMCID: PMC10630966 DOI: 10.1016/j.molcel.2023.08.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/23/2023] [Accepted: 08/24/2023] [Indexed: 09/24/2023]
Abstract
Nuclear pore complexes (NPCs) direct the nucleocytoplasmic transport of macromolecules. Here, we provide a composite multiscale structure of the yeast NPC, based on improved 3D density maps from cryogenic electron microscopy and AlphaFold2 models. Key features of the inner and outer rings were integrated into a comprehensive model. We resolved flexible connectors that tie together the core scaffold, along with equatorial transmembrane complexes and a lumenal ring that anchor this channel within the pore membrane. The organization of the nuclear double outer ring reveals an architecture that may be shared with ancestral NPCs. Additional connections between the core scaffold and the central transporter suggest that under certain conditions, a degree of local organization is present at the periphery of the transport machinery. These connectors may couple conformational changes in the scaffold to the central transporter to modulate transport. Collectively, this analysis provides insights into assembly, transport, and NPC evolution.
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Affiliation(s)
- Christopher W Akey
- Department of Pharmacology, Physiology and Biophysics, Boston University, Chobanian and Avedisian School of Medicine, 700 Albany Street, Boston, MA 02118, USA.
| | - Ignacia Echeverria
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Christna Ouch
- Department of Pharmacology, Physiology and Biophysics, Boston University, Chobanian and Avedisian School of Medicine, 700 Albany Street, Boston, MA 02118, USA; Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, 364 Plantation St., Worcester, MA 01605, USA
| | - Ilona Nudelman
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA
| | - Yi Shi
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY, USA
| | - Junjie Wang
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY, USA
| | - Brian T Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Javier Fernandez-Martinez
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA; Ikerbasque, Basque Foundation for Science, 48013 Bilbao, Spain; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, 48940 Leioa, Spain
| | - Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA.
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25
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Ghanaeian A, Majhi S, McCafferty CL, Nami B, Black CS, Yang SK, Legal T, Papoulas O, Janowska M, Valente-Paterno M, Marcotte EM, Wloga D, Bui KH. Integrated modeling of the Nexin-dynein regulatory complex reveals its regulatory mechanism. Nat Commun 2023; 14:5741. [PMID: 37714832 PMCID: PMC10504270 DOI: 10.1038/s41467-023-41480-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
Cilia are hairlike protrusions that project from the surface of eukaryotic cells and play key roles in cell signaling and motility. Ciliary motility is regulated by the conserved nexin-dynein regulatory complex (N-DRC), which links adjacent doublet microtubules and regulates and coordinates the activity of outer doublet complexes. Despite its critical role in cilia motility, the assembly and molecular basis of the regulatory mechanism are poorly understood. Here, using cryo-electron microscopy in conjunction with biochemical cross-linking and integrative modeling, we localize 12 DRC subunits in the N-DRC structure of Tetrahymena thermophila. We also find that the CCDC96/113 complex is in close contact with the DRC9/10 in the linker region. In addition, we reveal that the N-DRC is associated with a network of coiled-coil proteins that most likely mediates N-DRC regulatory activity.
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Affiliation(s)
- Avrin Ghanaeian
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
| | - Sumita Majhi
- Laboratory of Cytoskeleton and Cilia Biology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
| | - Caitlyn L McCafferty
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX, USA
| | - Babak Nami
- Genetics and Genome Biology Program, Hospital for Sick Children, Toronto, Canada
| | - Corbin S Black
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
| | - Shun Kai Yang
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
| | - Thibault Legal
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
| | - Ophelia Papoulas
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX, USA
| | - Martyna Janowska
- Laboratory of Cytoskeleton and Cilia Biology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
- Laboratory of Immunology, Mossakowski Institute of Experimental and Clinical Medicine, Polish Academy of Science, Pawinskiego 5, 02-106, Warsaw, Poland
| | - Melissa Valente-Paterno
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
| | - Edward M Marcotte
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX, USA
| | - Dorota Wloga
- Laboratory of Cytoskeleton and Cilia Biology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland.
| | - Khanh Huy Bui
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada.
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26
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Cohen S, Schneidman-Duhovny D. A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data. Proteomics 2023; 23:e2200341. [PMID: 37070547 DOI: 10.1002/pmic.202200341] [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: 11/15/2022] [Revised: 04/02/2023] [Accepted: 04/03/2023] [Indexed: 04/19/2023]
Abstract
Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The integrative structure modeling approach characterizes multi-subunit complexes by computational integration of data from fast and accessible experimental techniques. Crosslinking mass spectrometry is one such technique that provides spatial information about the proximity of crosslinked residues. One of the challenges in interpreting crosslinking datasets is designing a scoring function that, given a structure, can quantify how well it fits the data. Most approaches set an upper bound on the distance between Cα atoms of crosslinked residues and calculate a fraction of satisfied crosslinks. However, the distance spanned by the crosslinker greatly depends on the neighborhood of the crosslinked residues. Here, we design a deep learning model for predicting the optimal distance range for a crosslinked residue pair based on the structures of their neighborhoods. We find that our model can predict the distance range with the area under the receiver-operator curve of 0.86 and 0.7 for intra- and inter-protein crosslinks, respectively. Our deep scoring function can be used in a range of structure modeling applications.
