1
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Butterfield ER, Obado SO, Scutts SR, Zhang W, Chait BT, Rout MP, Field MC. A lineage-specific protein network at the trypanosome nuclear envelope. Nucleus 2024; 15:2310452. [PMID: 38605598 PMCID: PMC11018031 DOI: 10.1080/19491034.2024.2310452] [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: 10/19/2023] [Accepted: 01/18/2024] [Indexed: 04/13/2024] Open
Abstract
The nuclear envelope (NE) separates translation and transcription and is the location of multiple functions, including chromatin organization and nucleocytoplasmic transport. The molecular basis for many of these functions have diverged between eukaryotic lineages. Trypanosoma brucei, a member of the early branching eukaryotic lineage Discoba, highlights many of these, including a distinct lamina and kinetochore composition. Here, we describe a cohort of proteins interacting with both the lamina and NPC, which we term lamina-associated proteins (LAPs). LAPs represent a diverse group of proteins, including two candidate NPC-anchoring pore membrane proteins (POMs) with architecture conserved with S. cerevisiae and H. sapiens, and additional peripheral components of the NPC. While many of the LAPs are Kinetoplastid specific, we also identified broadly conserved proteins, indicating an amalgam of divergence and conservation within the trypanosome NE proteome, highlighting the diversity of nuclear biology across the eukaryotes, increasing our understanding of eukaryotic and NPC evolution.
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Affiliation(s)
| | - Samson O. Obado
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY, USA
| | - Simon R. Scutts
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Wenzhu Zhang
- 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
| | - Michael P. Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY, USA
| | - Mark C. Field
- School of Life Sciences, University of Dundee, Dundee, UK
- Biology Centre, Czech Academy of Sciences, Institute of Parasitology, České Budějovice, Czech Republic
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2
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Lin J, Sumara I. Cytoplasmic nucleoporin assemblage: the cellular artwork in physiology and disease. Nucleus 2024; 15:2387534. [PMID: 39135336 PMCID: PMC11323873 DOI: 10.1080/19491034.2024.2387534] [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: 05/08/2024] [Revised: 07/29/2024] [Accepted: 07/29/2024] [Indexed: 08/16/2024] Open
Abstract
Nucleoporins, essential proteins building the nuclear pore, are pivotal for ensuring nucleocytoplasmic transport. While traditionally confined to the nuclear envelope, emerging evidence indicates their presence in various cytoplasmic structures, suggesting potential non-transport-related roles. This review consolidates findings on cytoplasmic nucleoporin assemblies across different states, including normal physiological conditions, stress, and pathology, exploring their structural organization, formation dynamics, and functional implications. We summarize the current knowledge and the latest concepts on the regulation of nucleoporin homeostasis, aiming to enhance our understanding of their unexpected roles in physiological and pathological processes.
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Affiliation(s)
- Junyan Lin
- Department of Development and Stem Cells, Institute of Genetics and Molecular and Cellular Biology (IGBMC), Illkirch, France
- Centre National de la Recherche Scientifique UMR 7104, Strasbourg, France
- Institut National de la Santé et de la Recherche Médicale U964, Strasbourg, France
- Université de Strasbourg, Strasbourg, France
| | - Izabela Sumara
- Department of Development and Stem Cells, Institute of Genetics and Molecular and Cellular Biology (IGBMC), Illkirch, France
- Centre National de la Recherche Scientifique UMR 7104, Strasbourg, France
- Institut National de la Santé et de la Recherche Médicale U964, Strasbourg, France
- Université de Strasbourg, Strasbourg, France
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3
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Ille AM, Markosian C, Burley SK, Mathews MB, Pasqualini R, Arap W. Generative artificial intelligence performs rudimentary structural biology modeling. Sci Rep 2024; 14:19372. [PMID: 39169047 PMCID: PMC11339285 DOI: 10.1038/s41598-024-69021-2] [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: 03/26/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024] Open
Abstract
Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between the anti-viral nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the current capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.
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Affiliation(s)
- Alexander M Ille
- School of Graduate Studies, Rutgers, The State University of New Jersey, Newark, NJ, USA
- Rutgers Cancer Institute, Newark, NJ, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Christopher Markosian
- School of Graduate Studies, Rutgers, The State University of New Jersey, Newark, NJ, USA
- Rutgers Cancer Institute, Newark, NJ, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Rutgers Cancer Institute, New Brunswick, NJ, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California-San Diego, La Jolla, San Diego, CA, USA
| | - Michael B Mathews
- School of Graduate Studies, Rutgers, The State University of New Jersey, Newark, NJ, USA
- Division of Infectious Disease, Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute, Newark, NJ, USA.
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA.
| | - Wadih Arap
- Rutgers Cancer Institute, Newark, NJ, USA.
- Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA.
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4
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Stankunas E, Köhler A. Docking a flexible basket onto the core of the nuclear pore complex. Nat Cell Biol 2024:10.1038/s41556-024-01484-x. [PMID: 39138317 DOI: 10.1038/s41556-024-01484-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 07/17/2024] [Indexed: 08/15/2024]
Abstract
The nuclear basket attaches to the nucleoplasmic side of the nuclear pore complex (NPC), coupling transcription to mRNA quality control and export. The basket expands the functional repertoire of a subset of NPCs in Saccharomyces cerevisiae by drawing a unique RNA/protein interactome. Yet, how the basket docks onto the NPC core remains unknown. By integrating AlphaFold-based interaction screens, electron microscopy and membrane-templated reconstitution, we uncovered a membrane-anchored tripartite junction between basket and NPC core. The basket subunit Nup60 harbours three adjacent short linear motifs, which connect Mlp1, a parallel homodimer consisting of coiled-coil segments interrupted by flexible hinges, and the Nup85 subunit of the Y-complex. We reconstituted the Y-complex•Nup60•Mlp1 assembly on a synthetic membrane and validated the protein interfaces in vivo. Here we explain how a short linear motif-based protein junction can substantially reshape NPC structure and function, advancing our understanding of compositional and conformational NPC heterogeneity.
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Affiliation(s)
- Edvinas Stankunas
- Max Perutz Labs, Vienna Biocenter Campus, University of Vienna and Medical University of Vienna, Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Alwin Köhler
- Max Perutz Labs, Vienna Biocenter Campus, University of Vienna and Medical University of Vienna, Vienna, Austria.
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5
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Latham AP, Tempkin JOB, Otsuka S, Zhang W, Ellenberg J, Sali A. Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.06.606842. [PMID: 39149317 PMCID: PMC11326192 DOI: 10.1101/2024.08.06.606842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.
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6
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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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7
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Kyrilis FL, Low JKK, Mackay JP, Kastritis PL. Structural biology in cellulo: Minding the gap between conceptualization and realization. Curr Opin Struct Biol 2024; 87:102843. [PMID: 38788606 DOI: 10.1016/j.sbi.2024.102843] [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: 02/21/2024] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/26/2024]
Abstract
Recent technological advances have deepened our perception of cellular structure. However, most structural data doesn't originate from intact cells, limiting our understanding of cellular processes. Here, we discuss current and future developments that will bring us towards a structural picture of the cell. Electron cryotomography is the standard bearer, with its ability to provide in cellulo snapshots. Single-particle electron microscopy (of purified biomolecules and of complex mixtures) and covalent crosslinking combined with mass spectrometry also have significant roles to play, as do artificial intelligence algorithms in their many guises. To integrate these multiple approaches, data curation and standardisation will be critical - as is the need to expand efforts beyond our current protein-centric view to the other (macro)molecules that sustain life.
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Affiliation(s)
- Fotis L Kyrilis
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece. https://twitter.com/Fotansky_16
| | - Jason K K Low
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia
| | - Joel P Mackay
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia.
| | - Panagiotis L Kastritis
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece; Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, Halle/Saale, Germany; Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3, Halle/Saale, Germany; Biozentrum, Martin Luther University Halle-Wittenberg, Weinbergweg 22, Halle/Saale, Germany.
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8
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Watson AJI, Bartesaghi A. Advances in cryo-ET data processing: meeting the demands of visual proteomics. Curr Opin Struct Biol 2024; 87:102861. [PMID: 38889501 PMCID: PMC11283971 DOI: 10.1016/j.sbi.2024.102861] [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: 02/13/2024] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024]
Abstract
Cryogenic electron tomography (cryo-ET), a method that enables the viewing of biomolecules in near-native environments at high resolution, is rising in accessibility and applicability. Over the past several years, once slow sample preparation and data collection procedures have seen innovations which enable rapid collection of the large datasets required for attaining high resolution structures. Increased data availability has provided a driving force for exciting improvements in cryo-ET data processing methodologies throughout the entire processing pipeline and the development of accessible graphical user interfaces (GUIs) that enable individuals inexperienced in computational fields to convert raw tilt series into 3D structures. These advances in data processing are enabling cryo-ET to attain higher resolution and extending its applicability to more complex samples.
