1
|
Belsom A, Rappsilber J. Anatomy of a crosslinker. Curr Opin Chem Biol 2020; 60:39-46. [PMID: 32829152 DOI: 10.1016/j.cbpa.2020.07.008] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 07/10/2020] [Accepted: 07/13/2020] [Indexed: 12/17/2022]
Abstract
Crosslinking mass spectrometry has become a core technology in structural biology and is expanding its reach towards systems biology. Its appeal lies in a rapid workflow, high sensitivity and the ability to provide data on proteins in complex systems, even in whole cells. The technology depends heavily on crosslinking reagents. The anatomy of crosslinkers can be modular, sometimes comprising combinations of functional groups. These groups are defined by concepts including: reaction selectivity to increase information density, enrichability to improve detection, cleavability to enhance the identification process and isotope-labelling for quantification. Here, we argue that both concepts and functional groups need more thorough experimental evaluation, so that we can show exactly how and where they are useful when applied to crosslinkers. Crosslinker design should be driven by data, not only concepts. We focus on two crosslinker concepts with large consequences for the technology, namely reactive group reaction kinetics and enrichment groups.
Collapse
Affiliation(s)
- Adam Belsom
- 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, Edinburgh, EH9 3BF, UK.
| |
Collapse
|
2
|
Fajardo JE, Shrestha R, Gil N, Belsom A, Crivelli SN, Czaplewski C, Fidelis K, Grudinin S, Karasikov M, Karczyńska AS, Kryshtafovych A, Leitner A, Liwo A, Lubecka EA, Monastyrskyy B, Pagès G, Rappsilber J, Sieradzan AK, Sikorska C, Trabjerg E, Fiser A. Assessment of chemical-crosslink-assisted protein structure modeling in CASP13. Proteins 2019; 87:1283-1297. [PMID: 31569265 PMCID: PMC6851497 DOI: 10.1002/prot.25816] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 08/08/2019] [Accepted: 09/13/2019] [Indexed: 12/22/2022]
Abstract
With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest-to-date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows.
Collapse
Affiliation(s)
- J. Eduardo Fajardo
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Rojan Shrestha
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Nelson Gil
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Adam Belsom
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Silvia N. Crivelli
- Department of Computer Science, UC Davis, One Shields Ave., Davis, CA 95616
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis CA 95616-8816, USA
| | - Sergei Grudinin
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP LJK, 38000 Grenoble, France
| | - Mikhail Karasikov
- Center for Energy Systems, Skolkovo Institute of Science and Technology, Moscow, 143026, Russia
- Moscow Institute of Physics and Technology, Moscow, 141701, Russia
- Department of Computer Science, ETH Zurich, Zurich, 8092, Switzerland
| | | | - Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis CA 95616-8816, USA
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, 8093 Zurich, Switzerland
| | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
- School of Computational Sciences, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea
| | - Emilia A. Lubecka
- Institute of Informatics, Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Wita Stwosza 57, 80-308 Gdańsk, Poland
| | - Bohdan Monastyrskyy
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis CA 95616-8816, USA
| | - Guillaume Pagès
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP LJK, 38000 Grenoble, France
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
| | - Adam K. Sieradzan
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Celina Sikorska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Esben Trabjerg
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, 8093 Zurich, Switzerland
| | - Andras Fiser
- Department of Systems and Computational Biology, and Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| |
Collapse
|
3
|
Iacobucci C, Schäfer M, Sinz A. Free radical-initiated peptide sequencing (FRIPS)-based cross-linkers for improved peptide and protein structure analysis. MASS SPECTROMETRY REVIEWS 2019; 38:187-201. [PMID: 29660147 DOI: 10.1002/mas.21568] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 03/09/2018] [Indexed: 06/08/2023]
Abstract
Free radical-initiated peptide sequencing (FRIPS) has recently been introduced as an analytical strategy to create peptide radical ions in a predictable and effective way by collisional activation of specifically modified peptides ions. FRIPS is based on the unimolecular dissociation of open-shell ions and yields fragments that resemble those obtained by electron capture dissociation (ECD) or electron transfer dissociation (ETD). In this review article, we describe the fundamentals of FRIPS and highlight its fruitful combination with chemical cross-linking/mass spectrometry (MS) as a highly promising option to derive complementary structural information of peptides and proteins. FRIPS does not only yield exhaustive sequence information of cross-linked peptides, but also defines the exact cross-linking sites of the connected peptides. The development of more advanced FRIPS cross-linkers that extend the FRIPS-based cross-linking/MS approach to the study of large protein assemblies and protein interaction networks can be eagerly anticipated.