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Affiliation(s)
- Shon Cohen
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
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27
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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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28
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Singh R, Sledzieski S, Bryson B, Cowen L, Berger B. Contrastive learning in protein language space predicts interactions between drugs and protein targets. Proc Natl Acad Sci U S A 2023; 120:e2220778120. [PMID: 37289807 PMCID: PMC10268324 DOI: 10.1073/pnas.2220778120] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/10/2023] [Indexed: 06/10/2023] Open
Abstract
Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance of one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pretrained protein language models ("PLex") and employing a protein-anchored contrastive coembedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with subnanomolar affinity, plus a strongly binding EPHB1 inhibitor (KD = 1.3 nM). Furthermore, ConPLex embeddings are interpretable, which enables us to visualize the drug-target embedding space and use embeddings to characterize the function of human cell-surface proteins. We anticipate that ConPLex will facilitate efficient drug discovery by making highly sensitive in silico drug screening feasible at the genome scale. ConPLex is available open source at https://ConPLex.csail.mit.edu.
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Affiliation(s)
- Rohit Singh
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Samuel Sledzieski
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Bryan Bryson
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA02155
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA02139
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29
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Ellison MA, Namjilsuren S, Shirra M, Blacksmith M, Schusteff R, Kerr E, Fang F, Xiang Y, Shi Y, Arndt K. Spt6 directly interacts with Cdc73 and is required for Paf1 complex occupancy at active genes in Saccharomyces cerevisiae. Nucleic Acids Res 2023; 51:4814-4830. [PMID: 36928138 PMCID: PMC10250246 DOI: 10.1093/nar/gkad180] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 03/18/2023] Open
Abstract
The Paf1 complex (Paf1C) is a conserved transcription elongation factor that regulates transcription elongation efficiency, facilitates co-transcriptional histone modifications, and impacts molecular processes linked to RNA synthesis, such as polyA site selection. Coupling of the activities of Paf1C to transcription elongation requires its association with RNA polymerase II (Pol II). Mutational studies in yeast identified Paf1C subunits Cdc73 and Rtf1 as important mediators of Paf1C association with Pol II on active genes. While the interaction between Rtf1 and the general elongation factor Spt5 is relatively well-understood, the interactions involving Cdc73 have not been fully elucidated. Using a site-specific protein cross-linking strategy in yeast cells, we identified direct interactions between Cdc73 and two components of the Pol II elongation complex, the elongation factor Spt6 and the largest subunit of Pol II. Both of these interactions require the tandem SH2 domain of Spt6. We also show that Cdc73 and Spt6 can interact in vitro and that rapid depletion of Spt6 dissociates Paf1 from chromatin, altering patterns of Paf1C-dependent histone modifications genome-wide. These results reveal interactions between Cdc73 and the Pol II elongation complex and identify Spt6 as a key factor contributing to the occupancy of Paf1C at active genes in Saccharomyces cerevisiae.
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Affiliation(s)
- Mitchell A Ellison
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | | | - Margaret K Shirra
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Matthew S Blacksmith
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Rachel A Schusteff
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Eleanor M Kerr
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Fei Fang
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yufei Xiang
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yi Shi
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Karen M Arndt
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
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30
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Ghanaeian A, Majhi S, McCaffrey CL, Nami B, Black CS, Yang SK, Legal T, Papoulas O, Janowska M, Valente-Paterno M, Marcotte EM, Wloga D, Bui KH. Integrated modeling of the Nexin-dynein regulatory complex reveals its regulatory mechanism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.31.543107. [PMID: 37398254 PMCID: PMC10312493 DOI: 10.1101/2023.05.31.543107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Cilia are hairlike protrusions that project from the surface of eukaryotic cells and play key roles in cell signaling and motility. Ciliary motility is regulated by the conserved nexin-dynein regulatory complex (N-DRC), which links adjacent doublet microtubules and regulates and coordinates the activity of outer doublet complexes. Despite its critical role in cilia motility, the assembly and molecular basis of the regulatory mechanism are poorly understood. Here, utilizing cryo-electron microscopy in conjunction with biochemical cross-linking and integrative modeling, we localized 12 DRC subunits in the N-DRC structure of Tetrahymena thermophila . We also found that the CCDC96/113 complex is in close contact with the N-DRC. In addition, we revealed that the N-DRC is associated with a network of coiled-coil proteins that most likely mediates N-DRC regulatory activity.