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Affiliation(s)
- Abigail J I Watson
- Department of Biochemistry, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Alberto Bartesaghi
- Department of Biochemistry, Duke University School of Medicine, Durham, NC, 27710, USA; Department of Computer Science, Duke University, Durham, NC, 27708, USA; Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, USA.
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9
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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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10
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Brown CM, Marrink SJ. Modeling membranes in situ. Curr Opin Struct Biol 2024; 87:102837. [PMID: 38744147 DOI: 10.1016/j.sbi.2024.102837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/26/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
Molecular dynamics simulations of cellular membranes have come a long way-from simple model lipid bilayers to multicomponent systems capturing the crowded and complex nature of real cell membranes. In this opinionated minireview, we discuss the current challenge to simulate the dynamics of membranes in their native environment, in situ, with the prospect of reaching the level of whole cells and cell organelles using an integrative modeling framework.
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Affiliation(s)
- Chelsea M Brown
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands. https://twitter.com/chelseabrowncg
| | - Siewert J Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands. s.j.marrinkrug.nl
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11
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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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12
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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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13
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Gapsys V, Kopec W, Matthes D, de Groot BL. Biomolecular simulations at the exascale: From drug design to organelles and beyond. Curr Opin Struct Biol 2024; 88:102887. [PMID: 39029280 DOI: 10.1016/j.sbi.2024.102887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 06/07/2024] [Accepted: 06/24/2024] [Indexed: 07/21/2024]
Abstract
The rapid advancement in computational power available for research offers to bring not only quantitative improvements, but also qualitative changes in the field of biomolecular simulation. Here, we review the state of biomolecular dynamics simulations at the threshold to exascale resources becoming available. Both developments in parallel and distributed computing will be discussed, providing a perspective on the state of the art of both. A main focus will be on obtaining binding and conformational free energies, with an outlook to macromolecular complexes and (sub)cellular assemblies.
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Affiliation(s)
- Vytautas Gapsys
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse 2340, Belgium. https://twitter.com/VytasGapsys
| | - Wojciech Kopec
- Department of Chemistry, Queen Mary University of London, 327 Mile End Road, London E1 4NS, UK; Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany. https://twitter.com/wojciechkopec3
| | - Dirk Matthes
- Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany.
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14
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Brueckner AC, Shields B, Kirubakaran P, Suponya A, Panda M, Posy SL, Johnson S, Lakkaraju SK. MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics. J Comput Aided Mol Des 2024; 38:24. [PMID: 39014286 DOI: 10.1007/s10822-024-00564-2] [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] [Accepted: 06/28/2024] [Indexed: 07/18/2024]
Abstract
Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.
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Affiliation(s)
| | - Benjamin Shields
- Molecular Structure & Design, Bristol Myers Squibb, Princeton, NJ, 08540, USA
| | - Palani Kirubakaran
- Biocon Bristol Myers Squibb R&D Centre, Bangalore, 560099, Karnataka, India
| | - Alexander Suponya
- Molecular Structure & Design, Bristol Myers Squibb, Princeton, NJ, 08540, USA
| | - Manoranjan Panda
- Molecular Structure & Design, Bristol Myers Squibb, Princeton, NJ, 08540, USA
| | - Shana L Posy
- Molecular Structure & Design, Bristol Myers Squibb, Princeton, NJ, 08540, USA
| | - Stephen Johnson
- Molecular Structure & Design, Bristol Myers Squibb, Princeton, NJ, 08540, USA
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15
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Hutchings J, Villa E. Expanding insights from in situ cryo-EM. Curr Opin Struct Biol 2024; 88:102885. [PMID: 38996624 DOI: 10.1016/j.sbi.2024.102885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 05/28/2024] [Accepted: 06/21/2024] [Indexed: 07/14/2024]
Abstract
The combination of cryo-electron tomography and subtomogram analysis affords 3D high-resolution views of biological macromolecules in their native cellular environment, or in situ. Streamlined methods for acquiring and processing these data are advancing attainable resolutions into the realm of drug discovery. Yet regardless of resolution, structure prediction driven by artificial intelligence (AI) combined with subtomogram analysis is becoming powerful in understanding macromolecular assemblies. Automated and AI-assisted data mining is increasingly necessary to cope with the growing wealth of tomography data and to maximize the information obtained from them. Leveraging developments from AI and single-particle analysis could be essential in fulfilling the potential of in situ cryo-EM. Here, we highlight new developments for in situ cryo-EM and the emerging potential for AI in this process.
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Affiliation(s)
- Joshua Hutchings
- 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
| | - 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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16
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Wu F, Huang Y, Yang G, Ye S, Mukamel S, Jiang J. Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model. Proc Natl Acad Sci U S A 2024; 121:e2409257121. [PMID: 38917009 PMCID: PMC11228460 DOI: 10.1073/pnas.2409257121] [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: 05/09/2024] [Accepted: 05/28/2024] [Indexed: 06/27/2024] Open
Abstract
Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a "spectrum-structure" correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention-based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments.
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Affiliation(s)
- Fan Wu
- Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei230026, Anhui, China
| | - Yan Huang
- Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei230026, Anhui, China
| | - Guokun Yang
- Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei230026, Anhui, China
| | - Sheng Ye
- Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of Artificial Intelligence, Anhui University, Hefei230601, Anhui, China
| | - Shaul Mukamel
- Department of Chemistry and of Physics & Astronomy, University of California, Irvine, CA92697
| | - Jun Jiang
- Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei230026, Anhui, China
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17
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Borges-Araújo L, Pereira GP, Valério M, Souza PCT. Assessing the Martini 3 protein model: A review of its path and potential. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2024; 1872:141014. [PMID: 38670324 DOI: 10.1016/j.bbapap.2024.141014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/13/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024]
Abstract
Coarse-grained (CG) protein models have become indispensable tools for studying many biological protein details, from conformational dynamics to the organization of protein macro-complexes, and even the interaction of proteins with other molecules. The Martini force field is one of the most widely used CG models for bio-molecular simulations, partly because of the enormous success of its protein model. With the recent release of a new and improved version of the Martini force field - Martini 3 - a new iteration of its protein model was also made available. The Martini 3 protein force field is an evolution of its Martini 2 counterpart, aimed at improving many of the shortcomings that had been previously identified. In this mini-review, we first provide a general overview of the model and then focus on the successful advances made in the short time since its release, many of which would not have been possible before. Furthermore, we discuss reported limitations, potential directions for model improvement and comment on what the likely future development and application avenues are.
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Affiliation(s)
- Luís Borges-Araújo
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France; Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France
| | - Gilberto P Pereira
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France; Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France
| | - Mariana Valério
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France; Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France
| | - Paulo C T Souza
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France; Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France.
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18
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Bittrich S, Midlik A, Varadi M, Velankar S, Burley SK, Young JY, Sehnal D, Vallat B. Describing and Sharing Molecular Visualizations Using the MolViewSpec Toolkit. Curr Protoc 2024; 4:e1099. [PMID: 39024028 PMCID: PMC11338654 DOI: 10.1002/cpz1.1099] [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] [Indexed: 07/20/2024]
Abstract
With the ever-expanding toolkit of molecular viewers, the ability to visualize macromolecular structures has never been more accessible. Yet, the idiosyncratic technical intricacies across tools and the integration complexities associated with handling structure annotation data present significant barriers to seamless interoperability and steep learning curves for many users. The necessity for reproducible data visualizations is at the forefront of the current challenges. Recently, we introduced MolViewSpec (homepage: https://molstar.org/mol-view-spec/, GitHub project: https://github.com/molstar/mol-view-spec), a specification approach that defines molecular visualizations, decoupling them from the varying implementation details of different molecular viewers. Through the protocols presented herein, we demonstrate how to use MolViewSpec and its 3D view-building Python library for creating sophisticated, customized 3D views covering all standard molecular visualizations. MolViewSpec supports representations like cartoon and ball-and-stick with coloring, labeling, and applying complex transformations such as superposition to any macromolecular structure file in mmCIF, BinaryCIF, and PDB formats. These examples showcase progress towards reusability and interoperability of molecular 3D visualization in an era when handling molecular structures at scale is a timely and pressing matter in structural bioinformatics as well as research and education across the life sciences. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating a MolViewSpec view using the MolViewSpec Python package Basic Protocol 2: Creating a MolViewSpec view with reference to MolViewSpec annotation files Basic Protocol 3: Creating a MolViewSpec view with labels and other advanced features Support Protocol 1: Computing rotation and translation vectors Support Protocol 2: Creating a MolViewSpec annotation file.
