1
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Lu H, Zhu Z, Fields L, Zhang H, Li L. Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross-Linking. MASS SPECTROMETRY REVIEWS 2024. [PMID: 39300771 DOI: 10.1002/mas.21908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 08/31/2024] [Accepted: 09/02/2024] [Indexed: 09/22/2024]
Abstract
The exploration of protein structure and function stands at the forefront of life science and represents an ever-expanding focus in the development of proteomics. As mass spectrometry (MS) offers readout of protein conformational changes at both the protein and peptide levels, MS-based structural proteomics is making significant strides in the realms of structural and molecular biology, complementing traditional structural biology techniques. This review focuses on two powerful MS-based techniques for peptide-level readout, namely limited proteolysis-mass spectrometry (LiP-MS) and cross-linking mass spectrometry (XL-MS). First, we discuss the principles, features, and different workflows of these two methods. Subsequently, we delve into the bioinformatics strategies and software tools used for interpreting data associated with these protein conformation readouts and how the data can be integrated with other computational tools. Furthermore, we provide a comprehensive summary of the noteworthy applications of LiP-MS and XL-MS in diverse areas including neurodegenerative diseases, interactome studies, membrane proteins, and artificial intelligence-based structural analysis. Finally, we discuss the factors that modulate protein conformational changes. We also highlight the remaining challenges in understanding the intricacies of protein conformational changes by LiP-MS and XL-MS technologies.
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Affiliation(s)
- Haiyan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Zexin Zhu
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lauren Fields
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
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2
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Peng Y, Jain S, Radivojac P. An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics. Bioinformatics 2024; 40:i428-i436. [PMID: 38940171 PMCID: PMC11256928 DOI: 10.1093/bioinformatics/btae233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Cross-linking tandem mass spectrometry (XL-MS/MS) is an established analytical platform used to determine distance constraints between residues within a protein or from physically interacting proteins, thus improving our understanding of protein structure and function. To aid biological discovery with XL-MS/MS, it is essential that pairs of chemically linked peptides be accurately identified, a process that requires: (i) database search, that creates a ranked list of candidate peptide pairs for each experimental spectrum and (ii) false discovery rate (FDR) estimation, that determines the probability of a false match in a group of top-ranked peptide pairs with scores above a given threshold. Currently, the only available FDR estimation mechanism in XL-MS/MS is the target-decoy approach (TDA). However, despite its simplicity, TDA has both theoretical and practical limitations that impact the estimation accuracy and increase run time over potential decoy-free approaches (DFAs). RESULTS We introduce a novel decoy-free framework for FDR estimation in XL-MS/MS. Our approach relies on multi-sample mixtures of skew normal distributions, where the latent components correspond to the scores of correct peptide pairs (both peptides identified correctly), partially incorrect peptide pairs (one peptide identified correctly, the other incorrectly), and incorrect peptide pairs (both peptides identified incorrectly). To learn these components, we exploit the score distributions of first- and second-ranked peptide-spectrum matches for each experimental spectrum and subsequently estimate FDR using a novel expectation-maximization algorithm with constraints. We evaluate the method on ten datasets and provide evidence that the proposed DFA is theoretically sound and a viable alternative to TDA owing to its good performance in terms of accuracy, variance of estimation, and run time. AVAILABILITY AND IMPLEMENTATION https://github.com/shawn-peng/xlms.
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Affiliation(s)
- Yisu Peng
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
| | - Shantanu Jain
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
- The Institute for Experiential AI, Northeastern University, Boston, MA 02115, United States
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
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3
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Misal SA, Zhao B, Reilly JP. Interpretation of Anomalously Long Crosslinks in Ribosome Crosslinking reveals the ribosome interaction in stationary phase E. coli. RSC Chem Biol 2022; 3:886-894. [PMID: 35866168 PMCID: PMC9257603 DOI: 10.1039/d2cb00101b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 05/15/2022] [Indexed: 11/21/2022] Open
Abstract
Crosslinking mass spectrometry (XL-MS) of bacterial ribosomes revealed the dynamic intra and intermolecular interactions within the ribosome structure. It has been also extended to capture the interactions of ribosome binding...