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Affiliation(s)
- Avrin Ghanaeian
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
| | - Sumita Majhi
- Laboratory of Cytoskeleton and Cilia Biology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
| | - Caitie L McCaffrey
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, United States
| | - Babak Nami
- Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Canada
| | - Corbin S Black
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
| | - Shun Kai Yang
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
| | - Thibault Legal
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
| | - Ophelia Papoulas
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, United States
| | - Martyna Janowska
- Laboratory of Cytoskeleton and Cilia Biology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
- current address: Laboratory of Immunology, Mossakowski Institute of Experimental and Clinical Medicine, Polish Academy of Science, Pawinskiego 5, 02-106 Warsaw, Poland
| | - Melissa Valente-Paterno
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
| | - Edward M Marcotte
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, United States
| | - Dorota Wloga
- Laboratory of Cytoskeleton and Cilia Biology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
| | - Khanh Huy Bui
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Québec, Canada
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31
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Vermeire PJ, Lilina AV, Hashim HM, Dlabolová L, Fiala J, Beelen S, Kukačka Z, Harvey JN, Novák P, Strelkov SV. Molecular structure of soluble vimentin tetramers. Sci Rep 2023; 13:8841. [PMID: 37258554 DOI: 10.1038/s41598-023-34814-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 05/08/2023] [Indexed: 06/02/2023] Open
Abstract
Intermediate filaments (IFs) are essential constituents of the metazoan cytoskeleton. A vast family of cytoplasmic IF proteins are capable of self-assembly from soluble tetrameric species into typical 10-12 nm wide filaments. The primary structure of these proteins includes the signature central 'rod' domain of ~ 300 residues which forms a dimeric α-helical coiled coil composed of three segments (coil1A, coil1B and coil2) interconnected by non-helical, flexible linkers (L1 and L12). The rod is flanked by flexible terminal head and tail domains. At present, the molecular architecture of mature IFs is only poorly known, limiting our capacity to rationalize the effect of numerous disease-related mutations found in IF proteins. Here we addressed the molecular structure of soluble vimentin tetramers which are formed by two antiparallel, staggered dimers with coil1B domains aligned (A11 tetramers). By examining a series of progressive truncations, we show that the presence of the coil1A domain is essential for the tetramer formation. In addition, we employed a novel chemical cross-linking pipeline including isotope labelling to identify intra- and interdimeric cross-links within the tetramer. We conclude that the tetramer is synergistically stabilized by the interactions of the aligned coil1B domains, the interactions between coil1A and the N-terminal portion of coil2, and the electrostatic attraction between the oppositely charged head and rod domains. Our cross-linking data indicate that, starting with a straight A11 tetramer, flexibility of linkers L1 and L12 enables 'backfolding' of both the coil1A and coil2 domains onto the tetrameric core formed by the coil1B domains. Through additional small-angle X-ray scattering experiments we show that the elongated A11 tetramers dominate in low ionic strength solutions, while there is also a significant structural flexibility especially in the terminal domains.
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Affiliation(s)
| | | | - Hani M Hashim
- Laboratory for Biocrystallography, KU Leuven, 3000, Leuven, Belgium
- Department of Chemistry, KU Leuven, 3000, Leuven, Belgium
| | - Lada Dlabolová
- Department of Biochemistry, Charles University, 12800, Prague, Czech Republic
- Institute of Microbiology of the Czech Academy of Sciences, 14220, Prague, Czech Republic
| | - Jan Fiala
- Department of Biochemistry, Charles University, 12800, Prague, Czech Republic
- Institute of Microbiology of the Czech Academy of Sciences, 14220, Prague, Czech Republic
| | - Steven Beelen
- Laboratory for Biocrystallography, KU Leuven, 3000, Leuven, Belgium
| | - Zdeněk Kukačka
- Department of Biochemistry, Charles University, 12800, Prague, Czech Republic
- Institute of Microbiology of the Czech Academy of Sciences, 14220, Prague, Czech Republic
| | | | - Petr Novák
- Department of Biochemistry, Charles University, 12800, Prague, Czech Republic
- Institute of Microbiology of the Czech Academy of Sciences, 14220, Prague, Czech Republic
| | - Sergei V Strelkov
- Laboratory for Biocrystallography, KU Leuven, 3000, Leuven, Belgium.
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32
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Shor B, Schneidman-Duhovny D. Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541003. [PMID: 37293053 PMCID: PMC10245790 DOI: 10.1101/2023.05.16.541003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.
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Affiliation(s)
- Ben Shor
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
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33
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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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Haubrich K, Spiteri VA, Farnaby W, Sobott F, Ciulli A. Breaking free from the crystal lattice: Structural biology in solution to study protein degraders. Curr Opin Struct Biol 2023; 79:102534. [PMID: 36804675 DOI: 10.1016/j.sbi.2023.102534] [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: 10/23/2022] [Revised: 12/15/2022] [Accepted: 01/06/2023] [Indexed: 02/17/2023]
Abstract
Structural biology offers a versatile arsenal of techniques and methods to investigate the structure and conformational dynamics of proteins and their assemblies. The growing field of targeted protein degradation centres on the premise of developing small molecules, termed degraders, to induce proximity between an E3 ligase and a protein of interest to be signalled for degradation. This new drug modality brings with it new opportunities and challenges to structural biologists. Here we discuss how several structural biology techniques, including nuclear magnetic resonance, cryo-electron microscopy, structural mass spectrometry and small angle scattering, have been explored to complement X-ray crystallography in studying degraders and their ternary complexes. Together the studies covered in this review make a case for the invaluable perspectives that integrative structural biology techniques in solution can bring to understanding ternary complexes and designing degraders.