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Affiliation(s)
- Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California
- These authors contributed equally to this work
| | - Adam Midlik
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom
- These authors contributed equally to this work
| | - Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom
| | - Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - David Sehnal
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey
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19
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Jiao Z, He Y, Fu X, Zhang X, Geng Z, Ding W. A predicted model-aided reconstruction algorithm for X-ray free-electron laser single-particle imaging. IUCRJ 2024; 11:602-619. [PMID: 38904548 PMCID: PMC11220885 DOI: 10.1107/s2052252524004858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 05/23/2024] [Indexed: 06/22/2024]
Abstract
Ultra-intense, ultra-fast X-ray free-electron lasers (XFELs) enable the imaging of single protein molecules under ambient temperature and pressure. A crucial aspect of structure reconstruction involves determining the relative orientations of each diffraction pattern and recovering the missing phase information. In this paper, we introduce a predicted model-aided algorithm for orientation determination and phase retrieval, which has been tested on various simulated datasets and has shown significant improvements in the success rate, accuracy and efficiency of XFEL data reconstruction.
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Affiliation(s)
- Zhichao Jiao
- Laboratory of Soft Matter PhysicsInstitute of Physics, Chinese Academy of SciencesBeijing100190People’s Republic of China
- University of Chinese Academy of SciencesBeijing100049People’s Republic of China
| | - Yao He
- Research Instrument ScientistNew York University Abu DhabiAbu DhabiUnited Arab Emirates
| | - Xingke Fu
- Laboratory of Soft Matter PhysicsInstitute of Physics, Chinese Academy of SciencesBeijing100190People’s Republic of China
- University of Chinese Academy of SciencesBeijing100049People’s Republic of China
| | - Xin Zhang
- The University of Hong KongHong Kong SARPeople’s Republic of China
| | - Zhi Geng
- Beijing Synchrotron Radiation FacilityInstitute of High Energy Physics, Chinese Academy of SciencesBeijing100049People’s Republic of China
- University of Chinese Academy of SciencesBeijing100049People’s Republic of China
| | - Wei Ding
- Laboratory of Soft Matter PhysicsInstitute of Physics, Chinese Academy of SciencesBeijing100190People’s Republic of China
- University of Chinese Academy of SciencesBeijing100049People’s Republic of China
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20
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Bragulat-Teixidor H, Ishihara K, Szücs GM, Otsuka S. The endoplasmic reticulum connects to the nucleus by constricted junctions that mature after mitosis. EMBO Rep 2024; 25:3137-3159. [PMID: 38877171 PMCID: PMC11239909 DOI: 10.1038/s44319-024-00175-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 05/27/2024] [Accepted: 06/03/2024] [Indexed: 06/16/2024] Open
Abstract
Junctions between the endoplasmic reticulum (ER) and the outer membrane of the nuclear envelope (NE) physically connect both organelles. These ER-NE junctions are essential for supplying the NE with lipids and proteins synthesized in the ER. However, little is known about the structure of these ER-NE junctions. Here, we systematically study the ultrastructure of ER-NE junctions in cryo-fixed mammalian cells staged in anaphase, telophase, and interphase by correlating live cell imaging with three-dimensional electron microscopy. Our results show that ER-NE junctions in interphase cells have a pronounced hourglass shape with a constricted neck of 7-20 nm width. This morphology is significantly distinct from that of junctions within the ER network, and their morphology emerges as early as telophase. The highly constricted ER-NE junctions are seen in several mammalian cell types, but not in budding yeast. We speculate that the unique and highly constricted ER-NE junctions are regulated via novel mechanisms that contribute to ER-to-NE lipid and protein traffic in higher eukaryotes.
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Affiliation(s)
- Helena Bragulat-Teixidor
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna, Austria.
- Medical University of Vienna, Max Perutz Labs, Vienna, Austria.
- Vienna BioCenter PhD Program, a Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria.
| | - Keisuke Ishihara
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gréta Martina Szücs
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna, Austria
- Medical University of Vienna, Max Perutz Labs, Vienna, Austria
| | - Shotaro Otsuka
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna, Austria.
- Medical University of Vienna, Max Perutz Labs, Vienna, Austria.
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21
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Lyu J, Kapolka N, Gumpper R, Alon A, Wang L, Jain MK, Barros-Álvarez X, Sakamoto K, Kim Y, DiBerto J, Kim K, Glenn IS, Tummino TA, Huang S, Irwin JJ, Tarkhanova OO, Moroz Y, Skiniotis G, Kruse AC, Shoichet BK, Roth BL. AlphaFold2 structures guide prospective ligand discovery. Science 2024; 384:eadn6354. [PMID: 38753765 PMCID: PMC11253030 DOI: 10.1126/science.adn6354] [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: 12/19/2023] [Accepted: 04/24/2024] [Indexed: 05/18/2024]
Abstract
AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.
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Affiliation(s)
- Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
- The Evnin Family Laboratory of Computational Molecular Discovery, The Rockefeller University, New York, NY 10065, USA
| | - Nicholas Kapolka
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Ryan Gumpper
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Assaf Alon
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Liang Wang
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Manish K. Jain
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Kensuke Sakamoto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Yoojoong Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Jeffrey DiBerto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Kuglae Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Isabella S. Glenn
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Tia A. Tummino
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Sijie Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - John J. Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Yurii Moroz
- Chemspace LLC, Kyiv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine
- Enamine Ltd., Kyiv 02094, Ukraine
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Andrew C. Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Brian K. Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Bryan L. Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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22
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Ji Z, Pandey T, de Belly H, Wang B, Weiner OD, Tang Y, Guang S, Goddard TD, Ma DK. ER-GUARD: an evolutionarily conserved antioxidant defense system at ER membranes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.19.599784. [PMID: 38948723 PMCID: PMC11212984 DOI: 10.1101/2024.06.19.599784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Oxidative protein folding in the endoplasmic reticulum (ER) is essential for all eukaryotic cells yet generates hydrogen peroxide (H2O2), a reactive oxygen species (ROS). The ER-transmembrane protein that provides reducing equivalents to ER and guards the cytosol for antioxidant defense remains unidentified. Here we combine AlphaFold2-based and functional reporter screens in C. elegans to identify a previously uncharacterized and evolutionarily conserved protein ERGU-1 that fulfills these roles. Deleting C. elegans ERGU-1 causes excessive H2O2 and transcriptional gene up-regulation through SKN-1, homolog of mammalian antioxidant master regulator NRF2. ERGU-1 deficiency also impairs organismal reproduction and behaviors. Both C. elegans and human ERGU-1 proteins localize to ER membranes and form network reticulum structures. We name this system ER-GUARD, Endoplasmic Reticulum Guardian Aegis of Redox Defense. Human and Drosophila homologs of ERGU-1 can rescue C. elegans mutant phenotypes, demonstrating evolutionarily ancient and conserved functions. Together, our results reveal an ER-membrane-specific protein machinery and defense-net system ER-GUARD for peroxide detoxification and suggest a previously unknown but conserved pathway for antioxidant defense in animal cells.
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Affiliation(s)
- Zhijian Ji
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, USA
| | - Taruna Pandey
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, USA
| | - Henry de Belly
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, USA
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, USA
| | - Bingying Wang
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, USA
| | - Orion D. Weiner
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, USA
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, USA
| | - Yao Tang
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Shouhong Guang
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Thomas D. Goddard
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA
| | - Dengke K. Ma
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, USA
- Department of Physiology, University of California, San Francisco, San Francisco, California, USA
- Innovative Genomics Institute, University of California, Berkeley, California, USA
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23
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Derks J, Jonson T, Leduc A, Khan S, Khoury L, Rafiee MR, Slavov N. Single-nucleus proteomics identifies regulators of protein transport. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.17.599449. [PMID: 38948785 PMCID: PMC11212961 DOI: 10.1101/2024.06.17.599449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The physiological response of a cell to stimulation depends on its proteome configuration. Therefore, the abundance variation of regulatory proteins across unstimulated single cells can be associatively linked with their response to stimulation. Here we developed an approach that leverages this association across individual cells and nuclei to systematically identify potential regulators of biological processes, followed by targeted validation. Specifically, we applied this approach to identify regulators of nucleocytoplasmic protein transport in macrophages stimulated with lipopolysaccharide (LPS). To this end, we quantified the proteomes of 3,412 individual nuclei, sampling the dynamic response to LPS treatment, and linking functional variability to proteomic variability. Minutes after the stimulation, the protein transport in individual nuclei correlated strongly with the abundance of known protein transport regulators, thus revealing the impact of natural protein variability on functional cellular response. We found that simple biophysical constraints, such as the quantity of nuclear pores, partially explain the variability in LPS-induced nucleocytoplasmic transport. Among the many proteins newly identified to be associated with the response, we selected 16 for targeted validation by knockdown. The knockdown phenotypes confirmed the inferences derived from natural protein and functional variation of single nuclei, thus demonstrating the potential of (sub-)single-cell proteomics to infer functional regulation. We expect this approach to generalize to broad applications and enhance the functional interpretability of single-cell omics data.