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Affiliation(s)
- Santosh A Misal
- Department of Chemistry, Indiana University 800 East Kirkwood Avenue Bloomington IN 47405 USA
| | - Bingqing Zhao
- Department of Chemistry, Indiana University 800 East Kirkwood Avenue Bloomington IN 47405 USA
| | - James P Reilly
- Department of Chemistry, Indiana University 800 East Kirkwood Avenue Bloomington IN 47405 USA
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4
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Yugandhar K, Zhao Q, Gupta S, Xiong D, Yu H. Progress in methodologies and quality-control strategies in protein cross-linking mass spectrometry. Proteomics 2021; 21:e2100145. [PMID: 34647422 DOI: 10.1002/pmic.202100145] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 10/04/2021] [Indexed: 11/10/2022]
Abstract
Deciphering the interaction networks and structural dynamics of proteins is pivotal to better understanding their biological functions. Cross-linking mass spectrometry (XL-MS) is a powerful and increasingly popular technology that provides information about protein-protein interactions and their structural constraints for individual proteins and multiprotein complexes on a proteome-scale. In this review, we first assess the coverage and depth of the XL-MS technique by utilizing publicly available datasets. We then delve into the progress in XL-MS experimental and computational methodologies and examine different quality-control strategies reported in the literature. Finally, we discuss the progress in XL-MS applications along with the scope for future improvements.
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Affiliation(s)
- Kumar Yugandhar
- Department of Computational Biology, Cornell University, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, New York, USA
| | - Qiuye Zhao
- Department of Computational Biology, Cornell University, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, New York, USA
| | - Shobhita Gupta
- Department of Computational Biology, Cornell University, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, New York, USA
| | - Dapeng Xiong
- Department of Computational Biology, Cornell University, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, New York, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, New York, USA
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5
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Huang R, Zhu W, Xu Z, Chen J, Jiang B, Chen H, Chen W. Accurate Retention Time Prediction Based on Monolinked Peptide Information to Confidently Identify Cross-Linked Peptides. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:2410-2416. [PMID: 34320809 DOI: 10.1021/jasms.1c00120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cross-linking mass spectrometry methods have not been successfully applied to protein-protein interaction discovery at a proteome-wide level mainly due to the computation complexity (O (n2)) issue. In a previous report, we proposed a decision tree searching strategy (DTSS), which can reduce complexity by orders of magnitude. In this study, we further found that the monolinked peptides carry out the information on the retention time of the corresponding cross-linked pairs; therefore, the retention time of cross-linked peptide pairs can be predicted accurately. By utilizing the retention time as an extra filter, the false positive rate can be reduced by around 86% with a sensitivity loss of 10%. The method combined with DTSS (T-DTSS) not only benefits improving identification confidence but also leads to lower cutoff scores and facilitates substantially increasing inter-cross-link identification. T-DTSS was successfully applied to the identification of inter-cross-links obtained from Escherichia coli cell lysate cross-linked by a newly synthesized enrichable cross-linker, pDSBE. The approach can be applicable to both cleavable and noncleavable methods.
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Affiliation(s)
- Rong Huang
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Wei Zhu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Zili Xu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Jiakang Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Biao Jiang
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Hongli Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Wenzhang Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
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6
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Kyrilis FL, Belapure J, Kastritis PL. Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective. Front Mol Biosci 2021; 8:660542. [PMID: 33937337 PMCID: PMC8082361 DOI: 10.3389/fmolb.2021.660542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein-protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural signatures and subsequently reconstructed cryo-EM maps, and, ultimately, characterizing unidentified protein communities at high resolution. In this review article, we summarize recent literature in detecting protein communities from native cell extracts and identify the remaining challenges and opportunities. We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein communities within native cell extracts.
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Affiliation(s)
- Fotis L. Kyrilis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Jaydeep Belapure
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Panagiotis L. Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Biozentrum, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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7
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Peng Y, Jain S, Li YF, Greguš M, Ivanov AR, Vitek O, Radivojac P. New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics. Bioinformatics 2020; 36:i745-i753. [PMID: 33381824 DOI: 10.1093/bioinformatics/btaa807] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra. RESULTS We introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms. AVAILABILITYAND IMPLEMENTATION https://github.com/shawn-peng/FDR-estimation. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yisu Peng
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | - Shantanu Jain
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | | | - Michal Greguš
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.,Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA 02115, USA
| | - Alexander R Ivanov
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.,Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA 02115, USA
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.,Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA 02115, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.,Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.,Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA 02115, USA
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8
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Huang R, Gao X, Xu Z, Zhu W, Wei D, Jiang B, Chen H, Chen W. Decision Tree Searching Strategy to Boost the Identification of Cross-Linked Peptides. Anal Chem 2020; 92:13702-13710. [DOI: 10.1021/acs.analchem.0c00452] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Rong Huang
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Xiuxia Gao
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Zili Xu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Wei Zhu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Ding Wei
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Biao Jiang
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Hongli Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Wenzhang Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
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9
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Iacobucci I, Monaco V, Cozzolino F, Monti M. From classical to new generation approaches: An excursus of -omics methods for investigation of protein-protein interaction networks. J Proteomics 2020; 230:103990. [PMID: 32961344 DOI: 10.1016/j.jprot.2020.103990] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/11/2020] [Accepted: 08/31/2020] [Indexed: 01/24/2023]
Abstract
Functional Proteomics aims to the identification of in vivo protein-protein interaction (PPI) in order to piece together protein complexes, and therefore, cell pathways involved in biological processes of interest. Over the years, proteomic approaches used for protein-protein interaction investigation have relied on classical biochemical protocols adapted to a global overview of protein-protein interactions, within so-called "interactomics" investigation. In particular, their coupling with advanced mass spectrometry instruments and innovative analytical methods led to make great strides in the PPIs investigation in proteomics. In this review, an overview of protein complexes purification strategies, from affinity purification approaches, including proximity-dependent labeling techniques and cross-linking strategy for the identification of transient interactions, to Blue Native Gel Electrophoresis (BN-PAGE) and Size Exclusion Chromatography (SEC) employed in the "complexome profiling", has been reported, giving a look to their developments, strengths and weakness and providing to readers several recent applications of each strategy. Moreover, a section dedicated to bioinformatic databases and platforms employed for protein networks analyses was also included.
