1
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Bulvas O, Knejzlík Z, Sýs J, Filimoněnko A, Čížková M, Clarová K, Rejman D, Kouba T, Pichová I. Deciphering the allosteric regulation of mycobacterial inosine-5'-monophosphate dehydrogenase. Nat Commun 2024; 15:6673. [PMID: 39107302 PMCID: PMC11303537 DOI: 10.1038/s41467-024-50933-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 07/24/2024] [Indexed: 08/10/2024] Open
Abstract
Allosteric regulation of inosine 5'-monophosphate dehydrogenase (IMPDH), an essential enzyme of purine metabolism, contributes to the homeostasis of adenine and guanine nucleotides. However, the precise molecular mechanism of IMPDH regulation in bacteria remains unclear. Using biochemical and cryo-EM approaches, we reveal the intricate molecular mechanism of the IMPDH allosteric regulation in mycobacteria. The enzyme is inhibited by both GTP and (p)ppGpp, which bind to the regulatory CBS domains and, via interactions with basic residues in hinge regions, lock the catalytic core domains in a compressed conformation. This results in occlusion of inosine monophosphate (IMP) substrate binding to the active site and, ultimately, inhibition of the enzyme. The GTP and (p)ppGpp allosteric effectors bind to their dedicated sites but stabilize the compressed octamer by a common mechanism. Inhibition is relieved by the competitive displacement of GTP or (p)ppGpp by ATP allowing IMP-induced enzyme expansion. The structural knowledge and mechanistic understanding presented here open up new possibilities for the development of allosteric inhibitors with antibacterial potential.
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Affiliation(s)
- Ondřej Bulvas
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Zdeněk Knejzlík
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jakub Sýs
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Anatolij Filimoněnko
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Monika Čížková
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Kamila Clarová
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Dominik Rejman
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Tomáš Kouba
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
| | - Iva Pichová
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
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2
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Jamali K, Käll L, Zhang R, Brown A, Kimanius D, Scheres SHW. Automated model building and protein identification in cryo-EM maps. Nature 2024; 628:450-457. [PMID: 38408488 PMCID: PMC11006616 DOI: 10.1038/s41586-024-07215-4] [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: 05/17/2023] [Accepted: 02/19/2024] [Indexed: 02/28/2024]
Abstract
Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs1,2. Here we present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality to those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy to those built by humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will therefore remove bottlenecks and increase objectivity in cryo-EM structure determination.
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Affiliation(s)
| | - Lukas Käll
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Rui Zhang
- Washington University in St Louis, St Louis, MO, USA
| | - Alan Brown
- Blavatnik Institute, Harvard Medical School, Boston, MA, USA
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3
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Terashi G, Wang X, Prasad D, Nakamura T, Kihara D. DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction. Nat Methods 2024; 21:122-131. [PMID: 38066344 DOI: 10.1038/s41592-023-02099-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 10/22/2023] [Indexed: 12/19/2023]
Abstract
Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, there are still many cases where tracing protein main chains is difficult, even in maps determined at a near-atomic resolution. Here we developed a protein structure modeling method, DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, we integrated AlphaFold2 with the de novo density tracing protocol to combine their complementary strengths and achieved even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign the chain identity to the structure models of homo-multimers, which is not a trivial task for existing methods.
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Affiliation(s)
- Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Devashish Prasad
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Tsukasa Nakamura
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
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4
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Silale A, Zhu Y, Witwinowski J, Smith RE, Newman KE, Bhamidimarri SP, Baslé A, Khalid S, Beloin C, Gribaldo S, van den Berg B. Dual function of OmpM as outer membrane tether and nutrient uptake channel in diderm Firmicutes. Nat Commun 2023; 14:7152. [PMID: 37932269 PMCID: PMC10628300 DOI: 10.1038/s41467-023-42601-y] [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: 02/10/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
The outer membrane (OM) in diderm, or Gram-negative, bacteria must be tethered to peptidoglycan for mechanical stability and to maintain cell morphology. Most diderm phyla from the Terrabacteria group have recently been shown to lack well-characterised OM attachment systems, but instead have OmpM, which could represent an ancestral tethering system in bacteria. Here, we have determined the structure of the most abundant OmpM protein from Veillonella parvula (diderm Firmicutes) by single particle cryogenic electron microscopy. We also characterised the channel properties of the transmembrane β-barrel of OmpM and investigated the structure and PG-binding properties of its periplasmic stalk region. Our results show that OM tethering and nutrient acquisition are genetically linked in V. parvula, and probably other diderm Terrabacteria. This dual function of OmpM may have played a role in the loss of the OM in ancestral bacteria and the emergence of monoderm bacterial lineages.
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Affiliation(s)
- Augustinas Silale
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Framlington Place, NE2 4HH, Newcastle upon Tyne, UK
| | - Yiling Zhu
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Framlington Place, NE2 4HH, Newcastle upon Tyne, UK
| | - Jerzy Witwinowski
- Institut Pasteur, Université de Paris Cité, Unit Evolutionary Biology of the Microbial Cell, Paris, France
| | - Robert E Smith
- Institut Pasteur, Université de Paris Cité, Unit Evolutionary Biology of the Microbial Cell, Paris, France
| | - Kahlan E Newman
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK
| | - Satya P Bhamidimarri
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Framlington Place, NE2 4HH, Newcastle upon Tyne, UK
| | - Arnaud Baslé
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Framlington Place, NE2 4HH, Newcastle upon Tyne, UK
| | - Syma Khalid
- Department of Biochemistry, University of Oxford, Oxford, OX1 3QU, UK
| | - Christophe Beloin
- Institut Pasteur, Université de Paris Cité, Genetics of Biofilms Laboratory, Paris, France.
| | - Simonetta Gribaldo
- Institut Pasteur, Université de Paris Cité, Unit Evolutionary Biology of the Microbial Cell, Paris, France.
| | - Bert van den Berg
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Framlington Place, NE2 4HH, Newcastle upon Tyne, UK.