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Affiliation(s)
- Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
| | - Tobias Jonson
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
| | - Saad Khan
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
| | - Mahmoud-Reza Rafiee
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
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24
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Mayorga LS, Masone D. The Secret Ballet Inside Multivesicular Bodies. ACS NANO 2024; 18:15651-15660. [PMID: 38830824 DOI: 10.1021/acsnano.4c01590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Lipid bilayers possess the capacity for self-assembly due to the amphipathic nature of lipid molecules, which have both hydrophobic and hydrophilic regions. When confined, lipid bilayers exhibit astonishing versatility in their forms, adopting diverse shapes that are challenging to observe through experimental means. Exploiting this adaptability, lipid structures motivate the development of bio-inspired mechanomaterials and integrated nanobio-interfaces that could seamlessly merge with biological entities, ultimately bridging the gap between synthetic and biological systems. In this work, we demonstrate how, in numerical simulations of multivesicular bodies, a fascinating evolution unfolds from an initial semblance of order toward states of higher entropy over time. We observe dynamic rearrangements in confined vesicles that reveal unexpected limit shapes of distinct geometric patterns. We identify five structures as the basic building blocks that systematically repeat under various conditions of size and composition. Moreover, we observe more complex and less frequent shapes that emerge in confined spaces. Our results provide insights into the dynamics of multivesicular systems, offering a richer understanding of how confined lipid bodies spontaneously self-organize.
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Affiliation(s)
- Luis S Mayorga
- Instituto de Histología y Embriología de Mendoza (IHEM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCuyo), 5500 Mendoza, Argentina
- Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Cuyo (UNCuyo), 5500, Mendoza, Argentina
| | - Diego Masone
- Instituto de Histología y Embriología de Mendoza (IHEM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCuyo), 5500 Mendoza, Argentina
- Facultad de Ingeniería, Universidad Nacional de Cuyo (UNCuyo), 5500 Mendoza, Argentina
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25
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Khakwani MMAK, Ji XY, Khattak S, Sun YC, Yao K, Zhang L. Targeting colorectal cancer at the level of nuclear pore complex. J Adv Res 2024:S2090-1232(24)00245-5. [PMID: 38876192 DOI: 10.1016/j.jare.2024.06.009] [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: 03/13/2024] [Revised: 05/23/2024] [Accepted: 06/07/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Nuclear pore complexes (NPCs) are the architectures entrenched in nuclear envelop of a cell that regulate the nucleo-cytoplasmic transportation of materials, such as proteins and RNAs for proper functioning of a cell. The appropriate localization of proteins and RNAs within the cell is essential for its normal functionality. For such a complex transportation of materials across the NPC, around 60 proteins are involved comprising nucleoporins, karyopherins and RAN system proteins that play a vital role in NPC's structure formation, cargo translocation across NPC, and cargoes' rapid directed transportation respectively. In various cancers, the structure and function of NPC is often exaggerated, following altered expressions of its nucleoporins and karyopherins, affecting other proteins of associated signaling pathways. Some inhibitors of karyopherins at present, have potential to regulate the altered level/expression of these karyopherin molecules. AIM OF REVIEW This review summarizes the data from 1990 to 2023, mainly focusing on recent studies that illustrate the structure and function of NPC, the relationship and mechanisms of nucleoporins and karyopherins with colorectal cancer, as well as therapeutic values, in order to understand the pathology and underlying basis of colorectal cancer associated with NPC. This is the first review to our knowledge elucidating the detailed updated studies targeting colorectal cancer at NPC. The review also aims to target certain karyopherins, Nups and their possible inhibitors and activators molecules as a therapeutic strategy. KEY SCIENTIFIC CONCEPTS OF REVIEW NPC structure provides understanding, how nucleoporins and karyopherins as key molecules are responsible for appropriate nucleocytoplasmic transportation. Many studies provide evidences, describing the role of disrupted nucleoporins and karyopherins not only in CRC but also in other non-hematological and hematological malignancies. At present, some inhibitors of karyopherins have therapeutic potential for CRC, however development of more potent inhibitors may provide more effective therapeutic strategies for CRC in near future.
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Affiliation(s)
- Muhammad Mahtab Aslam Khan Khakwani
- Department of General Surgery, Huaihe Hospital of Henan University, Henan University, Kaifeng 475004, China; Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medicine, Henan University, Kaifeng, Henan 475004, China
| | - Xin-Ying Ji
- Department of Oncology, Huaxian County Hospital, Huaxian, Henan Province 456400, China; Faculty of Basic Medical Subjects, Shu-Qing Medical College of Zhengzhou, Gong-Ming Rd, Mazhai Town, Erqi District, Zhengzhou, Henan 450064, China
| | - Saadullah Khattak
- Department of General Surgery, Huaihe Hospital of Henan University, Henan University, Kaifeng 475004, China
| | - Ying-Chuan Sun
- Department of Internal Oncology (Section I), Xuchang Municipal Central Hospital, Xuchang, Henan 430000, China
| | - Kunhou Yao
- Department of General Surgery, Huaihe Hospital of Henan University, Henan University, Kaifeng 475004, China.
| | - Lei Zhang
- Department of General Surgery, Huaihe Hospital of Henan University, Henan University, Kaifeng 475004, China; Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medicine, Henan University, Kaifeng, Henan 475004, China.
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26
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Gauger M, Heinz M, Halbritter ALJ, Stelzl LS, Erlenbach N, Hummer G, Sigurdsson ST, Prisner TF. Structure and Internal Dynamics of Short RNA Duplexes Determined by a Combination of Pulsed EPR Methods and MD Simulations. Angew Chem Int Ed Engl 2024; 63:e202402498. [PMID: 38530284 DOI: 10.1002/anie.202402498] [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: 02/03/2024] [Revised: 03/24/2024] [Accepted: 03/25/2024] [Indexed: 03/27/2024]
Abstract
We used EPR spectroscopy to characterize the structure of RNA duplexes and their internal twist, stretch and bending motions. We prepared eight 20-base-pair-long RNA duplexes containing the rigid spin-label Çm, a cytidine analogue, at two positions and acquired orientation-selective PELDOR/DEER data. By using different frequency bands (X-, Q-, G-band), detailed information about the distance and orientation of the labels was obtained and provided insights into the global conformational dynamics of the RNA duplex. We used 19F Mims ENDOR experiments on three singly Çm- and singly fluorine-labeled RNA duplexes to determine the exact position of the Çm spin label in the helix. In a quantitative comparison to MD simulations of RNA with and without Çm spin labels, we found that state-of-the-art force fields with explicit parameterization of the spin label were able to describe the conformational ensemble present in our experiments. The MD simulations further confirmed that the Çm spin labels are excellent mimics of cytidine inducing only small local changes in the RNA structure. Çm spin labels are thus ideally suited for high-precision EPR experiments to probe the structure and, in conjunction with MD simulations, motions of RNA.
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Affiliation(s)
- Maximilian Gauger
- Institute of Physical and Theoretical Chemistry and Center of Biomolecular Magnetic Resonance, Goethe University Frankfurt, Max-von-Laue Str. 7, 60438, Frankfurt am Main, Germany
| | - Marcel Heinz
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438, Frankfurt am Main, Germany
| | | | - Lukas S Stelzl
- Faculty of Biology, Johannes Gutenberg University, 55128, Mainz, Germany
- KOMET 1, Institute of Physics, Johannes Gutenberg University, Staudingerweg 9, 55128, Mainz, Germany
- Institute of Quantitative and Computational Bioscience (IQCB), Johannes Gutenberg University Mainz, 55128, Mainz, Germany
- Institute of Molecular Biology (IMB), 55128, Mainz, Germany
| | - Nicole Erlenbach
- Institute of Physical and Theoretical Chemistry and Center of Biomolecular Magnetic Resonance, Goethe University Frankfurt, Max-von-Laue Str. 7, 60438, Frankfurt am Main, Germany
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438, Frankfurt am Main, Germany
- Institute of Biophysics, Goethe University Frankfurt, Max-von-Laue Str. 1, 60438, Frankfurt am Main, Germany
| | | | - Thomas F Prisner
- Institute of Physical and Theoretical Chemistry and Center of Biomolecular Magnetic Resonance, Goethe University Frankfurt, Max-von-Laue Str. 7, 60438, Frankfurt am Main, Germany
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27
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Wang L, Wen Z, Liu SW, Zhang L, Finley C, Lee HJ, Fan HJS. Overview of AlphaFold2 and breakthroughs in overcoming its limitations. Comput Biol Med 2024; 176:108620. [PMID: 38761500 DOI: 10.1016/j.compbiomed.2024.108620] [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: 10/29/2023] [Revised: 05/01/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024]
Abstract
Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.