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Affiliation(s)
- Kevin Haubrich
- Centre for Targeted Protein Degradation & Division of Biological Chemistry and Drug Discovery, School of Life Sciences, University of Dundee, Dundee, UK. https://twitter.com/KevinHaubrich1
| | - Valentina A Spiteri
- Centre for Targeted Protein Degradation & Division of Biological Chemistry and Drug Discovery, School of Life Sciences, University of Dundee, Dundee, UK. https://twitter.com/val_spiteri
| | - William Farnaby
- Centre for Targeted Protein Degradation & Division of Biological Chemistry and Drug Discovery, School of Life Sciences, University of Dundee, Dundee, UK. https://twitter.com/farnaby84
| | - Frank Sobott
- School of Molecular and Cellular Biology & Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, UK. https://twitter.com/FrankSobott
| | - Alessio Ciulli
- Centre for Targeted Protein Degradation & Division of Biological Chemistry and Drug Discovery, School of Life Sciences, University of Dundee, Dundee, UK.
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35
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Lee K, O'Reilly FJ. Cross-linking mass spectrometry for mapping protein complex topologies in situ. Essays Biochem 2023; 67:215-228. [PMID: 36734207 PMCID: PMC10070479 DOI: 10.1042/ebc20220168] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 02/04/2023]
Abstract
Cross-linking mass spectrometry has become an established technology to provide structural information on the topology and dynamics of protein complexes. Readily accessible workflows can provide detailed data on simplified systems, such as purified complexes. However, using this technology to study the structure of protein complexes in situ, such as in organelles, cells, and even tissues, is still a technological frontier. The complexity of these systems remains a considerable challenge, but there have been dramatic improvements in sample handling, data acquisition, and data processing. Here, we summarise these developments and describe the paths towards comprehensive and comparative structural interactomes by cross-linking mass spectrometry.
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Affiliation(s)
- Kitaik Lee
- Center for Structural Biology, Center for Cancer Research, National Cancer Institute (NCI), Frederick, MD 21702-1201, U.S.A
| | - Francis J O'Reilly
- Center for Structural Biology, Center for Cancer Research, National Cancer Institute (NCI), Frederick, MD 21702-1201, U.S.A
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36
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Kim SK, Dickinson MS, Finer-Moore J, Guan Z, Kaake RM, Echeverria I, Chen J, Pulido EH, Sali A, Krogan NJ, Rosenberg OS, Stroud RM. Structure and dynamics of the essential endogenous mycobacterial polyketide synthase Pks13. Nat Struct Mol Biol 2023; 30:296-308. [PMID: 36782050 PMCID: PMC10312659 DOI: 10.1038/s41594-022-00918-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 12/21/2022] [Indexed: 02/15/2023]
Abstract
The mycolic acid layer of the Mycobacterium tuberculosis cell wall is essential for viability and virulence, and the enzymes responsible for its synthesis are targets for antimycobacterial drug development. Polyketide synthase 13 (Pks13) is a module encoding several enzymatic and transport functions that carries out the condensation of two different long-chain fatty acids to produce mycolic acids. We determined structures by cryogenic-electron microscopy of dimeric multi-enzyme Pks13 purified from mycobacteria under normal growth conditions, captured with native substrates. Structures define the ketosynthase (KS), linker and acyl transferase (AT) domains at 1.8 Å resolution and two alternative locations of the N-terminal acyl carrier protein. These structures suggest intermediate states on the pathway for substrate delivery to the KS domain. Other domains, visible at lower resolution, are flexible relative to the KS-AT core. The chemical structures of three bound endogenous long-chain fatty acid substrates were determined by electrospray ionization mass spectrometry.
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Affiliation(s)
- Sun Kyung Kim
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | - Miles Sasha Dickinson
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
- Chemistry and Chemical Biology Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Janet Finer-Moore
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | - Ziqiang Guan
- Department of Biochemistry, Duke University Medical Center, Durham, NC, USA
| | - Robyn M Kaake
- Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA
| | - Jen Chen
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | - Ernst H Pulido
- Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Nevan J Krogan
- Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA
| | - Oren S Rosenberg
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA.
- Department of Medicine, Division of Infectious Diseases, University of California San Francisco, San Francisco, CA, USA.
| | - Robert M Stroud
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA.
- Chemistry and Chemical Biology Graduate Program, University of California San Francisco, San Francisco, CA, USA.