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5
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Jamali K, Käll L, Zhang R, Brown A, Kimanius D, Scheres SH. Automated model building and protein identification in cryo-EM maps. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541002. [PMID: 37292681 PMCID: PMC10245678 DOI: 10.1101/2023.05.16.541002] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention. We present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality as those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy as humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will thus remove bottlenecks and increase objectivity in cryo-EM structure determination.
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Affiliation(s)
| | - Lukas Käll
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Rui Zhang
- Washington University in St. Louis, St. Louis, MO, USA
| | - Alan Brown
- Blavatnik Institute, Harvard Medical School, Boston, MA, USA
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6
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Chojnowski G. DoubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models. Nucleic Acids Res 2023; 51:8255-8269. [PMID: 37395405 PMCID: PMC10450167 DOI: 10.1093/nar/gkad553] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/29/2023] [Accepted: 06/16/2023] [Indexed: 07/04/2023] Open
Abstract
Sequence assignment is a key step of the model building process in both cryogenic electron microscopy (cryo-EM) and macromolecular crystallography (MX). If the assignment fails, it can result in difficult to identify errors affecting the interpretation of a model. There are many model validation strategies that help experimentalists in this step of protein model building, but they are virtually non-existent for nucleic acids. Here, I present doubleHelix-a comprehensive method for assignment, identification, and validation of nucleic acid sequences in structures determined using cryo-EM and MX. The method combines a neural network classifier of nucleobase identities and a sequence-independent secondary structure assignment approach. I show that the presented method can successfully assist sequence-assignment step in nucleic-acid model building at lower resolutions, where visual map interpretation is very difficult. Moreover, I present examples of sequence assignment errors detected using doubleHelix in cryo-EM and MX structures of ribosomes deposited in the Protein Data Bank, which escaped the scrutiny of available model-validation approaches. The doubleHelix program source code is available under BSD-3 license at https://gitlab.com/gchojnowski/doublehelix.
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Affiliation(s)
- Grzegorz Chojnowski
- European Molecular Biology Laboratory, Hamburg Unit, Notkestraße 85, 22607 Hamburg, Germany
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7
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Madani A, Krause B, Greene ER, Subramanian S, Mohr BP, Holton JM, Olmos JL, Xiong C, Sun ZZ, Socher R, Fraser JS, Naik N. Large language models generate functional protein sequences across diverse families. Nat Biotechnol 2023; 41:1099-1106. [PMID: 36702895 PMCID: PMC10400306 DOI: 10.1038/s41587-022-01618-2] [Citation(s) in RCA: 177] [Impact Index Per Article: 177.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/17/2022] [Indexed: 01/27/2023]
Abstract
Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties. ProGen can be further fine-tuned to curated sequences and tags to improve controllable generation performance of proteins from families with sufficient homologous samples. Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%. ProGen is readily adapted to diverse protein families, as we demonstrate with chorismate mutase and malate dehydrogenase.
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Affiliation(s)
- Ali Madani
- Salesforce Research, Palo Alto, CA, USA.
- Profluent Bio, San Francisco, CA, USA.
| | | | - Eric R Greene
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Subu Subramanian
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA, USA
| | | | - James M Holton
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Jose Luis Olmos
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | | | | | | | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
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8
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White JBR, Silale A, Feasey M, Heunis T, Zhu Y, Zheng H, Gajbhiye A, Firbank S, Baslé A, Trost M, Bolam DN, van den Berg B, Ranson NA. Outer membrane utilisomes mediate glycan uptake in gut Bacteroidetes. Nature 2023:10.1038/s41586-023-06146-w. [PMID: 37286596 DOI: 10.1038/s41586-023-06146-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 04/27/2023] [Indexed: 06/09/2023]
Abstract
Bacteroidetes are abundant members of the human microbiota, utilizing a myriad of diet- and host-derived glycans in the distal gut1. Glycan uptake across the bacterial outer membrane of these bacteria is mediated by SusCD protein complexes, comprising a membrane-embedded barrel and a lipoprotein lid, which is thought to open and close to facilitate substrate binding and transport. However, surface-exposed glycan-binding proteins and glycoside hydrolases also play critical roles in the capture, processing and transport of large glycan chains. The interactions between these components in the outer membrane are poorly understood, despite being crucial for nutrient acquisition by our colonic microbiota. Here we show that for both the levan and dextran utilization systems of Bacteroides thetaiotaomicron, the additional outer membrane components assemble on the core SusCD transporter, forming stable glycan-utilizing machines that we term utilisomes. Single-particle cryogenic electron microscopy structures in the absence and presence of substrate reveal concerted conformational changes that demonstrate the mechanism of substrate capture, and rationalize the role of each component in the utilisome.
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Affiliation(s)
- Joshua B R White
- Astbury Centre for Structural Molecular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK
| | - Augustinas Silale
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - Matthew Feasey
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - Tiaan Heunis
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - Yiling Zhu
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - Hong Zheng
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - Akshada Gajbhiye
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - Susan Firbank
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - Arnaud Baslé
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - Matthias Trost
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - David N Bolam
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - Bert van den Berg
- Biosciences Institute, The Medical School, Newcastle University, Newcastle upon Tyne, UK.
| | - Neil A Ranson
- Astbury Centre for Structural Molecular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK.
