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Beton JG, Mulvaney T, Cragnolini T, Topf M. Cryo-EM structure and B-factor refinement with ensemble representation. Nat Commun 2024; 15:444. [PMID: 38200043 PMCID: PMC10781738 DOI: 10.1038/s41467-023-44593-1] [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: 07/11/2022] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
Cryo-EM experiments produce images of macromolecular assemblies that are combined to produce three-dimensional density maps. Typically, atomic models of the constituent molecules are fitted into these maps, followed by a density-guided refinement. We introduce TEMPy-ReFF, a method for atomic structure refinement in cryo-EM density maps. Our method represents atomic positions as components of a Gaussian mixture model, utilising their variances as B-factors, which are used to derive an ensemble description. Extensively tested on a substantial dataset of 229 cryo-EM maps from EMDB ranging in resolution from 2.1-4.9 Å with corresponding PDB and CERES atomic models, our results demonstrate that TEMPy-ReFF ensembles provide a superior representation of cryo-EM maps. On a single-model basis, it performs similarly to the CERES re-refinement protocol, although there are cases where it provides a better fit to the map. Furthermore, our method enables the creation of composite maps free of boundary artefacts. TEMPy-ReFF is useful for better interpretation of flexible structures, such as those involving RNA, DNA or ligands.
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Affiliation(s)
- Joseph G Beton
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany
| | - Thomas Mulvaney
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany
| | - Tristan Cragnolini
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany
- Institute of Structural and Molecular Biology, Birkbeck, University of London, London, UK
| | - Maya Topf
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany.
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2
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New opportunities in integrative structural modeling. Curr Opin Struct Biol 2022; 77:102488. [PMID: 36279817 DOI: 10.1016/j.sbi.2022.102488] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 12/14/2022]
Abstract
Integrative structural modeling enables structure determination of macromolecules and their complexes by integrating data from multiple sources. It has been successfully used to characterize macromolecular structures when a single structural biology technique was insufficient. Recent developments in cellular structural biology, including in-cell cryo-electron tomography and artificial intelligence-based structure prediction, have created new opportunities for integrative structural modeling. Here, we will review these opportunities along with the latest developments in integrative modeling methods and their applications. We also highlight open challenges and directions for further development.
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Graziadei A, Rappsilber J. Leveraging crosslinking mass spectrometry in structural and cell biology. Structure 2021; 30:37-54. [PMID: 34895473 DOI: 10.1016/j.str.2021.11.007] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/11/2021] [Accepted: 11/17/2021] [Indexed: 12/18/2022]
Abstract
Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.
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Affiliation(s)
- Andrea Graziadei
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany; Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, UK.
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4
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Anishchenko I, Baek M, Park H, Hiranuma N, Kim DE, Dauparas J, Mansoor S, Humphreys IR, Baker D. Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14. Proteins 2021; 89:1722-1733. [PMID: 34331359 PMCID: PMC8616808 DOI: 10.1002/prot.26194] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/23/2021] [Accepted: 07/25/2021] [Indexed: 12/29/2022]
Abstract
The trRosetta structure prediction method employs deep learning to generate predicted residue-residue distance and orientation distributions from which 3D models are built. We sought to improve the method by incorporating as inputs (in addition to sequence information) both language model embeddings and template information weighted by sequence similarity to the target. We also developed a refinement pipeline that recombines models generated by template-free and template utilizing versions of trRosetta guided by the DeepAccNet accuracy predictor. Both benchmark tests and CASP results show that the new pipeline is a considerable improvement over the original trRosetta, and it is faster and requires less computing resources, completing the entire modeling process in a median < 3 h in CASP14. Our human group improved results with this pipeline primarily by identifying additional homologous sequences for input into the network. We also used the DeepAccNet accuracy predictor to guide Rosetta high-resolution refinement for submissions in the regular and refinement categories; although performance was quite good on a CASP relative scale, the overall improvements were rather modest in part due to missing inter-domain or inter-chain contacts.