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Gorbea Colón JJ, Palao L, Chen SF, Kim HJ, Snyder L, Chang YW, Tsai KL, Murakami K. Structural basis of a transcription pre-initiation complex on a divergent promoter. Mol Cell 2023; 83:574-588.e11. [PMID: 36731470 PMCID: PMC10162435 DOI: 10.1016/j.molcel.2023.01.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 11/28/2022] [Accepted: 01/06/2023] [Indexed: 02/04/2023]
Abstract
Most eukaryotic promoter regions are divergently transcribed. As the RNA polymerase II pre-initiation complex (PIC) is intrinsically asymmetric and responsible for transcription in a single direction, it is unknown how divergent transcription arises. Here, the Saccharomyces cerevisiae Mediator complexed with a PIC (Med-PIC) was assembled on a divergent promoter and analyzed by cryoelectron microscopy. The structure reveals two distinct Med-PICs forming a dimer through the Mediator tail module, induced by a homodimeric activator protein localized near the dimerization interface. The tail dimer is associated with ∼80-bp upstream DNA, such that two flanking core promoter regions are positioned and oriented in a suitable form for PIC assembly in opposite directions. Also, cryoelectron tomography visualized the progress of the PIC assembly on the two core promoter regions, providing direct evidence for the role of the Med-PIC dimer in divergent transcription.
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Affiliation(s)
- Jose J Gorbea Colón
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Leon Palao
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shin-Fu Chen
- Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Hee Jong Kim
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Laura Snyder
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yi-Wei Chang
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Kuang-Lei Tsai
- Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
| | - Kenji Murakami
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Center for Genome Integrity, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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38
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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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39
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Cohen T, Halfon M, Carter L, Sharkey B, Jain T, Sivasubramanian A, Schneidman-Duhovny D. Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models. Methods Enzymol 2022; 678:237-262. [PMID: 36641210 DOI: 10.1016/bs.mie.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD<4Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.
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Affiliation(s)
- Tomer Cohen
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Matan Halfon
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lester Carter
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, United States
| | - Beth Sharkey
- High-Throughput Expression, Adimab LLC, Lebanon, NH, United States
| | - Tushar Jain
- Computational Biology, Adimab LLC, Palo Alto, CA, United States
| | | | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
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40
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Burley SK, Berman HM, Chiu W, Dai W, Flatt JW, Hudson BP, Kaelber JT, Khare SD, Kulczyk AW, Lawson CL, Pintilie GD, Sali A, Vallat B, Westbrook JD, Young JY, Zardecki C. Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future. Biophys Rev 2022; 14:1281-1301. [PMID: 36474933 PMCID: PMC9715422 DOI: 10.1007/s12551-022-01013-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/06/2022] [Indexed: 12/04/2022] Open
Abstract
As a discipline, structural biology has been transformed by the three-dimensional electron microscopy (3DEM) "Resolution Revolution" made possible by convergence of robust cryo-preservation of vitrified biological materials, sample handling systems, and measurement stages operating a liquid nitrogen temperature, improvements in electron optics that preserve phase information at the atomic level, direct electron detectors (DEDs), high-speed computing with graphics processing units, and rapid advances in data acquisition and processing software. 3DEM structure information (atomic coordinates and related metadata) are archived in the open-access Protein Data Bank (PDB), which currently holds more than 11,000 3DEM structures of proteins and nucleic acids, and their complexes with one another and small-molecule ligands (~ 6% of the archive). Underlying experimental data (3DEM density maps and related metadata) are stored in the Electron Microscopy Data Bank (EMDB), which currently holds more than 21,000 3DEM density maps. After describing the history of the PDB and the Worldwide Protein Data Bank (wwPDB) partnership, which jointly manages both the PDB and EMDB archives, this review examines the origins of the resolution revolution and analyzes its impact on structural biology viewed through the lens of PDB holdings. Six areas of focus exemplifying the impact of 3DEM across the biosciences are discussed in detail (icosahedral viruses, ribosomes, integral membrane proteins, SARS-CoV-2 spike proteins, cryogenic electron tomography, and integrative structure determination combining 3DEM with complementary biophysical measurement techniques), followed by a review of 3DEM structure validation by the wwPDB that underscores the importance of community engagement.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093 USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Helen M. Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Wah Chiu
- Department of Bioengineering, Stanford University, Stanford, CA USA
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA USA
| | - Wei Dai
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Jason T. Kaelber
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Sagar D. Khare
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854 USA
| | - Arkadiusz W. Kulczyk
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, Piscataway, NJ 08901 USA
| | - Catherine L. Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | | | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158 USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901 USA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA
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41
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Beton JG, Cragnolini T, Kaleel M, Mulvaney T, Sweeney A, Topf M. Integrating model simulation tools and
cryo‐electron
microscopy. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Joseph George Beton
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Birkbeck and University College London London UK
| | - Manaz Kaleel
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Aaron Sweeney
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
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42
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McCafferty CL, Papoulas O, Jordan MA, Hoogerbrugge G, Nichols C, Pigino G, Taylor DW, Wallingford JB, Marcotte EM. Integrative modeling reveals the molecular architecture of the intraflagellar transport A (IFT-A) complex. eLife 2022; 11:e81977. [PMID: 36346217 PMCID: PMC9674347 DOI: 10.7554/elife.81977] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/07/2022] [Indexed: 11/10/2022] Open
Abstract
Intraflagellar transport (IFT) is a conserved process of cargo transport in cilia that is essential for development and homeostasis in organisms ranging from algae to vertebrates. In humans, variants in genes encoding subunits of the cargo-adapting IFT-A and IFT-B protein complexes are a common cause of genetic diseases known as ciliopathies. While recent progress has been made in determining the atomic structure of IFT-B, little is known of the structural biology of IFT-A. Here, we combined chemical cross-linking mass spectrometry and cryo-electron tomography with AlphaFold2-based prediction of both protein structures and interaction interfaces to model the overall architecture of the monomeric six-subunit IFT-A complex, as well as its polymeric assembly within cilia. We define monomer-monomer contacts and membrane-associated regions available for association with transported cargo, and we also use this model to provide insights into the pleiotropic nature of human ciliopathy-associated genetic variants in genes encoding IFT-A subunits. Our work demonstrates the power of integration of experimental and computational strategies both for multi-protein structure determination and for understanding the etiology of human genetic disease.