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9
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Figiel M, Gapińska M, Czarnocki-Cieciura M, Zajko W, Sroka M, Skowronek K, Nowotny M. Mechanism of protein-primed template-independent DNA synthesis by Abi polymerases. Nucleic Acids Res 2022; 50:10026-10040. [PMID: 36107766 PMCID: PMC9508834 DOI: 10.1093/nar/gkac772] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/23/2022] [Accepted: 08/30/2022] [Indexed: 11/20/2022] Open
Abstract
Abortive infection (Abi) is a bacterial antiphage defense strategy involving suicide of the infected cell. Some Abi pathways involve polymerases that are related to reverse transcriptases. They are unique in the way they combine the ability to synthesize DNA in a template-independent manner with protein priming. Here, we report crystal and cryo-electron microscopy structures of two Abi polymerases: AbiK and Abi-P2. Both proteins adopt a bilobal structure with an RT-like domain that comprises palm and fingers subdomains and a unique helical domain. AbiK and Abi-P2 adopt a hexameric and trimeric configuration, respectively, which is unprecedented for reverse transcriptases. Biochemical experiments showed that the formation of these oligomers is required for the DNA polymerization activity. The structure of the AbiK–DNA covalent adduct visualized interactions between the 3′ end of DNA and the active site and covalent attachment of the 5′ end of DNA to a tyrosine residue used for protein priming. Our data reveal a structural basis of the mechanism of highly unusual template-independent protein-priming polymerases.
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Affiliation(s)
- Małgorzata Figiel
- Laboratory of Protein Structure, International Institute of Molecular and Cell Biology , Warsaw , Poland
| | - Marta Gapińska
- Laboratory of Protein Structure, International Institute of Molecular and Cell Biology , Warsaw , Poland
| | - Mariusz Czarnocki-Cieciura
- Laboratory of Protein Structure, International Institute of Molecular and Cell Biology , Warsaw , Poland
| | - Weronika Zajko
- Laboratory of Protein Structure, International Institute of Molecular and Cell Biology , Warsaw , Poland
| | - Małgorzata Sroka
- Laboratory of Protein Structure, International Institute of Molecular and Cell Biology , Warsaw , Poland
| | - Krzysztof Skowronek
- Biophysics and Bioanalytics Facility, International Institute of Molecular and Cell Biology , Warsaw , Poland
| | - Marcin Nowotny
- Laboratory of Protein Structure, International Institute of Molecular and Cell Biology , Warsaw , Poland
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10
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Bond PS, Cowtan KD. ModelCraft: an advanced automated model-building pipeline using Buccaneer. Acta Crystallogr D Struct Biol 2022; 78:1090-1098. [PMID: 36048149 PMCID: PMC9435595 DOI: 10.1107/s2059798322007732] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/01/2022] [Indexed: 01/16/2023] Open
Abstract
Interactive model building can be a difficult and time-consuming step in the structure-solution process. Automated model-building programs such as Buccaneer often make it quicker and easier by completing most of the model in advance. However, they may fail to do so with low-resolution data or a poor initial model or map. The Buccaneer pipeline is a relatively simple program that iterates Buccaneer with REFMAC to refine the model and update the map. A new pipeline called ModelCraft has been developed that expands on this to include shift-field refinement, machine-learned pruning of incorrect residues, classical density modification, addition of water and dummy atoms, building of nucleic acids and final rebuilding of side chains. Testing was performed on 1180 structures solved by experimental phasing, 1338 structures solved by molecular replacement using homologues and 2030 structures solved by molecular replacement using predicted AlphaFold models. Compared with the previous Buccaneer pipeline, ModelCraft increased the mean completeness of the protein models in the experimental phasing cases from 91% to 95%, the molecular-replacement cases from 50% to 78% and the AlphaFold cases from 82% to 91%.
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11
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Chua EYD, Mendez JH, Rapp M, Ilca SL, Tan YZ, Maruthi K, Kuang H, Zimanyi CM, Cheng A, Eng ET, Noble AJ, Potter CS, Carragher B. Better, Faster, Cheaper: Recent Advances in Cryo-Electron Microscopy. Annu Rev Biochem 2022; 91:1-32. [PMID: 35320683 PMCID: PMC10393189 DOI: 10.1146/annurev-biochem-032620-110705] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Cryo-electron microscopy (cryo-EM) continues its remarkable growth as a method for visualizing biological objects, which has been driven by advances across the entire pipeline. Developments in both single-particle analysis and in situ tomography have enabled more structures to be imaged and determined to better resolutions, at faster speeds, and with more scientists having improved access. This review highlights recent advances at each stageof the cryo-EM pipeline and provides examples of how these techniques have been used to investigate real-world problems, including antibody development against the SARS-CoV-2 spike during the recent COVID-19 pandemic.