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Affiliation(s)
- Lei Wang
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Zehua Wen
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Shi-Wei Liu
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Lihong Zhang
- Digestive Department, Binhai New Area Hospital of TCM Tianjin, Tianjin, 300451, China
| | - Cierra Finley
- Department of Natural Sciences, Southwest Tennessee Community College, Memphis, TN, 38015, USA
| | - Ho-Jin Lee
- Department of Natural Sciences, Southwest Tennessee Community College, Memphis, TN, 38015, USA; Division of Natural & Mathematical Sciences, LeMoyne-Own College, Memphis, TN, 38126, USA.
| | - Hua-Jun Shawn Fan
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China.
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28
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Solà Colom M, Fu Z, Gunkel P, Güttler T, Trakhanov S, Srinivasan V, Gregor K, Pleiner T, Görlich D. A checkpoint function for Nup98 in nuclear pore formation suggested by novel inhibitory nanobodies. EMBO J 2024; 43:2198-2232. [PMID: 38649536 PMCID: PMC11148069 DOI: 10.1038/s44318-024-00081-w] [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: 09/12/2023] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/25/2024] Open
Abstract
Nuclear pore complex (NPC) biogenesis is a still enigmatic example of protein self-assembly. We now introduce several cross-reacting anti-Nup nanobodies for imaging intact nuclear pore complexes from frog to human. We also report a simplified assay that directly tracks postmitotic NPC assembly with added fluorophore-labeled anti-Nup nanobodies. During interphase, NPCs are inserted into a pre-existing nuclear envelope. Monitoring this process is challenging because newly assembled NPCs are indistinguishable from pre-existing ones. We overcame this problem by inserting Xenopus-derived NPCs into human nuclear envelopes and using frog-specific anti-Nup nanobodies for detection. We further asked whether anti-Nup nanobodies could serve as NPC assembly inhibitors. Using a selection strategy against conserved epitopes, we obtained anti-Nup93, Nup98, and Nup155 nanobodies that block Nup-Nup interfaces and arrest NPC assembly. We solved structures of nanobody-target complexes and identified roles for the Nup93 α-solenoid domain in recruiting Nup358 and the Nup214·88·62 complex, as well as for Nup155 and the Nup98 autoproteolytic domain in NPC scaffold assembly. The latter suggests a checkpoint linking pore formation to the assembly of the Nup98-dominated permeability barrier.
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Affiliation(s)
- Mireia Solà Colom
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- AI Proteins, 20 Overland St., Boston, MA, USA
| | - Zhenglin Fu
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Philip Gunkel
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Thomas Güttler
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Octapharma Biopharmaceuticals, Im Neuenheimer Feld 590, 69120, Heidelberg, Germany
| | - Sergei Trakhanov
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Vasundara Srinivasan
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Department of Chemistry, Institute of Biochemistry and Molecular Biology, Universität Hamburg, Hamburg, Germany
| | - Kathrin Gregor
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Tino Pleiner
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Dirk Görlich
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
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29
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Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, Ronneberger O, Willmore L, Ballard AJ, Bambrick J, Bodenstein SW, Evans DA, Hung CC, O'Neill M, Reiman D, Tunyasuvunakool K, Wu Z, Žemgulytė A, Arvaniti E, Beattie C, Bertolli O, Bridgland A, Cherepanov A, Congreve M, Cowen-Rivers AI, Cowie A, Figurnov M, Fuchs FB, Gladman H, Jain R, Khan YA, Low CMR, Perlin K, Potapenko A, Savy P, Singh S, Stecula A, Thillaisundaram A, Tong C, Yakneen S, Zhong ED, Zielinski M, Žídek A, Bapst V, Kohli P, Jaderberg M, Hassabis D, Jumper JM. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 2024; 630:493-500. [PMID: 38718835 PMCID: PMC11168924 DOI: 10.1038/s41586-024-07487-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/29/2024] [Indexed: 06/13/2024]
Abstract
The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.
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Affiliation(s)
| | - Jonas Adler
- Core Contributor, Google DeepMind, London, UK
| | - Jack Dunger
- Core Contributor, Google DeepMind, London, UK
| | | | - Tim Green
- Core Contributor, Google DeepMind, London, UK
| | | | | | | | | | | | | | | | | | | | | | | | - Zachary Wu
- Core Contributor, Google DeepMind, London, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Yousuf A Khan
- Google DeepMind, London, UK
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA
| | | | | | | | | | | | | | | | | | | | - Ellen D Zhong
- Google DeepMind, London, UK
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | | | | | | | | | | | - Demis Hassabis
- Core Contributor, Google DeepMind, London, UK.
- Core Contributor, Isomorphic Labs, London, UK.
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30
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Panagaki F, Tapia-Rojo R, Zhu T, Milmoe N, Paracuellos P, Board S, Mora M, Walker J, Rostkova E, Stannard A, Infante E, Garcia-Manyes S. Structural anisotropy results in mechano-directional transport of proteins across nuclear pores. NATURE PHYSICS 2024; 20:1180-1193. [PMID: 39036650 PMCID: PMC11254768 DOI: 10.1038/s41567-024-02438-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 02/08/2024] [Indexed: 07/23/2024]
Abstract
The nuclear pore complex regulates nucleocytoplasmic transport by means of a tightly synchronized suite of biochemical reactions. The physicochemical properties of the translocating cargos are emerging as master regulators of their shuttling dynamics. As well as being affected by molecular weight and surface-exposed amino acids, the kinetics of the nuclear translocation of protein cargos also depend on their nanomechanical properties, yet the mechanisms underpinning the mechanoselectivity of the nuclear pore complex are unclear. Here we show that proteins with locally soft regions in the vicinity of the nuclear-localization sequence exhibit higher nuclear-import rates, and that such mechanoselectivity is specifically impaired upon knocking down nucleoporin 153, a key protein in the nuclear pore complex. This allows us to design a short, easy-to-express and chemically inert unstructured peptide tag that accelerates the nuclear-import rate of stiff protein cargos. We also show that U2OS osteosarcoma cells expressing the peptide-tagged myocardin-related transcription factor import this mechanosensitive protein to the nucleus at higher rates and display faster motility. Locally unstructured regions lower the free-energy barrier of protein translocation and might offer a control mechanism for nuclear mechanotransduction.
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Affiliation(s)
- Fani Panagaki
- Single Molecule Mechanobiology Laboratory, The Francis Crick Institute, London, UK
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Rafael Tapia-Rojo
- Single Molecule Mechanobiology Laboratory, The Francis Crick Institute, London, UK
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Tong Zhu
- Single Molecule Mechanobiology Laboratory, The Francis Crick Institute, London, UK
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Natalie Milmoe
- Single Molecule Mechanobiology Laboratory, The Francis Crick Institute, London, UK
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Patricia Paracuellos
- Single Molecule Mechanobiology Laboratory, The Francis Crick Institute, London, UK
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Stephanie Board
- Single Molecule Mechanobiology Laboratory, The Francis Crick Institute, London, UK
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Marc Mora
- Single Molecule Mechanobiology Laboratory, The Francis Crick Institute, London, UK
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Jane Walker
- Single Molecule Mechanobiology Laboratory, The Francis Crick Institute, London, UK
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Elena Rostkova
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Andrew Stannard
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Elvira Infante
- Single Molecule Mechanobiology Laboratory, The Francis Crick Institute, London, UK
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
| | - Sergi Garcia-Manyes
- Single Molecule Mechanobiology Laboratory, The Francis Crick Institute, London, UK
- Department of Physics, Randall Centre for Cell and Molecular Biophysics, Centre for the Physical Science of Life and London Centre for Nanotechnology, King’s College London, London, UK
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31
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Ille AM, Markosian C, Burley SK, Mathews MB, Pasqualini R, Arap W. Generative artificial intelligence performs rudimentary structural biology modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.575113. [PMID: 38293060 PMCID: PMC10827103 DOI: 10.1101/2024.01.10.575113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.
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Affiliation(s)
- Alexander M. Ille
- School of Graduate Studies, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Christopher Markosian
- School of Graduate Studies, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, USA
| | - Michael B. Mathews
- School of Graduate Studies, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
- Division of Infectious Disease, Department of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA
- Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey, USA
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32
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Feng Q, Saladin M, Wu C, Cao E, Zheng W, Zhang A, Bhardwaj P, Li X, Shen Q, Kapinos LE, Mariappan M, Lusk CP, Xiong Y, Lim RYH, Lin C. Channel width modulates the permeability of DNA origami based nuclear pore mimics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.09.593438. [PMID: 38766144 PMCID: PMC11100828 DOI: 10.1101/2024.05.09.593438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Nucleoporins (nups) in the central channel of nuclear pore complexes (NPCs) form a selective barrier that suppresses the diffusion of most macromolecules while enabling rapid transport of nuclear transport receptors (NTRs) with bound cargos. The complex molecular interactions between nups and NTRs have been thought to underlie the gatekeeping function of the NPC. Recent studies have shown considerable variation in NPC diameter but how altering NPC diameter might impact the selective barrier properties remains unclear. Here, we build DNA nanopores with programmable diameters and nup arrangement to mimic NPCs of different diameters. We use hepatitis B virus (HBV) capsids as a model for large-size cargos. We find that Nup62 proteins form a dynamic cross-channel meshwork impermeable to HBV capsids when grafted on the interior of 60-nm wide nanopores but not in 79-nm pores, where Nup62 cluster locally. Furthermore, importing substantially changes the dynamics of Nup62 assemblies and facilitates the passage of HBV capsids through NPC mimics containing Nup62 and Nup153. Our study shows the transport channel width is critical to the permeability of nup barriers and underscores the role of NTRs in dynamically remodeling nup assemblies and mediating the nuclear entry of viruses.