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Affiliation(s)
- Ilaria Iacobucci
- Department of Chemical Sciences, University Federico II of Naples, Strada Comunale Cinthia, 26, 80126 Naples, Italy; CEINGE Advanced Biotechnologies, Via G. Salvatore 486, 80145 Naples, Italy
| | - Vittoria Monaco
- CEINGE Advanced Biotechnologies, Via G. Salvatore 486, 80145 Naples, Italy
| | - Flora Cozzolino
- Department of Chemical Sciences, University Federico II of Naples, Strada Comunale Cinthia, 26, 80126 Naples, Italy; CEINGE Advanced Biotechnologies, Via G. Salvatore 486, 80145 Naples, Italy.
| | - Maria Monti
- Department of Chemical Sciences, University Federico II of Naples, Strada Comunale Cinthia, 26, 80126 Naples, Italy; CEINGE Advanced Biotechnologies, Via G. Salvatore 486, 80145 Naples, Italy.
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10
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Na S, Paek E. Computational methods in mass spectrometry-based structural proteomics for studying protein structure, dynamics, and interactions. Comput Struct Biotechnol J 2020; 18:1391-1402. [PMID: 32637038 PMCID: PMC7322682 DOI: 10.1016/j.csbj.2020.06.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 06/01/2020] [Accepted: 06/01/2020] [Indexed: 12/28/2022] Open
Abstract
Mass spectrometry (MS) has made enormous contributions to comprehensive protein identification and quantification in proteomics. MS is also gaining momentum for structural biology in a variety of ways, complementing conventional structural biology techniques. Here, we will review how MS-based techniques, such as hydrogen/deuterium exchange, covalent labeling, and chemical cross-linking, enable the characterization of protein structure, dynamics, and interactions, especially from a perspective of their data analyses. Structural information encoded by chemical probes in intact proteins is decoded by interpreting MS data at a peptide level, i.e., revealing conformational and dynamic changes in local regions of proteins. The structural MS data are not amenable to data analyses in traditional proteomics workflow, requiring dedicated software for each type of data. We first provide basic principles of data interpretation, including isotopic distribution and peptide sequencing. We then focus particularly on computational methods for structural MS data analyses and discuss outstanding challenges in a proteome-wide large scale analysis.
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Affiliation(s)
- Seungjin Na
- Dept. of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Eunok Paek
- Dept. of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
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11
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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.
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12
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Lu L, Millikin RJ, Solntsev SK, Rolfs Z, Scalf M, Shortreed MR, Smith LM. Identification of MS-Cleavable and Noncleavable Chemically Cross-Linked Peptides with MetaMorpheus. J Proteome Res 2018; 17:2370-2376. [PMID: 29793340 DOI: 10.1021/acs.jproteome.8b00141] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Protein chemical cross-linking combined with mass spectrometry has become an important technique for the analysis of protein structure and protein-protein interactions. A variety of cross-linkers are well developed, but reliable, rapid, and user-friendly tools for large-scale analysis of cross-linked proteins are still in need. Here we report MetaMorpheusXL, a new search module within the MetaMorpheus software suite that identifies both MS-cleavable and noncleavable cross-linked peptides in MS data. MetaMorpheusXL identifies MS-cleavable cross-linked peptides with an ion-indexing algorithm, which enables an efficient large database search. The identification does not require the presence of signature fragment ions, an advantage compared with similar programs such as XlinkX. One complication associated with the need for signature ions from cleavable cross-linkers such as DSSO (disuccinimidyl sulfoxide) is the requirement for multiple fragmentation types and energy combinations, which is not necessary for MetaMorpheusXL. The ability to perform proteome-wide analysis is another advantage of MetaMorpheusXL compared with programs such as MeroX and DXMSMS. MetaMorpheusXL is also faster than other currently available MS-cleavable cross-link search software programs. It is imbedded in MetaMorpheus, an open-source and freely available software suite that provides a reliable, fast, user-friendly graphical user interface that is readily accessible to researchers.