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Affiliation(s)
- Eugene Y D Chua
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
- National Center for CryoEM Access and Training, New York, NY, USA
| | - Joshua H Mendez
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
- National Center for CryoEM Access and Training, New York, NY, USA
| | - Micah Rapp
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
| | - Serban L Ilca
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
| | - Yong Zi Tan
- Department of Biological Sciences, National University of Singapore, Singapore;
- Disease Intervention Technology Laboratory, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Kashyap Maruthi
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
- National Resource for Automated Molecular Microscopy, New York, NY, USA
| | - Huihui Kuang
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
- National Resource for Automated Molecular Microscopy, New York, NY, USA
| | - Christina M Zimanyi
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
- National Center for CryoEM Access and Training, New York, NY, USA
| | - Anchi Cheng
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
- National Resource for Automated Molecular Microscopy, New York, NY, USA
| | - Edward T Eng
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
- National Center for CryoEM Access and Training, New York, NY, USA
| | - Alex J Noble
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
- National Resource for Automated Molecular Microscopy, New York, NY, USA
- National Center for In-Situ Tomographic Ultramicroscopy, New York, NY, USA
- Simons Machine Learning Center, New York, NY, USA
| | - Clinton S Potter
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
- National Center for CryoEM Access and Training, New York, NY, USA
- National Resource for Automated Molecular Microscopy, New York, NY, USA
- National Center for In-Situ Tomographic Ultramicroscopy, New York, NY, USA
- Simons Machine Learning Center, New York, NY, USA
| | - Bridget Carragher
- New York Structural Biology Center, New York, NY, USA; , , , , , , , , , , ,
- Simons Electron Microscopy Center, New York, NY, USA
- National Center for CryoEM Access and Training, New York, NY, USA
- National Resource for Automated Molecular Microscopy, New York, NY, USA
- National Center for In-Situ Tomographic Ultramicroscopy, New York, NY, USA
- Simons Machine Learning Center, New York, NY, USA
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12
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Khera R, Mehdipour AR, Bolla JR, Kahnt J, Welsch S, Ermler U, Muenke C, Robinson CV, Hummer G, Xie H, Michel H. Cryo-EM structures of pentameric autoinducer-2 exporter from Escherichia coli reveal its transport mechanism. EMBO J 2022; 41:e109990. [PMID: 35698912 PMCID: PMC9475539 DOI: 10.15252/embj.2021109990] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 12/16/2022] Open
Abstract
Bacteria utilize small extracellular molecules to communicate in order to collectively coordinate their behaviors in response to the population density. Autoinducer-2 (AI-2), a universal molecule for both intra- and inter-species communication, is involved in the regulation of biofilm formation, virulence, motility, chemotaxis, and antibiotic resistance. While many studies have been devoted to understanding the biosynthesis and sensing of AI-2, very little information is available on its export. The protein TqsA from Escherichia coli, which belongs to the AI-2 exporter superfamily, has been shown to export AI-2. Here, we report the cryogenic electron microscopic structures of two AI-2 exporters (TqsA and YdiK) from E. coli at 3.35 Å and 2.80 Å resolutions, respectively. Our structures suggest that the AI-2 exporter exists as a homo-pentameric complex. In silico molecular docking and native mass spectrometry experiments were employed to demonstrate the interaction between AI-2 and TqsA, and the results highlight the functional importance of two helical hairpins in substrate binding. We propose that each monomer works as an independent functional unit utilizing an elevator-type transport mechanism.
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Affiliation(s)
- Radhika Khera
- Department of Molecular Membrane Biology, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Ahmad R Mehdipour
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany.,Centre for molecular modelling, Ghent University, Zwijnaarde, Belgium
| | - Jani R Bolla
- Physical and Theoretical Chemistry Laboratory, University of Oxford, Oxford, UK.,The Kavli Institute for Nanoscience Discovery, Oxford, UK.,Department of Plant Sciences, University of Oxford, Oxford, UK
| | - Joerg Kahnt
- Core Facility for Mass Spectrometry and Proteomics, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Sonja Welsch
- Central Electron Microscopy Facility, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Ulrich Ermler
- Department of Molecular Membrane Biology, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Cornelia Muenke
- Department of Molecular Membrane Biology, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Carol V Robinson
- Physical and Theoretical Chemistry Laboratory, University of Oxford, Oxford, UK.,The Kavli Institute for Nanoscience Discovery, Oxford, UK
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany.,Institute of Biophysics, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Hao Xie
- Department of Molecular Membrane Biology, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Hartmut Michel
- Department of Molecular Membrane Biology, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
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13
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Wilson LFL, Dendooven T, Hardwick SW, Echevarría-Poza A, Tryfona T, Krogh KBRM, Chirgadze DY, Luisi BF, Logan DT, Mani K, Dupree P. The structure of EXTL3 helps to explain the different roles of bi-domain exostosins in heparan sulfate synthesis. Nat Commun 2022; 13:3314. [PMID: 35676258 PMCID: PMC9178029 DOI: 10.1038/s41467-022-31048-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/31/2022] [Indexed: 11/08/2022] Open
Abstract
Heparan sulfate is a highly modified O-linked glycan that performs diverse physiological roles in animal tissues. Though quickly modified, it is initially synthesised as a polysaccharide of alternating β-D-glucuronosyl and N-acetyl-α-D-glucosaminyl residues by exostosins. These enzymes generally possess two glycosyltransferase domains (GT47 and GT64)-each thought to add one type of monosaccharide unit to the backbone. Although previous structures of murine exostosin-like 2 (EXTL2) provide insight into the GT64 domain, the rest of the bi-domain architecture is yet to be characterised; hence, how the two domains co-operate is unknown. Here, we report the structure of human exostosin-like 3 (EXTL3) in apo and UDP-bound forms. We explain the ineffectiveness of EXTL3's GT47 domain to transfer β-D-glucuronosyl units, and we observe that, in general, the bi-domain architecture would preclude a processive mechanism of backbone extension. We therefore propose that heparan sulfate backbone polymerisation occurs by a simple dissociative mechanism.