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Affiliation(s)
- Qingzhou Feng
- Department of Cell Biology, Yale School of Medicine, USA
- Nanobiology Institute, Yale University, USA
| | | | - Chunxiang Wu
- Department of Biophysics and Biochemistry, Yale University, USA
| | - Eason Cao
- Department of Cell Biology, Yale School of Medicine, USA
- Nanobiology Institute, Yale University, USA
| | - Wei Zheng
- Department of Biophysics and Biochemistry, Yale University, USA
| | - Amy Zhang
- Department of Cell Biology, Yale School of Medicine, USA
- Nanobiology Institute, Yale University, USA
| | | | - Xia Li
- Department of Cell Biology, Yale School of Medicine, USA
| | - Qi Shen
- Department of Cell Biology, Yale School of Medicine, USA
- Nanobiology Institute, Yale University, USA
- Department of Biophysics and Biochemistry, Yale University, USA
| | | | - Malaiyalam Mariappan
- Department of Cell Biology, Yale School of Medicine, USA
- Nanobiology Institute, Yale University, USA
| | | | - Yong Xiong
- Department of Biophysics and Biochemistry, Yale University, USA
| | - Roderick Y. H. Lim
- Biozentrum, University of Basel, Switzerland
- Swiss Nanoscience Institute, University of Basel, Switzerland
| | - Chenxiang Lin
- Department of Cell Biology, Yale School of Medicine, USA
- Nanobiology Institute, Yale University, USA
- Department of Biomedical Engineering, Yale University, USA
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33
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Cruz-León S, Majtner T, Hoffmann PC, Kreysing JP, Kehl S, Tuijtel MW, Schaefer SL, Geißler K, Beck M, Turoňová B, Hummer G. High-confidence 3D template matching for cryo-electron tomography. Nat Commun 2024; 15:3992. [PMID: 38734767 PMCID: PMC11088655 DOI: 10.1038/s41467-024-47839-8] [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: 10/31/2023] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
Visual proteomics attempts to build atlases of the molecular content of cells but the automated annotation of cryo electron tomograms remains challenging. Template matching (TM) and methods based on machine learning detect structural signatures of macromolecules. However, their applicability remains limited in terms of both the abundance and size of the molecular targets. Here we show that the performance of TM is greatly improved by using template-specific search parameter optimization and by including higher-resolution information. We establish a TM pipeline with systematically tuned parameters for the automated, objective and comprehensive identification of structures with confidence 10 to 100-fold above the noise level. We demonstrate high-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, fatty acid synthases, lipid membranes and microtubules, and individual subunits inside crowded eukaryotic cells. We provide software tools for the generic implementation of our method that is broadly applicable towards realizing visual proteomics.
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Affiliation(s)
- Sergio Cruz-León
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany
| | - Tomáš Majtner
- Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany
| | - Patrick C Hoffmann
- Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany
| | - Jan Philipp Kreysing
- Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany
- IMPRS on Cellular Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany
| | - Sebastian Kehl
- Max Planck Computing and Data Facility, Gießenbachstraße 2, 85748, Garching, Germany
| | - Maarten W Tuijtel
- Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany
| | - Stefan L Schaefer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany
| | - Katharina Geißler
- Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany
- IMPRS on Cellular Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany
| | - Martin Beck
- Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany.
- Institute of Biochemistry, Goethe University Frankfurt, 60438, Frankfurt am Main, Germany.
| | - Beata Turoňová
- Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany.
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany.
- Institute of Biophysics, Goethe University Frankfurt, 60438, Frankfurt am Main, Germany.
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34
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Ibáñez de Opakua A, Pantoja CF, Cima-Omori MS, Dienemann C, Zweckstetter M. Impact of distinct FG nucleoporin repeats on Nup98 self-association. Nat Commun 2024; 15:3797. [PMID: 38714656 PMCID: PMC11076500 DOI: 10.1038/s41467-024-48194-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 04/24/2024] [Indexed: 05/10/2024] Open
Abstract
Nucleoporins rich in phenylalanine/glycine (FG) residues form the permeability barrier within the nuclear pore complex and are implicated in several pathological cellular processes, including oncogenic fusion condensates. The self-association of FG-repeat proteins and interactions between FG-repeats play a critical role in these activities by forming hydrogel-like structures. Here we show that mutation of specific FG repeats of Nup98 can strongly decrease the protein's self-association capabilities. We further present a cryo-electron microscopy structure of a Nup98 peptide fibril with higher stability per residue compared with previous Nup98 fibril structures. The high-resolution structure reveals zipper-like hydrophobic patches which contain a GLFG motif and are less compatible for binding to nuclear transport receptors. The identified distinct molecular properties of different regions of the nucleoporin may contribute to spatial variations in the self-association of FG-repeats, potentially influencing transport processes through the nuclear pore.
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Affiliation(s)
- Alain Ibáñez de Opakua
- German Center for Neurodegenerative Diseases (DZNE), Von-Siebold-Str. 3a, Göttingen, Germany
| | - Christian F Pantoja
- German Center for Neurodegenerative Diseases (DZNE), Von-Siebold-Str. 3a, Göttingen, Germany
| | - Maria-Sol Cima-Omori
- German Center for Neurodegenerative Diseases (DZNE), Von-Siebold-Str. 3a, Göttingen, Germany
| | - Christian Dienemann
- Max Planck Institute for Multidisciplinary Sciences, Department of Molecular Biology, Am Fassberg 11, Göttingen, Germany
| | - Markus Zweckstetter
- German Center for Neurodegenerative Diseases (DZNE), Von-Siebold-Str. 3a, Göttingen, Germany.
- Max Planck Institute for Multidisciplinary Sciences, Department of NMR-based Structural Biology, Am Fassberg 11, Göttingen, Germany.
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35
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Bergeron JJM. Proteomics Impact on Cell Biology to Resolve Cell Structure and Function. Mol Cell Proteomics 2024; 23:100758. [PMID: 38574860 PMCID: PMC11070594 DOI: 10.1016/j.mcpro.2024.100758] [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: 02/07/2024] [Revised: 03/23/2024] [Accepted: 03/26/2024] [Indexed: 04/06/2024] Open
Abstract
The acceleration of advances in proteomics has enabled integration with imaging at the EM and light microscopy levels, cryo-EM of protein structures, and artificial intelligence with proteins comprehensively and accurately resolved for cell structures at nanometer to subnanometer resolution. Proteomics continues to outpace experimentally based structural imaging, but their ultimate integration is a path toward the goal of a compendium of all proteins to understand mechanistically cell structure and function.
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Affiliation(s)
- John J M Bergeron
- Department of Medicine, McGill University Hospital Research Institute, Montreal, Quebec, Canada.
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36
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Makio T, Zhang K, Love N, Mast FD, Liu X, Elaish M, Hobman T, Aitchison JD, Fontoura BMA, Wozniak RW. SARS-CoV-2 Orf6 is positioned in the nuclear pore complex by Rae1 to inhibit nucleocytoplasmic transport. Mol Biol Cell 2024; 35:ar62. [PMID: 38507240 PMCID: PMC11151100 DOI: 10.1091/mbc.e23-10-0386] [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: 10/10/2023] [Revised: 03/07/2024] [Accepted: 03/15/2024] [Indexed: 03/22/2024] Open
Abstract
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) accessory protein Orf6 works as an interferon antagonist, in part, by inhibiting the nuclear import activated p-STAT1, an activator of interferon-stimulated genes, and the export of the poly(A) RNA. Insight into the transport regulatory function of Orf6 has come from the observation that Orf6 binds to the nuclear pore complex (NPC) components: Rae1 and Nup98. To gain further insight into the mechanism of Orf6-mediated transport inhibition, we examined the role of Rae1 and Nup98. We show that Rae1 alone is not necessary to support p-STAT1 import or nuclear export of poly(A) RNA. Moreover, the loss of Rae1 suppresses the transport inhibitory activity of Orf6. We propose that the Rae1/Nup98 complex strategically positions Orf6 within the NPC where it alters FG-Nup interactions and their ability to support nuclear transport. In addition, we show that Rae1 is required for normal viral protein production during SARS-CoV-2 infection presumably through its role in supporting Orf6 function.