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Affiliation(s)
- Caitlyn L McCafferty
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | - Ophelia Papoulas
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | - Mareike A Jordan
- Max Planck Institute of Molecular Cell Biology and GeneticsDresdenGermany
| | - Gabriel Hoogerbrugge
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | - Candice Nichols
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | | | - David W Taylor
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | - John B Wallingford
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
| | - Edward M Marcotte
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of TexasAustinUnited States
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43
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Van Leene J, Eeckhout D, Gadeyne A, Matthijs C, Han C, De Winne N, Persiau G, Van De Slijke E, Persyn F, Mertens T, Smagghe W, Crepin N, Broucke E, Van Damme D, Pleskot R, Rolland F, De Jaeger G. Mapping of the plant SnRK1 kinase signalling network reveals a key regulatory role for the class II T6P synthase-like proteins. NATURE PLANTS 2022; 8:1245-1261. [PMID: 36376753 DOI: 10.1038/s41477-022-01269-w] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 09/09/2022] [Indexed: 06/16/2023]
Abstract
The central metabolic regulator SnRK1 controls plant growth and survival upon activation by energy depletion, but detailed molecular insight into its regulation and downstream targets is limited. Here we used phosphoproteomics to infer the sucrose-dependent processes targeted upon starvation by kinases as SnRK1, corroborating the relation of SnRK1 with metabolic enzymes and transcriptional regulators, while also pointing to SnRK1 control of intracellular trafficking. Next, we integrated affinity purification, proximity labelling and crosslinking mass spectrometry to map the protein interaction landscape, composition and structure of the SnRK1 heterotrimer, providing insight in its plant-specific regulation. At the intersection of this multi-dimensional interactome, we discovered a strong association of SnRK1 with class II T6P synthase (TPS)-like proteins. Biochemical and cellular assays show that TPS-like proteins function as negative regulators of SnRK1. Next to stable interactions with the TPS-like proteins, similar intricate connections were found with known regulators, suggesting that plants utilize an extended kinase complex to fine-tune SnRK1 activity for optimal responses to metabolic stress.
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Affiliation(s)
- Jelle Van Leene
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Dominique Eeckhout
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Astrid Gadeyne
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Caroline Matthijs
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Chao Han
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- The Key Laboratory of Plant Development and Environmental Adaptation Biology, Ministry of Education, School of Life Sciences, Shandong University, Qingdao, China
| | - Nancy De Winne
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Geert Persiau
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Eveline Van De Slijke
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Freya Persyn
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Toon Mertens
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Wouter Smagghe
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Nathalie Crepin
- Laboratory for Molecular Plant Biology, Biology Department, KU Leuven, Heverlee-Leuven, Belgium
- KU Leuven Plant Institute-LPI, Heverlee-Leuven, Belgium
| | - Ellen Broucke
- Laboratory for Molecular Plant Biology, Biology Department, KU Leuven, Heverlee-Leuven, Belgium
- KU Leuven Plant Institute-LPI, Heverlee-Leuven, Belgium
| | - Daniël Van Damme
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Roman Pleskot
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Institute of Experimental Botany, Czech Academy of Sciences, Prague, Czech Republic
| | - Filip Rolland
- Laboratory for Molecular Plant Biology, Biology Department, KU Leuven, Heverlee-Leuven, Belgium
- KU Leuven Plant Institute-LPI, Heverlee-Leuven, Belgium
| | - Geert De Jaeger
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium.
- VIB Center for Plant Systems Biology, Ghent, Belgium.
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44
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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: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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 .