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Affiliation(s)
- L F L Wilson
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, 22903, USA
| | - T Dendooven
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK
| | - S W Hardwick
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
| | - A Echevarría-Poza
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK
| | - T Tryfona
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK
| | - K B R M Krogh
- Department of Protein Biochemistry and Stability, Novozymes A/S, Krogshøjvej 36, 2880, Bagsværd, Denmark
| | - D Y Chirgadze
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
| | - B F Luisi
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
| | - D T Logan
- Biochemistry and Structural Biology, Centre for Molecular Protein Science, Department of Chemistry, Lund University, SE-221 00, Lund, Sweden
| | - K Mani
- Department of Experimental Medical Science, Division of Neuroscience, Glycobiology Group, Lund University, SE-221 00, Lund, Sweden.
| | - P Dupree
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, UK.
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14
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Chojnowski G. Sequence-assignment validation in cryo-EM models with checkMySequence. ACTA CRYSTALLOGRAPHICA SECTION D STRUCTURAL BIOLOGY 2022; 78:806-816. [PMID: 35775980 PMCID: PMC9248842 DOI: 10.1107/s2059798322005009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/10/2022] [Indexed: 01/18/2023]
Abstract
A new method, checkMySequence, for the fast and automated detection of register errors in protein models built into cryo-EM reconstructions is presented. The availability of new artificial intelligence-based protein-structure-prediction tools has radically changed the way that cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register shifts remain one of the most difficult to identify and correct. Here, checkMySequence, a fast, fully automated and parameter-free method for detecting register shifts in protein models built into cryo-EM maps, is introduced. It is shown that the method can assist model building in cases where poorer map resolution hinders visual interpretation. It is also shown that checkMySequence could have helped to avoid a widely discussed sequence-register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community. The software is freely available at https://gitlab.com/gchojnowski/checkmysequence.
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15
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Kurihara N, Nakagawa R, Hirano H, Okazaki S, Tomita A, Kobayashi K, Kusakizako T, Nishizawa T, Yamashita K, Scott DA, Nishimasu H, Nureki O. Structure of the type V-C CRISPR-Cas effector enzyme. Mol Cell 2022; 82:1865-1877.e4. [PMID: 35366394 PMCID: PMC9522604 DOI: 10.1016/j.molcel.2022.03.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 12/27/2021] [Accepted: 02/28/2022] [Indexed: 01/02/2023]
Abstract
RNA-guided CRISPR-Cas nucleases are widely used as versatile genome-engineering tools. Recent studies identified functionally divergent type V Cas12 family enzymes. Among them, Cas12c2 binds a CRISPR RNA (crRNA) and a trans-activating crRNA (tracrRNA) and recognizes double-stranded DNA targets with a short TN PAM. Here, we report the cryo-electron microscopy structures of the Cas12c2-guide RNA binary complex and the Cas12c2-guide RNA-target DNA ternary complex. The structures revealed that the crRNA and tracrRNA form an unexpected X-junction architecture, and that Cas12c2 recognizes a single T nucleotide in the PAM through specific hydrogen-bonding interactions with two arginine residues. Furthermore, our biochemical analyses indicated that Cas12c2 processes its precursor crRNA to a mature crRNA using the RuvC catalytic site through a unique mechanism. Collectively, our findings improve the mechanistic understanding of diverse type V CRISPR-Cas effectors.
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Affiliation(s)
- Nina Kurihara
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Ryoya Nakagawa
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Hisato Hirano
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Sae Okazaki
- Structural Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan
| | - Atsuhiro Tomita
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Kan Kobayashi
- PeptiDream Inc., 3-25-23 Tonomachi, Kawasaki-ku, Kawasaki City, Kanagawa Prefecture 210-0821, Japan
| | - Tsukasa Kusakizako
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tomohiro Nishizawa
- Graduate School of Medical Life Science, Yokohama City University, Yokohama 230-0045, Japan
| | - Keitaro Yamashita
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | | | - Hiroshi Nishimasu
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; Structural Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan; Inamori Research Institute for Science, 620 Suiginya-cho, Shimogyo-ku, Kyoto 600-8411, Japan.
| | - Osamu Nureki
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
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16
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Kögel A, Keidel A, Bonneau F, Schäfer IB, Conti E. The human SKI complex regulates channeling of ribosome-bound RNA to the exosome via an intrinsic gatekeeping mechanism. Mol Cell 2022; 82:756-769.e8. [PMID: 35120588 PMCID: PMC8860381 DOI: 10.1016/j.molcel.2022.01.009] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 12/01/2021] [Accepted: 01/07/2022] [Indexed: 12/31/2022]
Abstract
The superkiller (SKI) complex is the cytoplasmic co-factor and regulator of the RNA-degrading exosome. In human cells, the SKI complex functions mainly in co-translational surveillance-decay pathways, and its malfunction is linked to a severe congenital disorder, the trichohepatoenteric syndrome. To obtain insights into the molecular mechanisms regulating the human SKI (hSKI) complex, we structurally characterized several of its functional states in the context of 80S ribosomes and substrate RNA. In a prehydrolytic ATP form, the hSKI complex exhibits a closed conformation with an inherent gating system that effectively traps the 80S-bound RNA into the hSKI2 helicase subunit. When active, hSKI switches to an open conformation in which the gating is released and the RNA 3′ end exits the helicase. The emerging picture is that the gatekeeping mechanism and architectural remodeling of hSKI underpin a regulated RNA channeling system that is mechanistically conserved among the cytoplasmic and nuclear helicase-exosome complexes. hSKI has closed and open states connected to different helicase conformations The intrinsic closed state traps the RNA 3′ end and blocks the RNA exit path ATP induces the open state of hSKI, allowing 80S ribosome-bound RNA extraction The hSKI open state primes hSKI2 for channeling RNA to the cytosolic exosome
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Affiliation(s)
- Alexander Kögel
- Department of Structural Cell Biology, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Munich, Germany
| | - Achim Keidel
- Department of Structural Cell Biology, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Munich, Germany
| | - Fabien Bonneau
- Department of Structural Cell Biology, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Munich, Germany
| | - Ingmar B Schäfer
- Department of Structural Cell Biology, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Munich, Germany.
| | - Elena Conti
- Department of Structural Cell Biology, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Munich, Germany.