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Affiliation(s)
- Tadashi Makio
- Department of Cell Biology and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB, Canada T6G 2H7
| | - Ke Zhang
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75235
- Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai 200031, China
| | - Nicole Love
- Department of Cell Biology and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB, Canada T6G 2H7
| | - Fred D. Mast
- Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA 98101
| | - Xue Liu
- Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mohamed Elaish
- Department of Cell Biology and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB, Canada T6G 2H7
| | - Tom Hobman
- Department of Cell Biology and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB, Canada T6G 2H7
| | - John D. Aitchison
- Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA 98101
- Department of Pediatrics, University of Washington, Seattle, WA 98195
- Department of Biochemistry, University of Washington, Seattle, WA 98195
| | - Beatriz M. A. Fontoura
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75235
| | - Richard W. Wozniak
- Department of Cell Biology and Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB, Canada T6G 2H7
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37
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Lam JH, Nakano A, Katritch V. Scalable computation of anisotropic vibrations for large macromolecular assemblies. Nat Commun 2024; 15:3479. [PMID: 38658556 PMCID: PMC11043083 DOI: 10.1038/s41467-024-47685-8] [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: 08/31/2023] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
The Normal Mode Analysis (NMA) is a standard approach to elucidate the anisotropic vibrations of macromolecules at their folded states, where low-frequency collective motions can reveal rearrangements of domains and changes in the exposed surface of macromolecules. Recent advances in structural biology have enabled the resolution of megascale macromolecules with millions of atoms. However, the calculation of their vibrational modes remains elusive due to the prohibitive cost associated with constructing and diagonalizing the underlying eigenproblem and the current approaches to NMA are not readily adaptable for efficient parallel computing on graphic processing unit (GPU). Here, we present eigenproblem construction and diagonalization approach that implements level-structure bandwidth-reducing algorithms to transform the sparse computation in NMA to a globally-sparse-yet-locally-dense computation, allowing batched tensor products to be most efficiently executed on GPU. We map, optimize, and compare several low-complexity Krylov-subspace eigensolvers, supplemented by techniques such as Chebyshev filtering, sum decomposition, external explicit deflation and shift-and-inverse, to allow fast GPU-resident calculations. The method allows accurate calculation of the first 1000 vibrational modes of some largest structures in PDB ( > 2.4 million atoms) at least 250 times faster than existing methods.
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Affiliation(s)
- Jordy Homing Lam
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Bridge Institute and Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, University of Southern California, Los Angeles, CA, USA
| | - Aiichiro Nakano
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Department of Physics and Astronomy, University of Southern California, Los Angeles, CA, USA.
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA.
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Bridge Institute and Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
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38
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Nogales E, Mahamid J. Bridging structural and cell biology with cryo-electron microscopy. Nature 2024; 628:47-56. [PMID: 38570716 PMCID: PMC11211576 DOI: 10.1038/s41586-024-07198-2] [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: 07/18/2023] [Accepted: 02/13/2024] [Indexed: 04/05/2024]
Abstract
Most life scientists would agree that understanding how cellular processes work requires structural knowledge about the macromolecules involved. For example, deciphering the double-helical nature of DNA revealed essential aspects of how genetic information is stored, copied and repaired. Yet, being reductionist in nature, structural biology requires the purification of large amounts of macromolecules, often trimmed off larger functional units. The advent of cryogenic electron microscopy (cryo-EM) greatly facilitated the study of large, functional complexes and generally of samples that are hard to express, purify and/or crystallize. Nevertheless, cryo-EM still requires purification and thus visualization outside of the natural context in which macromolecules operate and coexist. Conversely, cell biologists have been imaging cells using a number of fast-evolving techniques that keep expanding their spatial and temporal reach, but always far from the resolution at which chemistry can be understood. Thus, structural and cell biology provide complementary, yet unconnected visions of the inner workings of cells. Here we discuss how the interplay between cryo-EM and cryo-electron tomography, as a connecting bridge to visualize macromolecules in situ, holds great promise to create comprehensive structural depictions of macromolecules as they interact in complex mixtures or, ultimately, inside the cell itself.
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Affiliation(s)
- Eva Nogales
- Molecular and Cell Biology Department, Institute for Quantitative Biomedicine, University of California, Berkeley, CA, USA.
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Howard Hughes Medical Institute, Berkeley, CA, USA.
| | - Julia Mahamid
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
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39
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Zhao J, Yu X, Shentu X, Li D. The application and development of electron microscopy for three-dimensional reconstruction in life science: a review. Cell Tissue Res 2024; 396:1-18. [PMID: 38416172 DOI: 10.1007/s00441-024-03878-7] [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: 10/17/2023] [Accepted: 02/13/2024] [Indexed: 02/29/2024]
Abstract
Imaging technologies have played a pivotal role in advancing biological research by enabling visualization of biological structures and processes. While traditional electron microscopy (EM) produces two-dimensional images, emerging techniques now allow high-resolution three-dimensional (3D) characterization of specimens in situ, meeting growing needs in molecular and cellular biology. Combining transmission electron microscopy (TEM) with serial sectioning inaugurated 3D imaging, attracting biologists seeking to explore cell ultrastructure and driving advancement of 3D EM reconstruction. By comprehensively and precisely rendering internal structure and distribution, 3D TEM reconstruction provides unparalleled ultrastructural insights into cells and molecules, holding tremendous value for elucidating structure-function relationships and broadly propelling structural biology. Here, we first introduce the principle of 3D reconstruction of cells and tissues by classical approaches in TEM and then discuss modern technologies utilizing TEM and on new SEM-based as well as cryo-electron microscope (cryo-EM) techniques. 3D reconstruction techniques from serial sections, electron tomography (ET), and the recent single-particle analysis (SPA) are examined; the focused ion beam scanning electron microscopy (FIB-SEM), the serial block-face scanning electron microscopy (SBF-SEM), and automatic tape-collecting lathe ultramicrotome (ATUM-SEM) for 3D reconstruction of large volumes are discussed. Finally, we review the challenges and development prospects of these technologies in life science. It aims to provide an informative reference for biological researchers.
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Affiliation(s)
- Jingjing Zhao
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China
| | - Xiaoping Yu
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China
| | - Xuping Shentu
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China
| | - Danting Li
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China.
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40
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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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41
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Yang S, Song C. Switching Go̅ -Martini for Investigating Protein Conformational Transitions and Associated Protein-Lipid Interactions. J Chem Theory Comput 2024; 20:2618-2629. [PMID: 38447049 DOI: 10.1021/acs.jctc.3c01222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named Switching Go̅-Martini, to simulate large-scale protein conformational transitions between different states, based on the switching Go̅ method and the CG Martini 3 force field. The method is straightforward and efficient, as demonstrated by the benchmarking applications for multiple protein systems, including glutamine binding protein (GlnBP), adenylate kinase (AdK), and β2-adrenergic receptor (β2AR). Moreover, by employing the Switching Go̅-Martini method, we can not only unveil the conformational transition from the E2Pi-PL state to E1 state of the type 4 P-type ATPase (P4-ATPase) flippase ATP8A1-CDC50 but also provide insights into the intricate details of lipid transport.
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Affiliation(s)
- Song Yang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Chen Song
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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42
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Powell BM, Brant TS, Davis JH, Mosalaganti S. Rapid structural analysis of bacterial ribosomes in situ. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.22.586148. [PMID: 38585831 PMCID: PMC10996489 DOI: 10.1101/2024.03.22.586148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Rapid structural analysis of purified proteins and their complexes has become increasingly common thanks to key methodological advances in cryo-electron microscopy (cryo-EM) and associated data processing software packages. In contrast, analogous structural analysis in cells via cryo-electron tomography (cryo-ET) remains challenging due to critical technical bottlenecks, including low-throughput sample preparation and imaging, and laborious data processing methods. Here, we describe the development of a rapid in situ cryo-ET sample preparation and data analysis workflow that results in the routine determination of sub-nm resolution ribosomal structures. We apply this workflow to E. coli, producing a 5.8 Å structure of the 70S ribosome from cells in less than 10 days, and we expect this workflow will be widely applicable to related bacterial samples.