Collapse
Affiliation(s)
- Claudio Iacobucci
- Department of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Halle (Saale), D-06120, Germany
| | - Mathias Schäfer
- Department of Chemistry, Institute of Organic Chemistry, University of Cologne, Cologne, D-50939, Germany
| | - Andrea Sinz
- Department of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Halle (Saale), D-06120, Germany
| |
Collapse
|
4
|
Lenz S, Giese SH, Fischer L, Rappsilber J. In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides. J Proteome Res 2018; 17:3923-3931. [PMID: 30293428 PMCID: PMC6279313 DOI: 10.1021/acs.jproteome.8b00600] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2018] [Indexed: 01/01/2023]
Abstract
Cross-linking/mass spectrometry has undergone a maturation process akin to standard proteomics by adapting key methods such as false discovery rate control and quantification. A poorly evaluated search setting in proteomics is the consideration of multiple (lighter) alternative values for the monoisotopic precursor mass to compensate for possible misassignments of the monoisotopic peak. Here, we show that monoisotopic peak assignment is a major weakness of current data handling approaches in cross-linking. Cross-linked peptides often have high precursor masses, which reduces the presence of the monoisotopic peak in the isotope envelope. Paired with generally low peak intensity, this generates a challenge that may not be completely solvable by precursor mass assignment routines. We therefore took an alternative route by '"in-search assignment of the monoisotopic peak" in the cross-link database search tool Xi (Xi-MPA), which considers multiple precursor masses during database search. We compare and evaluate the performance of established preprocessing workflows that partly correct the monoisotopic peak and Xi-MPA on three publicly available data sets. Xi-MPA always delivered the highest number of identifications with ∼2 to 4-fold increase of PSMs without compromising identification accuracy as determined by FDR estimation and comparison to crystallographic models.
Collapse
Affiliation(s)
- Swantje Lenz
- Bioanalytics,
Institute of Biotechnology, Technische Universität
Berlin, 13355 Berlin, Germany
| | - Sven H. Giese
- Bioanalytics,
Institute of Biotechnology, Technische Universität
Berlin, 13355 Berlin, Germany
| | - Lutz Fischer
- Wellcome
Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
| | - Juri Rappsilber
- Bioanalytics,
Institute of Biotechnology, Technische Universität
Berlin, 13355 Berlin, Germany
- Wellcome
Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
| |
Collapse
|
5
|
Cross-linking mass spectrometry: methods and applications in structural, molecular and systems biology. Nat Struct Mol Biol 2018; 25:1000-1008. [PMID: 30374081 DOI: 10.1038/s41594-018-0147-0] [Citation(s) in RCA: 201] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 09/19/2018] [Indexed: 01/11/2023]
Abstract
Over the past decade, cross-linking mass spectrometry (CLMS) has developed into a robust and flexible tool that provides medium-resolution structural information. CLMS data provide a measure of the proximity of amino acid residues and thus offer information on the folds of proteins and the topology of their complexes. Here, we highlight notable successes of this technique as well as common pipelines. Novel CLMS applications, such as in-cell cross-linking, probing conformational changes and tertiary-structure determination, are now beginning to make contributions to molecular biology and the emerging fields of structural systems biology and interactomics.
Collapse
|
6
|
Chu F, Thornton DT, Nguyen HT. Chemical cross-linking in the structural analysis of protein assemblies. Methods 2018; 144:53-63. [PMID: 29857191 DOI: 10.1016/j.ymeth.2018.05.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/22/2018] [Accepted: 05/25/2018] [Indexed: 12/31/2022] Open
Abstract
For decades, chemical cross-linking of proteins has been an established method to study protein interaction partners. The chemical cross-linking approach has recently been revived by mass spectrometric analysis of the cross-linking reaction products. Chemical cross-linking and mass spectrometric analysis (CXMS) enables the identification of residues that are close in three-dimensional (3D) space but not necessarily close in primary sequence. Therefore, this approach provides medium resolution information to guide de novo structure prediction, protein interface mapping and protein complex model building. The robustness and compatibility of the CXMS approach with multiple biochemical methods have made it especially appealing for challenging systems with multiple biochemical compositions and conformation states. This review provides an overview of the CXMS approach, describing general procedures in sample processing, data acquisition and analysis. Selection of proper chemical cross-linking reagents, strategies for cross-linked peptide identification, and successful application of CXMS in structural characterization of proteins and protein complexes are discussed.
Collapse
Affiliation(s)
- Feixia Chu
- Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 03824, United States; Hubbard Center for Genome Studies, University of New Hampshire, Durham, NH 03824, United States.