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17
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Dimos N, Helmer CPO, Chánique AM, Wahl MC, Kourist R, Hilal T, Loll B. CryoEM analysis of small plant biocatalysts at sub-2 Å resolution. Acta Crystallogr D Struct Biol 2022; 78:113-123. [PMID: 34981767 PMCID: PMC8725159 DOI: 10.1107/s205979832101216x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/16/2021] [Indexed: 12/02/2022] Open
Abstract
Enzyme catalysis has emerged as a key technology for developing efficient, sustainable processes in the chemical, biotechnological and pharmaceutical industries. Plants provide large and diverse pools of biosynthetic enzymes that facilitate complex reactions, such as the formation of intricate terpene carbon skeletons, with exquisite specificity. High-resolution structural analysis of these enzymes is crucial in order to understand their mechanisms and modulate their properties by targeted engineering. Although cryo-electron microscopy (cryoEM) has revolutionized structural biology, its applicability to high-resolution structural analysis of comparatively small enzymes has so far been largely unexplored. Here, it is shown that cryoEM can reveal the structures of plant borneol dehydrogenases of ∼120 kDa at or below 2 Å resolution, paving the way for the rapid development of new biocatalysts that can provide access to bioactive terpenes and terpenoids.
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Affiliation(s)
- Nicole Dimos
- Institute of Chemistry and Biochemistry, Department of Biology, Chemistry, Pharmacy, Laboratory of Structural Biochemistry, Free University of Berlin, Takustrasse 6, 14195 Berlin, Germany
| | - Carl P. O. Helmer
- Institute of Chemistry and Biochemistry, Department of Biology, Chemistry, Pharmacy, Laboratory of Structural Biochemistry, Free University of Berlin, Takustrasse 6, 14195 Berlin, Germany
| | - Andrea M. Chánique
- Institute of Molecular Biotechnology, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria
- Department of Chemical and Bioprocesses Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, 7810000 Santiago, Chile
| | - Markus C. Wahl
- Institute of Chemistry and Biochemistry, Department of Biology, Chemistry, Pharmacy, Laboratory of Structural Biochemistry, Free University of Berlin, Takustrasse 6, 14195 Berlin, Germany
- Macromolecular Crystallography, Helmholtz-Zentrum Berlin für Materialien und Energie, Albert-Einstein-Strasse 15, 12489 Berlin, Germany
| | - Robert Kourist
- Institute of Molecular Biotechnology, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria
| | - Tarek Hilal
- Institute of Chemistry and Biochemistry, Research Center of Electron Microscopy and Core Facility BioSupraMol, Free University of Berlin, Fabeckstrasse 36A, 14195 Berlin, Germany
| | - Bernhard Loll
- Institute of Chemistry and Biochemistry, Department of Biology, Chemistry, Pharmacy, Laboratory of Structural Biochemistry, Free University of Berlin, Takustrasse 6, 14195 Berlin, Germany
- moloX GmbH, Takustrasse 6, 14195 Berlin, Germany
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18
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Chojnowski G, Simpkin AJ, Leonardo DA, Seifert-Davila W, Vivas-Ruiz DE, Keegan RM, Rigden DJ. findMySequence: a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM. IUCRJ 2022; 9:86-97. [PMID: 35059213 PMCID: PMC8733886 DOI: 10.1107/s2052252521011088] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 10/22/2021] [Indexed: 05/15/2023]
Abstract
Although experimental protein-structure determination usually targets known proteins, chains of unknown sequence are often encountered. They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or appear as a contaminant. Regardless of the source of the problem, the unknown protein always requires characterization. Here, an automated pipeline is presented for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. The method's application to characterize the crystal structure of an unknown protein purified from a snake venom is presented. It is also shown that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures.
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Affiliation(s)
- Grzegorz Chojnowski
- European Molecular Biology Laboratory, Hamburg Unit, Notkestrasse 85, 22607 Hamburg, Germany
- Correspondence e-mail:
| | - Adam J. Simpkin
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Diego A. Leonardo
- São Carlos Institute of Physics, University of São Paulo, Avenida João Dagnone 1100, São Carlos, SP 13563-120, Brazil
| | | | - Dan E. Vivas-Ruiz
- Laboratorio de Biología Molecular, Facultad de Ciencias Biológicas, Universidad Nacional Mayor de San Marcos, Avenida Venezuela Cdra 34 S/N, Ciudad Universitaria, Lima, Peru
| | - Ronan M. Keegan
- Rutherford Appleton Laboratory, Research Complex at Harwell, UKRI-STFC, Didcot OX11 0FA, United Kingdom
| | - Daniel J. Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
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19
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Simpkin AJ, Winn MD, Rigden DJ, Keegan RM. Redeployment of automated MrBUMP search-model identification for map fitting in cryo-EM. Acta Crystallogr D Struct Biol 2021; 77:1378-1385. [PMID: 34726166 PMCID: PMC8561737 DOI: 10.1107/s2059798321009165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 09/03/2021] [Indexed: 11/22/2022] Open
Abstract
In crystallography, the phase problem can often be addressed by the careful preparation of molecular-replacement search models. This has led to the development of pipelines such as MrBUMP that can automatically identify homologous proteins from an input sequence and edit them to focus on the areas that are most conserved. Many of these approaches can be applied directly to cryo-EM to help discover, prepare and correctly place models (here called cryo-EM search models) into electrostatic potential maps. This can significantly reduce the amount of manual model building that is required for structure determination. Here, MrBUMP is repurposed to fit automatically obtained PDB-derived chains and domains into cryo-EM maps. MrBUMP was successfully able to identify and place cryo-EM search models across a range of resolutions. Methods such as map segmentation are also explored as potential routes to improved performance. Map segmentation was also found to improve the effectiveness of the pipeline for higher resolution (<8 Å) data sets.