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Affiliation(s)
- Ivan Anishchenko
- Department of Biochemistry and Institute for Protein DesignUniversity of WashingtonSeattleWashingtonUSA
| | - Minkyung Baek
- Department of Biochemistry and Institute for Protein DesignUniversity of WashingtonSeattleWashingtonUSA
| | - Hahnbeom Park
- Department of Biochemistry and Institute for Protein DesignUniversity of WashingtonSeattleWashingtonUSA
| | - Naozumi Hiranuma
- Department of Biochemistry and Institute for Protein DesignUniversity of WashingtonSeattleWashingtonUSA
- Paul G. Allen School of Computer Science & EngineeringUniversity of WashingtonSeattleWashingtonUSA
| | - David E. Kim
- Department of Biochemistry and Institute for Protein DesignUniversity of WashingtonSeattleWashingtonUSA
- Howard Hughes Medical InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Justas Dauparas
- Department of Biochemistry and Institute for Protein DesignUniversity of WashingtonSeattleWashingtonUSA
| | - Sanaa Mansoor
- Department of Biochemistry and Institute for Protein DesignUniversity of WashingtonSeattleWashingtonUSA
| | - Ian R. Humphreys
- Department of Biochemistry and Institute for Protein DesignUniversity of WashingtonSeattleWashingtonUSA
| | - David Baker
- Department of Biochemistry and Institute for Protein DesignUniversity of WashingtonSeattleWashingtonUSA
- Howard Hughes Medical InstituteUniversity of WashingtonSeattleWashingtonUSA
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5
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Piersimoni L, Kastritis PL, Arlt C, Sinz A. Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons. Chem Rev 2021; 122:7500-7531. [PMID: 34797068 DOI: 10.1021/acs.chemrev.1c00786] [Citation(s) in RCA: 106] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Mass spectrometry (MS) has become one of the key technologies of structural biology. In this review, the contributions of chemical cross-linking combined with mass spectrometry (XL-MS) for studying three-dimensional structures of proteins and for investigating protein-protein interactions are outlined. We summarize the most important cross-linking reagents, software tools, and XL-MS workflows and highlight prominent examples for characterizing proteins, their assemblies, and interaction networks in vitro and in vivo. Computational modeling plays a crucial role in deriving 3D-structural information from XL-MS data. Integrating XL-MS with other techniques of structural biology, such as cryo-electron microscopy, has been successful in addressing biological questions that to date could not be answered. XL-MS is therefore expected to play an increasingly important role in structural biology in the future.
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Affiliation(s)
- Lolita Piersimoni
- Department of Pharmaceutical Chemistry & Bioanalytics, Institute of Pharmacy, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Center for Structural Mass Spectrometry, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany
| | - Panagiotis L Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Kurt-Mothes-Strasse 3a, D-06120 Halle (Saale), Germany.,Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Biozentrum, Weinbergweg 22, D-06120 Halle (Saale), Germany
| | - Christian Arlt
- Department of Pharmaceutical Chemistry & Bioanalytics, Institute of Pharmacy, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Center for Structural Mass Spectrometry, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany
| | - Andrea Sinz
- Department of Pharmaceutical Chemistry & Bioanalytics, Institute of Pharmacy, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Center for Structural Mass Spectrometry, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany
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Giese SH, Sinn LR, Wegner F, Rappsilber J. Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry. Nat Commun 2021; 12:3237. [PMID: 34050149 PMCID: PMC8163845 DOI: 10.1038/s41467-021-23441-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 04/26/2021] [Indexed: 12/13/2022] Open
Abstract
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits the numbers of protein-protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein-protein interactions without affecting confidence. This approach is not limited to cell lysates and multi-dimensional separation but also improves considerably the analysis of crosslinked multiprotein complexes with a single chromatographic dimension. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of crosslinking mass spectrometry analyses.
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Affiliation(s)
- Sven H Giese
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Ludwig R Sinn
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Fritz Wegner
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany.
- Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
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Sharp MF, Bythell-Douglas R, Deans AJ, Crismani W. The Fanconi anemia ubiquitin E3 ligase complex as an anti-cancer target. Mol Cell 2021; 81:2278-2289. [PMID: 33984284 DOI: 10.1016/j.molcel.2021.04.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 03/27/2021] [Accepted: 04/21/2021] [Indexed: 10/21/2022]
Abstract
Agents that induce DNA damage can cure some cancers. However, the side effects of chemotherapy are severe because of the indiscriminate action of DNA-damaging agents on both healthy and cancerous cells. DNA repair pathway inhibition provides a less toxic and targeted alternative to chemotherapy. A compelling DNA repair target is the Fanconi anemia (FA) E3 ligase core complex due to its critical-and likely singular-role in the efficient removal of specific DNA lesions. FA pathway inactivation has been demonstrated to specifically kill some types of cancer cells without the addition of exogenous DNA damage, including cells that lack BRCA1, BRCA2, ATM, or functionally related genes. In this perspective, we discuss the genetic and biochemical evidence in support of the FA core complex as a compelling drug target for cancer therapy. In particular, we discuss the genetic, biochemical, and structural data that could rapidly advance our capacity to identify and implement the use of FA core complex inhibitors in the clinic.
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Affiliation(s)
- Michael F Sharp
- Genome Stability Unit, St. Vincent's Institute of Medical Research, Fitzroy, VIC, Australia
| | - Rohan Bythell-Douglas
- Genome Stability Unit, St. Vincent's Institute of Medical Research, Fitzroy, VIC, Australia
| | - Andrew J Deans
- Genome Stability Unit, St. Vincent's Institute of Medical Research, Fitzroy, VIC, Australia; Department of Medicine (St. Vincent's), University of Melbourne, Fitzroy, VIC, Australia
| | - Wayne Crismani
- Genome Stability Unit, St. Vincent's Institute of Medical Research, Fitzroy, VIC, Australia; Department of Medicine (St. Vincent's), University of Melbourne, Fitzroy, VIC, Australia.
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