| | - Daniel T Thornton
- Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 03824, United States
| | - Hieu T Nguyen
- Department of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH 03824, United States
| |
Collapse
|
7
|
Sinz A. Cross‐Linking/Mass Spectrometry for Studying Protein Structures and Protein–Protein Interactions: Where Are We Now and Where Should We Go from Here? Angew Chem Int Ed Engl 2018; 57:6390-6396. [DOI: 10.1002/anie.201709559] [Citation(s) in RCA: 126] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 12/06/2017] [Indexed: 01/13/2023]
Affiliation(s)
- Andrea Sinz
- Department of Pharmaceutical Chemistry & Bioanalytics, Institute of PharmacyMartin Luther University Halle-Wittenberg Wolfgang-Langenbeck-Str. 4 06120 Halle (Saale) Germany
| |
Collapse
|
8
|
Sinz A. Vernetzung/Massenspektrometrie zur Untersuchung von Proteinstrukturen und Protein‐Protein‐Wechselwirkungen: Wo stehen wir und welchen Weg wollen wir einschlagen? Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201709559] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Andrea Sinz
- Abteilung für Pharmazeutische Chemie & BioanalytikInstitut für PharmazieMartin-Luther-Universität Halle-Wittenberg Wolfgang-Langenbeck-Straße 4 06120 Halle (Saale) Deutschland
| |
Collapse
|
9
|
Iacobucci C, Götze M, Piotrowski C, Arlt C, Rehkamp A, Ihling C, Hage C, Sinz A. Carboxyl-Photo-Reactive MS-Cleavable Cross-Linkers: Unveiling a Hidden Aspect of Diazirine-Based Reagents. Anal Chem 2018; 90:2805-2809. [DOI: 10.1021/acs.analchem.7b04915] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Claudio Iacobucci
- Department of Pharmaceutical Chemistry
and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Wolfgang-Langenbeck-Strasse 4, Halle/Saale D-06120, Germany
| | - Michael Götze
- Department of Pharmaceutical Chemistry
and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Wolfgang-Langenbeck-Strasse 4, Halle/Saale D-06120, Germany
| | - Christine Piotrowski
- Department of Pharmaceutical Chemistry
and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Wolfgang-Langenbeck-Strasse 4, Halle/Saale D-06120, Germany
| | - Christian Arlt
- Department of Pharmaceutical Chemistry
and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Wolfgang-Langenbeck-Strasse 4, Halle/Saale D-06120, Germany
| | - Anne Rehkamp
- Department of Pharmaceutical Chemistry
and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Wolfgang-Langenbeck-Strasse 4, Halle/Saale D-06120, Germany
| | - Christian Ihling
- Department of Pharmaceutical Chemistry
and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Wolfgang-Langenbeck-Strasse 4, Halle/Saale D-06120, Germany
| | - Christoph Hage
- Department of Pharmaceutical Chemistry
and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Wolfgang-Langenbeck-Strasse 4, Halle/Saale D-06120, Germany
| | - Andrea Sinz
- Department of Pharmaceutical Chemistry
and Bioanalytics, Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Wolfgang-Langenbeck-Strasse 4, Halle/Saale D-06120, Germany
| |
Collapse
|
10
|
Ogorzalek TL, Hura GL, Belsom A, Burnett KH, Kryshtafovych A, Tainer JA, Rappsilber J, Tsutakawa SE, Fidelis K. Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy. Proteins 2018; 86 Suppl 1:202-214. [PMID: 29314274 DOI: 10.1002/prot.25452] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 12/18/2017] [Accepted: 01/01/2018] [Indexed: 12/13/2022]
Abstract
Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution.
Collapse
Affiliation(s)
- Tadeusz L Ogorzalek
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Greg L Hura
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Adam Belsom
- Wellcome Centre for Cell Biology, Institute of Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, U.K
| | - Kathryn H Burnett
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Andriy Kryshtafovych
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, CA, 95616, USA
| | - John A Tainer
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA.,Department of Molecular and Cellular Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Juri Rappsilber
- Wellcome Centre for Cell Biology, Institute of Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, U.K.,Chair of Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Susan E Tsutakawa
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Krzysztof Fidelis
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, CA, 95616, USA
| |
Collapse
|
11
|
Protein Tertiary Structure by Crosslinking/Mass Spectrometry. Trends Biochem Sci 2018; 43:157-169. [PMID: 29395654 PMCID: PMC5854373 DOI: 10.1016/j.tibs.2017.12.006] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 12/19/2017] [Accepted: 12/21/2017] [Indexed: 12/21/2022]
Abstract
Observing the structures of proteins within the cell and tracking structural changes under different cellular conditions are the ultimate challenges for structural biology. This, however, requires an experimental technique that can generate sufficient data for structure determination and is applicable in the native environment of proteins. Crosslinking/mass spectrometry (CLMS) and protein structure determination have recently advanced to meet these requirements and crosslinking-driven de novo structure determination in native environments is now possible. In this opinion article, we highlight recent successes in the field of CLMS with protein structure modeling and challenges it still holds. The earliest structural studies on proteins using crosslinking/mass spectrometry aimed to elucidate their tertiary three-dimensional structure. Tertiary structure modeling using crosslinking fell out of favor for almost two decades because crosslink data were not informative to aid structure modeling. Two game-changing trends emerged: using short-range crosslinkers that capture relevant modeling information and high-density crosslinking. High-density crosslinking uses unspecific crosslinkers to dramatically increase crosslink numbers. In addition, computational structure modeling methods made significant progress in exploiting CLMS data. The combination of high-density crosslinking and computational structure modeling enables the elucidation of tertiary protein structure in native environments. This sidesteps the key limitation of today’s structure determination methods, which are unable (except for a few, specialized methods) to probe the structure of proteins in cell lysates or even intact cells.
Collapse
|