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Affiliation(s)
- Adam J. Simpkin
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Martyn D. Winn
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Daniel J. Rigden
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Ronan M. Keegan
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
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20
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Yamashita K, Palmer CM, Burnley T, Murshudov GN. Cryo-EM single-particle structure refinement and map calculation using Servalcat. Acta Crystallogr D Struct Biol 2021; 77:1282-1291. [PMID: 34605431 PMCID: PMC8489229 DOI: 10.1107/s2059798321009475] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 09/11/2021] [Indexed: 11/10/2022] Open
Abstract
In 2020, cryo-EM single-particle analysis achieved true atomic resolution thanks to technological developments in hardware and software. The number of high-resolution reconstructions continues to grow, increasing the importance of the accurate determination of atomic coordinates. Here, a new Python package and program called Servalcat is presented that is designed to facilitate atomic model refinement. Servalcat implements a refinement pipeline using the program REFMAC5 from the CCP4 package. After the refinement, Servalcat calculates a weighted Fo - Fc difference map, which is derived from Bayesian statistics. This map helps manual and automatic model building in real space, as is common practice in crystallography. The Fo - Fc map helps in the visualization of weak features including hydrogen densities. Although hydrogen densities are weak, they are stronger than in the electron-density maps produced by X-ray crystallography, and some H atoms are even visible at ∼1.8 Å resolution. Servalcat also facilitates atomic model refinement under symmetry constraints. If point-group symmetry has been applied to the map during reconstruction, the asymmetric unit model is refined with the appropriate symmetry constraints.
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Affiliation(s)
- Keitaro Yamashita
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Colin M. Palmer
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Campus, Didcot OX11 0FA, United Kingdom
| | - Tom Burnley
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Campus, Didcot OX11 0FA, United Kingdom
| | - Garib N. Murshudov
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
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21
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Olek M, Joseph AP. Cryo-EM Map-Based Model Validation Using the False Discovery Rate Approach. Front Mol Biosci 2021; 8:652530. [PMID: 34084774 PMCID: PMC8167059 DOI: 10.3389/fmolb.2021.652530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/26/2021] [Indexed: 01/18/2023] Open
Abstract
Significant technological developments and increasing scientific interest in cryogenic electron microscopy (cryo-EM) has resulted in a rapid increase in the amount of data generated by these experiments and the derived atomic models. Robust measures for the validation of 3D reconstructions and atomic models are essential for appropriate interpretation of the data. The resolution of data and availability of software tools that work across a range of resolutions often limit the quality of derived models. Hence, the final atomic model is often incomplete or contains regions where atomic positions are less reliable or incorrectly built. Extensive manual pruning and local adjustments or rebuilding are usually required to address these issues. The presented research introduces a software tool for the validation of the backbone trace of atomic models built in the cryo-EM density maps. In this study, we use the false discovery rate analysis, which can be used to segregate molecular signals from the background. Each atomic position in the model can be associated with an FDR backbone validation score, which can be used to identify potential mistraced residues. We demonstrate that the proposed validation score is complementary to existing validation metrics and is useful especially in cases where the model is built in the maps having varying local resolution. We also discuss the application of the score for automated pruning of atomic models built ab-initio during the iterative model building process in Buccaneer. We have implemented this score in the CCP-EM software suite.
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Affiliation(s)
- Mateusz Olek
- Department of Chemistry, University of York, York, United Kingdom
- Electron BioImaging Center, Rutherford Appleton Laboratory, Didcot, United Kingdom
| | - Agnel Praveen Joseph
- Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell, Didcot, United Kingdom
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22
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Lawson CL, Kryshtafovych A, Adams PD, Afonine PV, Baker ML, Barad BA, Bond P, Burnley T, Cao R, Cheng J, Chojnowski G, Cowtan K, Dill KA, DiMaio F, Farrell DP, Fraser JS, Herzik MA, Hoh SW, Hou J, Hung LW, Igaev M, Joseph AP, Kihara D, Kumar D, Mittal S, Monastyrskyy B, Olek M, Palmer CM, Patwardhan A, Perez A, Pfab J, Pintilie GD, Richardson JS, Rosenthal PB, Sarkar D, Schäfer LU, Schmid MF, Schröder GF, Shekhar M, Si D, Singharoy A, Terashi G, Terwilliger TC, Vaiana A, Wang L, Wang Z, Wankowicz SA, Williams CJ, Winn M, Wu T, Yu X, Zhang K, Berman HM, Chiu W. Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge. Nat Methods 2021; 18:156-164. [PMID: 33542514 PMCID: PMC7864804 DOI: 10.1038/s41592-020-01051-w] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 12/21/2020] [Indexed: 01/30/2023]
Abstract
This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.