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Affiliation(s)
- Barrett M. Powell
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Tyler S. Brant
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, 48109
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Joseph H. Davis
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139
- Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Shyamal Mosalaganti
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, 48109
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, Michigan, 48109
- Department of Biophysics, University of Michigan, Ann Arbor, Michigan, 48109
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43
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Nie Y, Zheng Z, Li C, Zhan H, Kou L, Gu Y, Lü C. Resolving the dynamic properties of entangled linear polymers in non-equilibrium coarse grain simulation with a priori scaling factors. NANOSCALE 2024. [PMID: 38494916 DOI: 10.1039/d3nr06185j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The molecular weight of polymers can influence the material properties, but the molecular weight at the experiment level sometimes can be a huge burden for property prediction with full-atomic simulations. The traditional bottom-up coarse grain (CG) simulation can reduce the computation cost. However, the dynamic properties predicted by the CG simulation can deviate from the full-atomic simulation result. Usually, in CG simulations, the diffusion is faster and the viscosity and modulus are much lower. The fast dynamics in CG are usually solved by a posteriori scaling on time, temperature, or potential modifications, which usually have poor transferability to other non-fitted physical properties because of a lack of fundamental physics. In this work, a priori scaling factors were calculated by the loss of degrees of freedom and implemented in the iterative Boltzmann inversion. According to the simulation results on 3 different CG levels at different temperatures and loading rates, such a priori scaling factors can help in reproducing some dynamic properties of polycaprolactone in CG simulation more accurately, such as heat capacity, Young's modulus, and viscosity, while maintaining the accuracy in the structural distribution prediction. The transferability of entropy-enthalpy compensation and a dissipative particle dynamics thermostat is also presented for comparison. The proposed method reveals the huge potential for developing customized CG thermostats and offers a simple way to rebuild multiphysics CG models for polymers with good transferability.
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Affiliation(s)
- Yihan Nie
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
| | - Zhuoqun Zheng
- School of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Chengkai Li
- School of Materials Science and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
| | - Haifei Zhan
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology (QUT), Brisbane QLD 4001, Australia
- Center for Materials Science, Queensland University of Technology (QUT), Brisbane QLD 4001, Australia
| | - Liangzhi Kou
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology (QUT), Brisbane QLD 4001, Australia
- Center for Materials Science, Queensland University of Technology (QUT), Brisbane QLD 4001, Australia
| | - Yuantong Gu
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology (QUT), Brisbane QLD 4001, Australia
- Center for Materials Science, Queensland University of Technology (QUT), Brisbane QLD 4001, Australia
| | - Chaofeng Lü
- Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, China
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
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44
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Lyu J, Kapolka N, Gumpper R, Alon A, Wang L, Jain MK, Barros-Álvarez X, Sakamoto K, Kim Y, DiBerto J, Kim K, Tummino TA, Huang S, Irwin JJ, Tarkhanova OO, Moroz Y, Skiniotis G, Kruse AC, Shoichet BK, Roth BL. AlphaFold2 structures template ligand discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.20.572662. [PMID: 38187536 PMCID: PMC10769324 DOI: 10.1101/2023.12.20.572662] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors. In retrospective docking screens against the σ2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors' experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor. The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure. To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.
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Affiliation(s)
- Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
- The Evnin Family Laboratory of Computational Molecular Discovery, The Rockefeller University, New York, NY 10065, USA (present address)
| | - Nicholas Kapolka
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Ryan Gumpper
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Assaf Alon
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Pharmacology Department, Yale School of Medicine, New Haven, CT 06510, USA (present address)
| | - Liang Wang
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Manish K Jain
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kensuke Sakamoto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Yoojoong Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Jeffrey DiBerto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Kuglae Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon 21983, Korea (present address)
| | - Tia A Tummino
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Sijie Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Yurii Moroz
- Chemspace LLC, Kyiv, 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv, 01601, Ukraine
- Enamine Ltd., Kyiv, 02094, Ukraine
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, US
| | - Andrew C Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7360, USA
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45
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Beck TL, Carloni P, Asthagiri DN. All-Atom Biomolecular Simulation in the Exascale Era. J Chem Theory Comput 2024; 20:1777-1782. [PMID: 38382017 DOI: 10.1021/acs.jctc.3c01276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.
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Affiliation(s)
- Thomas L Beck
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Paolo Carloni
- INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany
- Department of Physics, RWTH Aachen University, D-52078 Aachen, Germany
| | - Dilipkumar N Asthagiri
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
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46
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Jänes J, Beltrao P. Deep learning for protein structure prediction and design-progress and applications. Mol Syst Biol 2024; 20:162-169. [PMID: 38291232 PMCID: PMC10912668 DOI: 10.1038/s44320-024-00016-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 12/21/2023] [Accepted: 01/11/2024] [Indexed: 02/01/2024] Open
Abstract
Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underlie biological processes in living cells and can have practical applications in the study of disease mutations or the discovery of novel drug treatments. Here, we review the progress made in sequence-based prediction of protein structures with a focus on applications that go beyond the prediction of single monomer structures. This includes the application of deep learning methods for the prediction of structures of protein complexes, different conformations, the evolution of protein structures and the application of these methods to protein design. These developments create new opportunities for research that will have impact across many areas of biomedical research.
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Affiliation(s)
- Jürgen Jänes
- Institute of Molecular Systems Biology, ETH Zürich, 8093, Zürich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pedro Beltrao
- Institute of Molecular Systems Biology, ETH Zürich, 8093, Zürich, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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47
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McCafferty CL, Klumpe S, Amaro RE, Kukulski W, Collinson L, Engel BD. Integrating cellular electron microscopy with multimodal data to explore biology across space and time. Cell 2024; 187:563-584. [PMID: 38306982 DOI: 10.1016/j.cell.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.
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Affiliation(s)
| | - Sven Klumpe
- Research Group CryoEM Technology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.
| | - Rommie E Amaro
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Wanda Kukulski
- Institute of Biochemistry and Molecular Medicine, University of Bern, Bühlstrasse 28, 3012 Bern, Switzerland.
| | - Lucy Collinson
- Electron Microscopy Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.
| | - Benjamin D Engel
- Biozentrum, University of Basel, Spitalstrasse 41, 4056 Basel, Switzerland.
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48
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Palao L, Murakami K, Chang YW. Combining per-particle cryo-ET and cryo-EM single particle analysis to elucidate heterogeneous DNA-protein organization. Curr Opin Struct Biol 2024; 84:102765. [PMID: 38181688 PMCID: PMC10922635 DOI: 10.1016/j.sbi.2023.102765] [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: 10/01/2023] [Revised: 12/06/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024]
Abstract
Cryo-electron microscopy single particle analysis (cryo-EM SPA) and cryo-electron tomography (cryo-ET) have historically been employed as distinct approaches for investigating molecular structures of disparate sample types, focusing on highly purified biological macromolecules and in situ cellular contexts, respectively. However, these techniques offer inherently complementary structural insights that, when combined, provide a more comprehensive understanding of complex biological systems. For example, if both techniques are applied to the same purified biological macromolecules, cryo-ET has the ability to resolve highly flexible yet strong signal features on an individual target molecule which will not be preserved in the high-resolution cryo-EM SPA results. In this review, we highlight recent achievements utilizing such applications to unveil new insights into the chromatin assembly and activities of DNA-protein assemblies. This convergence of cryo-EM SPA and cryo-ET holds great promise for elucidating new structural aspects of these essential molecular processes.
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Affiliation(s)
- Leon Palao
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, PA, USA; Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Kenji Murakami
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, PA, USA; Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, PA, USA; Institute of Structural Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Yi-Wei Chang
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, PA, USA; Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, PA, USA; Institute of Structural Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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49
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Fu L, Weiskopf EN, Akkermans O, Swanson NA, Cheng S, Schwartz TU, Görlich D. HIV-1 capsids enter the FG phase of nuclear pores like a transport receptor. Nature 2024; 626:843-851. [PMID: 38267583 PMCID: PMC10881386 DOI: 10.1038/s41586-023-06966-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 12/13/2023] [Indexed: 01/26/2024]
Abstract
HIV-1 infection requires nuclear entry of the viral genome. Previous evidence suggests that this entry proceeds through nuclear pore complexes (NPCs), with the 120 × 60 nm capsid squeezing through an approximately 60-nm-wide central channel1 and crossing the permeability barrier of the NPC. This barrier can be described as an FG phase2 that is assembled from cohesively interacting phenylalanine-glycine (FG) repeats3 and is selectively permeable to cargo captured by nuclear transport receptors (NTRs). Here we show that HIV-1 capsid assemblies can target NPCs efficiently in an NTR-independent manner and bind directly to several types of FG repeats, including barrier-forming cohesive repeats. Like NTRs, the capsid readily partitions into an in vitro assembled cohesive FG phase that can serve as an NPC mimic and excludes much smaller inert probes such as mCherry. Indeed, entry of the capsid protein into such an FG phase is greatly enhanced by capsid assembly, which also allows the encapsulated clients to enter. Thus, our data indicate that the HIV-1 capsid behaves like an NTR, with its interior serving as a cargo container. Because capsid-coating with trans-acting NTRs would increase the diameter by 10 nm or more, we suggest that such a 'self-translocating' capsid undermines the size restrictions imposed by the NPC scaffold, thereby bypassing an otherwise effective barrier to viral infection.
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Affiliation(s)
- Liran Fu
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Erika N Weiskopf
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Onno Akkermans
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicholas A Swanson
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shiya Cheng
- Department of Meiosis, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Thomas U Schwartz
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Dirk Görlich
- Department of Cellular Logistics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
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50
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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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