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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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45
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Arvindekar S, Jackman MJ, Low JKK, Landsberg MJ, Mackay JP, Viswanath S. Molecular architecture of nucleosome remodeling and deacetylase sub-complexes by integrative structure determination. Protein Sci 2022; 31:e4387. [PMID: 36040254 PMCID: PMC9413472 DOI: 10.1002/pro.4387] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/18/2022] [Accepted: 06/19/2022] [Indexed: 11/11/2022]
Abstract
The nucleosome remodeling and deacetylase (NuRD) complex is a chromatin-modifying assembly that regulates gene expression and DNA damage repair. Despite its importance, limited structural information describing the complete NuRD complex is available and a detailed understanding of its mechanism is therefore lacking. Drawing on information from SEC-MALLS, DIA-MS, XLMS, negative-stain EM, X-ray crystallography, NMR spectroscopy, secondary structure predictions, and homology models, we applied Bayesian integrative structure determination to investigate the molecular architecture of three NuRD sub-complexes: MTA1-HDAC1-RBBP4, MTA1N -HDAC1-MBD3GATAD2CC , and MTA1-HDAC1-RBBP4-MBD3-GATAD2A [nucleosome deacetylase (NuDe)]. The integrative structures were corroborated by examining independent crosslinks, cryo-EM maps, biochemical assays, known cancer-associated mutations, and structure predictions from AlphaFold. The robustness of the models was assessed by jack-knifing. Localization of the full-length MBD3, which connects the deacetylase and chromatin remodeling modules in NuRD, has not previously been possible; our models indicate two different locations for MBD3, suggesting a mechanism by which MBD3 in the presence of GATAD2A asymmetrically bridges the two modules in NuRD. Further, our models uncovered three previously unrecognized subunit interfaces in NuDe: HDAC1C -MTA1BAH , MTA1BAH -MBD3MBD , and HDAC160-100 -MBD3MBD . Our approach also allowed us to localize regions of unknown structure, such as HDAC1C and MBD3IDR , thereby resulting in the most complete and robustly cross-validated structural characterization of these NuRD sub-complexes so far.
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Affiliation(s)
- Shreyas Arvindekar
- National Centre for Biological SciencesTata Institute of Fundamental ResearchBangaloreIndia
| | - Matthew J. Jackman
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia
| | - Jason K. K. Low
- School of Life and Environmental SciencesUniversity of SydneySydneyNew South WalesAustralia
| | - Michael J. Landsberg
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia
| | - Joel P. Mackay
- School of Life and Environmental SciencesUniversity of SydneySydneyNew South WalesAustralia
| | - Shruthi Viswanath
- National Centre for Biological SciencesTata Institute of Fundamental ResearchBangaloreIndia
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46
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Sae-Lee W, McCafferty CL, Verbeke EJ, Havugimana PC, Papoulas O, McWhite CD, Houser JR, Vanuytsel K, Murphy GJ, Drew K, Emili A, Taylor DW, Marcotte EM. The protein organization of a red blood cell. Cell Rep 2022; 40:111103. [PMID: 35858567 PMCID: PMC9764456 DOI: 10.1016/j.celrep.2022.111103] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/18/2022] [Accepted: 06/24/2022] [Indexed: 11/28/2022] Open
Abstract
Red blood cells (RBCs) (erythrocytes) are the simplest primary human cells, lacking nuclei and major organelles and instead employing about a thousand proteins to dynamically control cellular function and morphology in response to physiological cues. In this study, we define a canonical RBC proteome and interactome using quantitative mass spectrometry and machine learning. Our data reveal an RBC interactome dominated by protein homeostasis, redox biology, cytoskeletal dynamics, and carbon metabolism. We validate protein complexes through electron microscopy and chemical crosslinking and, with these data, build 3D structural models of the ankyrin/Band 3/Band 4.2 complex that bridges the spectrin cytoskeleton to the RBC membrane. The model suggests spring-like compression of ankyrin may contribute to the characteristic RBC cell shape and flexibility. Taken together, our study provides an in-depth view of the global protein organization of human RBCs and serves as a comprehensive resource for future research.
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Affiliation(s)
- Wisath Sae-Lee
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX 78712, USA
| | - Caitlyn L McCafferty
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX 78712, USA
| | - Eric J Verbeke
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX 78712, USA
| | - Pierre C Havugimana
- Center for Network Systems Biology, Boston University, Boston, MA 02118, USA
| | - Ophelia Papoulas
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX 78712, USA
| | - Claire D McWhite
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX 78712, USA
| | - John R Houser
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX 78712, USA
| | - Kim Vanuytsel
- Center for Regenerative Medicine, Boston University School of Medicine, 670 Albany Street, Boston, MA 02118, USA
| | - George J Murphy
- Center for Regenerative Medicine, Boston University School of Medicine, 670 Albany Street, Boston, MA 02118, USA
| | - Kevin Drew
- Department of Biological Sciences, University of Illinois at Chicago, 900 S. Ashland Avenue, Chicago, IL 60607, USA
| | - Andrew Emili
- Center for Network Systems Biology, Boston University, Boston, MA 02118, USA
| | - David W Taylor
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX 78712, USA
| | - Edward M Marcotte
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, University of Texas, Austin, TX 78712, USA.