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Affiliation(s)
- Catherine L. Lawson
- grid.430387.b0000 0004 1936 8796Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ USA
| | - Andriy Kryshtafovych
- grid.27860.3b0000 0004 1936 9684Genome Center, University of California, Davis, CA USA
| | - Paul D. Adams
- grid.184769.50000 0001 2231 4551Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA ,grid.47840.3f0000 0001 2181 7878Department of Bioengineering, University of California Berkeley, Berkeley, CA USA
| | - Pavel V. Afonine
- grid.184769.50000 0001 2231 4551Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA
| | - Matthew L. Baker
- grid.267308.80000 0000 9206 2401Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Benjamin A. Barad
- grid.214007.00000000122199231Department of Integrated Computational Structural Biology, The Scripps Research Institute, La Jolla, CA USA
| | - Paul Bond
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Tom Burnley
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Renzhi Cao
- grid.261584.c0000 0001 0492 9915Department of Computer Science, Pacific Lutheran University, Tacoma, WA USA
| | - Jianlin Cheng
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Grzegorz Chojnowski
- grid.475756.20000 0004 0444 5410European Molecular Biology Laboratory, c/o DESY, Hamburg, Germany
| | - Kevin Cowtan
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Ken A. Dill
- grid.36425.360000 0001 2216 9681Laufer Center, Stony Brook University, Stony Brook, NY USA
| | - Frank DiMaio
- grid.34477.330000000122986657Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA USA
| | - Daniel P. Farrell
- grid.34477.330000000122986657Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA USA
| | - James S. Fraser
- grid.266102.10000 0001 2297 6811Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA USA
| | - Mark A. Herzik
- grid.266100.30000 0001 2107 4242Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA USA
| | - Soon Wen Hoh
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Jie Hou
- grid.262962.b0000 0004 1936 9342Department of Computer Science, Saint Louis University, St. Louis, MO USA
| | - Li-Wei Hung
- grid.148313.c0000 0004 0428 3079Los Alamos National Laboratory, Los Alamos, NM USA
| | - Maxim Igaev
- grid.418140.80000 0001 2104 4211Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Agnel P. Joseph
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Daisuke Kihara
- grid.169077.e0000 0004 1937 2197Department of Biological Sciences, Purdue University, West Lafayette, IN USA ,grid.169077.e0000 0004 1937 2197Department of Computer Science, Purdue University, West Lafayette, IN USA
| | - Dilip Kumar
- grid.39382.330000 0001 2160 926XVerna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX USA
| | - Sumit Mittal
- grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA ,grid.411530.20000 0001 0694 3745School of Advanced Sciences and Languages, VIT Bhopal University, Bhopal, India
| | - Bohdan Monastyrskyy
- grid.27860.3b0000 0004 1936 9684Genome Center, University of California, Davis, CA USA
| | - Mateusz Olek
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Colin M. Palmer
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Ardan Patwardhan
- grid.225360.00000 0000 9709 7726The European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Alberto Perez
- grid.15276.370000 0004 1936 8091Department of Chemistry, University of Florida, Gainesville, FL USA
| | - Jonas Pfab
- grid.462982.30000 0000 8883 2602Division of Computing & Software Systems, University of Washington, Bothell, WA USA
| | - Grigore D. Pintilie
- grid.168010.e0000000419368956Department of Bioengineering, Stanford University, Stanford, CA USA
| | - Jane S. Richardson
- grid.26009.3d0000 0004 1936 7961Department of Biochemistry, Duke University, Durham, NC USA
| | - Peter B. Rosenthal
- grid.451388.30000 0004 1795 1830Structural Biology of Cells and Viruses Laboratory, Francis Crick Institute, London, UK
| | - Daipayan Sarkar
- grid.169077.e0000 0004 1937 2197Department of Biological Sciences, Purdue University, West Lafayette, IN USA ,grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA
| | - Luisa U. Schäfer
- grid.8385.60000 0001 2297 375XInstitute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
| | - Michael F. Schmid
- grid.168010.e0000000419368956Division of CryoEM and Biomaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA USA
| | - Gunnar F. Schröder
- grid.8385.60000 0001 2297 375XInstitute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany ,grid.411327.20000 0001 2176 9917Physics Department, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Mrinal Shekhar
- grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA ,grid.66859.34Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Dong Si
- grid.462982.30000 0000 8883 2602Division of Computing & Software Systems, University of Washington, Bothell, WA USA
| | - Abishek Singharoy
- grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA
| | - Genki Terashi
- grid.418140.80000 0001 2104 4211Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | | | - Andrea Vaiana
- grid.418140.80000 0001 2104 4211Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Liguo Wang
- grid.34477.330000000122986657Department of Biological Structure, University of Washington, Seattle, WA USA
| | - Zhe Wang
- grid.225360.00000 0000 9709 7726The European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Stephanie A. Wankowicz
- grid.266102.10000 0001 2297 6811Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Biophysics Graduate Program, University of California, San Francisco, CA USA
| | | | - Martyn Winn
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Tianqi Wu
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Xiaodi Yu
- grid.497530.c0000 0004 0389 4927SMPS, Janssen Research and Development, Spring House, PA USA
| | - Kaiming Zhang
- grid.168010.e0000000419368956Department of Bioengineering, Stanford University, Stanford, CA USA
| | - Helen M. Berman
- grid.430387.b0000 0004 1936 8796Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ USA ,grid.42505.360000 0001 2156 6853Department of Biological Sciences and Bridge Institute, University of Southern California, Los Angeles, CA USA
| | - Wah Chiu
- grid.168010.e0000000419368956Department of Bioengineering, Stanford University, Stanford, CA USA ,grid.168010.e0000000419368956Division of CryoEM and Biomaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA USA
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