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13
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Thurston BA, Ferguson AL. Machine learning and molecular design of self-assembling -conjugated oligopeptides. MOLECULAR SIMULATION 2018. [DOI: 10.1080/08927022.2018.1469754] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
- Bryce A. Thurston
- Department of Physics, University of Illinois at Urbana-Champaign , Urbana, IL, USA
| | - Andrew L. Ferguson
- Department of Physics, University of Illinois at Urbana-Champaign , Urbana, IL, USA
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign , Urbana, IL, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign , Urbana, IL, USA
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14
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Yu C, Huang L. Cross-Linking Mass Spectrometry: An Emerging Technology for Interactomics and Structural Biology. Anal Chem 2018; 90:144-165. [PMID: 29160693 PMCID: PMC6022837 DOI: 10.1021/acs.analchem.7b04431] [Citation(s) in RCA: 222] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Clinton Yu
- Department of Physiology & Biophysics, University of California, Irvine, Irvine, CA 92697
| | - Lan Huang
- Department of Physiology & Biophysics, University of California, Irvine, Irvine, CA 92697
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15
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Borman RP, Wang Y, Nguyen MD, Ganesana M, Lee ST, Venton BJ. Automated Algorithm for Detection of Transient Adenosine Release. ACS Chem Neurosci 2017; 8:386-393. [PMID: 28196418 DOI: 10.1021/acschemneuro.6b00262] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Spontaneous adenosine release events have been discovered in the brain that last only a few seconds. The identification of these adenosine events from fast-scan cyclic voltammetry (FSCV) data is difficult due to the random nature of adenosine release. In this study, we develop an algorithm that automatically identifies and characterizes adenosine transient features, including event time, concentration, and duration. Automating the data analysis reduces analysis time from 10 to 18 h to about 40 min per experiment. The algorithm identifies adenosine based on its two oxidation peaks, the time delay between them, and their current vs time peak ratios. In order to validate the program, four data sets from three independent researchers were analyzed by the algorithm and then compared to manual identification by an analyst. The algorithm resulted in 10 ± 4% false negatives and 9 ± 3% false positives. The specificity of the algorithm was verified by comparing calibration data for adenosine triphosphate (ATP), histamine, hydrogen peroxide, and pH changes and these analytes were not identified as adenosine. Stimulated histamine release in vivo was also not identified as adenosine. The code is modular in design and could be easily adjusted to detect features of spontaneous dopamine or other neurochemical transients in FSCV data.
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Affiliation(s)
- Ryan P. Borman
- Department of Chemistry, University of Virginia, Charlottesville, Virginia 22904, United States
| | - Ying Wang
- Department of Chemistry, University of Virginia, Charlottesville, Virginia 22904, United States
| | - Michael D. Nguyen
- Department of Chemistry, University of Virginia, Charlottesville, Virginia 22904, United States
| | | | - Scott T. Lee
- Department of Chemistry, University of Virginia, Charlottesville, Virginia 22904, United States
| | - B. Jill Venton
- Department of Chemistry, University of Virginia, Charlottesville, Virginia 22904, United States
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16
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Tinnefeld V, Venne AS, Sickmann A, Zahedi RP. Enrichment of Cross-Linked Peptides Using Charge-Based Fractional Diagonal Chromatography (ChaFRADIC). J Proteome Res 2017; 16:459-469. [DOI: 10.1021/acs.jproteome.6b00587] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Verena Tinnefeld
- Leibniz-Institut
für Analytische Wissenschaften - ISAS - e.V., Dortmund 44227, Germany
| | - A. Saskia Venne
- Leibniz-Institut
für Analytische Wissenschaften - ISAS - e.V., Dortmund 44227, Germany
| | - Albert Sickmann
- Leibniz-Institut
für Analytische Wissenschaften - ISAS - e.V., Dortmund 44227, Germany
- Department
of Chemistry, College of Physical Sciences, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom
- Medizinisches
Proteom Center, Ruhr Universität Bochum, Bochum 44801, Germany
| | - René P. Zahedi
- Leibniz-Institut
für Analytische Wissenschaften - ISAS - e.V., Dortmund 44227, Germany
| |
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