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47
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Sipe SN, Jeanne Dit Fouque K, Garabedian A, Leng F, Fernandez-Lima F, Brodbelt JS. Exploring the Conformations and Binding Location of HMGA2·DNA Complexes Using Ion Mobility Spectrometry and 193 nm Ultraviolet Photodissociation Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1092-1102. [PMID: 35687872 PMCID: PMC9274541 DOI: 10.1021/jasms.2c00083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Although it is widely accepted that protein function is largely dependent on its structure, intrinsically disordered proteins (IDPs) lack defined structure but are essential in proper cellular processes. Mammalian high mobility group proteins (HMGA) are one such example of IDPs that perform a number of crucial nuclear activities and have been highly studied due to their involvement in the proliferation of a variety of disease and cancers. Traditional structural characterization methods have had limited success in understanding HMGA proteins and their ability to coordinate to DNA. Ion mobility spectrometry and mass spectrometry provide insights into the diversity and heterogeneity of structures adopted by IDPs and are employed here to interrogate HMGA2 in its unbound states and bound to two DNA hairpins. The broad distribution of collision cross sections observed for the apo-protein are restricted when HMGA2 is bound to DNA, suggesting that increased protein organization is promoted in the holo-form. Ultraviolet photodissociation was utilized to probe the changes in structures for the compact and elongated structures of HMGA2 by analyzing backbone cleavage propensities and solvent accessibility based on charge-site analysis, which revealed a spectrum of conformational possibilities. Namely, preferential binding of the DNA hairpins with the second of three AT-hooks of HMGA2 is suggested based on the suppression of backbone fragmentation and distribution of DNA-containing protein fragments.
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Affiliation(s)
- Sarah N Sipe
- Department of Chemistry, University of Texas, Austin, Texas 78712 United States
| | - Kevin Jeanne Dit Fouque
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida 33199, United States
| | - Alyssa Garabedian
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida 33199, United States
| | - Fenfei Leng
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida 33199, United States
- Biomolecular Sciences Institute, Florida International University, Miami, Florida 33199, United States
| | - Francisco Fernandez-Lima
- Department of Chemistry and Biochemistry, Florida International University, Miami, Florida 33199, United States
- Biomolecular Sciences Institute, Florida International University, Miami, Florida 33199, United States
| | - Jennifer S Brodbelt
- Department of Chemistry, University of Texas, Austin, Texas 78712 United States
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Xu B, Zhu Y, Cao C, Chen H, Jin Q, Li G, Ma J, Yang SL, Zhao J, Zhu J, Ding Y, Fang X, Jin Y, Kwok CK, Ren A, Wan Y, Wang Z, Xue Y, Zhang H, Zhang QC, Zhou Y. Recent advances in RNA structurome. SCIENCE CHINA. LIFE SCIENCES 2022; 65:1285-1324. [PMID: 35717434 PMCID: PMC9206424 DOI: 10.1007/s11427-021-2116-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/01/2022] [Indexed: 12/27/2022]
Abstract
RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.
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Affiliation(s)
- Bingbing Xu
- MOE Laboratory of Biosystems Homeostasis & Protection, Innovation Center for Cell Signaling Network, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yanda Zhu
- MOE Laboratory of Biosystems Homeostasis & Protection, Innovation Center for Cell Signaling Network, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Changchang Cao
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Hao Chen
- Life Sciences Institute, Zhejiang University, Hangzhou, 310058, China
| | - Qiongli Jin
- State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Guangnan Li
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Junfeng Ma
- Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Siwy Ling Yang
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Jieyu Zhao
- Department of Chemistry, and State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Jianghui Zhu
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing, 100084, China
| | - Yiliang Ding
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, United Kingdom.
| | - Xianyang Fang
- Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
| | - Yongfeng Jin
- MOE Laboratory of Biosystems Homeostasis & Protection, Innovation Center for Cell Signaling Network, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
| | - Chun Kit Kwok
- Department of Chemistry, and State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China.
- Shenzhen Research Institute of City University of Hong Kong, Shenzhen, 518057, China.
| | - Aiming Ren
- Life Sciences Institute, Zhejiang University, Hangzhou, 310058, China.
| | - Yue Wan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore.
| | - Zhiye Wang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
| | - Yuanchao Xue
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100101, China.
| | - Huakun Zhang
- Key Laboratory of Molecular Epigenetics of the Ministry of Education, Northeast Normal University, Changchun, 130024, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
- Tsinghua-Peking Center for Life Sciences, Beijing, 100084, China.
| | - Yu Zhou
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
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49
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Ullanat V, Kasukurthi N, Viswanath S. PrISM: Precision for Integrative Structural Models. Bioinformatics 2022; 38:3837-3839. [PMID: 35723541 DOI: 10.1093/bioinformatics/btac400] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/04/2022] [Accepted: 06/16/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION A single precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information. RESULTS We develop PrISM (Precision for Integrative Structural Models), to efficiently identify high and low-precision regions for integrative models. AVAILABILITY PrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper is available at doi:10.5281/zenodo.6241200. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Varun Ullanat
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Nikhil Kasukurthi
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Shruthi Viswanath
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
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50
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Braberg H, Echeverria I, Kaake RM, Sali A, Krogan NJ. From systems to structure - using genetic data to model protein structures. Nat Rev Genet 2022; 23:342-354. [PMID: 35013567 PMCID: PMC8744059 DOI: 10.1038/s41576-021-00441-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2021] [Indexed: 12/11/2022]
Abstract
Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.
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Affiliation(s)
- Hannes Braberg
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Robyn M Kaake
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Gladstone Institutes, San Francisco, CA, USA
| | - Andrej Sali
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Gladstone Institutes, San Francisco, CA, USA.
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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