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Puliasis SS, Lewandowska D, Hemsley PA, Zhang R. ProtView: A Versatile Tool for In Silico Protease Evaluation and Selection in a Proteomic and Proteogenomic Context. J Proteome Res 2023. [PMID: 37248202 DOI: 10.1021/acs.jproteome.3c00135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Many tools have been created to generate in silico proteome digests with different protease enzymes and provide useful information for selecting optimal digest schemes for specific needs. This can save on time and resources and generate insights on the observable proteome. However, there remains a need for a tool that evaluates digest schemes beyond protein and amino acid coverages in the proteomic domain. Here, we present ProtView, a versatile in silico protease combination digest evaluation workflow that maps in silico-digested peptides to both protein and genome references, so that the potential observable portions of the proteome, transcriptome, and genome can be identified. The proteomic identification and quantification of evidence for transcriptional, co-transcriptional, post-transcriptional, translational, and post-translational regulation can all be examined in silico with ProtView prior to an experiment. Benchmarking against biological data comparing multiple proteases shows that ProtView can correctly estimate performances among the digest schemes. ProtView provides this information in a way that is easy to interpret, allowing for digest schemes to be evaluated before carrying out an experiment, in context that can optimize both proteomic and proteogenomic experiments. ProtView is available at https://github.com/SSPuliasis/ProtView.
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Affiliation(s)
- Sophia S Puliasis
- Division of Plant Sciences, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland, UK
- Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK
| | - Dominika Lewandowska
- Cell and Molecular Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK
| | - Piers A Hemsley
- Division of Plant Sciences, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland, UK
- Cell and Molecular Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK
| | - Runxuan Zhang
- Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK
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Richards AL, Chen KH, Wilburn DB, Stevenson E, Polacco BJ, Searle BC, Swaney DL. Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes. J Proteome Res 2022; 21:1124-1136. [PMID: 35234472 PMCID: PMC9035370 DOI: 10.1021/acs.jproteome.1c00960] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments; however, due to the additional analysis time required, it has not been widely adopted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 45% increase in nonredundant amino acid detections. Nontryptic peptides enabled noncanonical protein isoform determination and resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as protein isoform analysis.
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Affiliation(s)
- Alicia L Richards
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
| | - Kuei-Ho Chen
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
| | - Damien B Wilburn
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio 43210, United States.,Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, United States.,Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Erica Stevenson
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
| | - Benjamin J Polacco
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
| | - Brian C Searle
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio 43210, United States.,Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, United States
| | - Danielle L Swaney
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
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Gerritsen JS, White FM. Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells. Expert Rev Proteomics 2021; 18:661-674. [PMID: 34468274 PMCID: PMC8628306 DOI: 10.1080/14789450.2021.1976152] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 08/31/2021] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Many pathologies, including cancer, have been associated with aberrant phosphorylation-mediated signaling networks that drive altered cell proliferation, migration, metabolic regulation, and can lead to systemic inflammation. Phosphoproteomics, the large-scale analysis of protein phosphorylation sites, has emerged as a powerful tool to define signaling network regulation and dysregulation in normal and pathological conditions. AREAS COVERED We provide an overview of methodology for global phosphoproteomics as well as enrichment of specific subsets of the phosphoproteome, including phosphotyrosine and phospho-motif enrichment of kinase substrates. We review quantitative methods, advantages and limitations of different mass spectrometry acquisition formats, and computational approaches to extract biological insight from phosphoproteomics data. Throughout, we discuss various applications and their challenges in implementation. EXPERT OPINION Over the past 20 years the field of phosphoproteomics has advanced to enable deep biological and clinical insight through the quantitative analysis of signaling networks. Future areas of development include Clinical Laboratory Improvement Amendments (CLIA)-approved methods for analysis of clinical samples, continued improvements in sensitivity to enable analysis of small numbers of rare cells and tissue microarrays, and computational methods to integrate data resulting from multiple systems-level quantitative analytical methods.
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Affiliation(s)
- Jacqueline S Gerritsen
- Koch Institute for Integrative Cancer Research; Center for Precision Cancer Medicine; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, U.S.A
| | - Forest M White
- Koch Institute for Integrative Cancer Research; Center for Precision Cancer Medicine; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, U.S.A
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Fossati A, Richards AL, Chen KH, Jaganath D, Cattamanchi A, Ernst JD, Swaney DL. Toward Comprehensive Plasma Proteomics by Orthogonal Protease Digestion. J Proteome Res 2021; 20:4031-4040. [PMID: 34319755 DOI: 10.1021/acs.jproteome.1c00357] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Rapid and consistent protein identification across large clinical cohorts is an important goal for clinical proteomics. With the development of data-independent technologies (DIA/SWATH-MS), it is now possible to analyze hundreds of samples with great reproducibility and quantitative accuracy. However, this technology benefits from empirically derived spectral libraries that define the detectable set of peptides and proteins. Here, we apply a simple and accessible tip-based workflow for the generation of spectral libraries to provide a comprehensive overview on the plasma proteome in individuals with and without active tuberculosis (TB). To boost protein coverage, we utilized nonconventional proteases such as GluC and AspN together with the gold standard trypsin, identifying more than 30,000 peptides mapping to 3309 proteins. Application of this library to quantify plasma proteome differences in TB infection recovered more than 400 proteins in 50 min of MS acquisition, including diagnostic Mycobacterium tuberculosis (Mtb) proteins that have previously been detectable primarily by antibody-based assays and intracellular proteins not previously described to be in plasma.
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Affiliation(s)
- Andrea Fossati
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States
| | - Alicia L Richards
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States
| | - Kuei-Ho Chen
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States
| | - Devan Jaganath
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, California 94158, United States.,Center for Tuberculosis, University of California San Francisco, San Francisco, California 94158, United States.,Department of Pediatrics, Division of Pediatric Infectious Diseases, University of California San Francisco, San Francisco, California 94158, United States
| | - Adithya Cattamanchi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, California 94158, United States.,Center for Tuberculosis, University of California San Francisco, San Francisco, California 94158, United States.,Department of Pediatrics, Division of Pediatric Infectious Diseases, University of California San Francisco, San Francisco, California 94158, United States
| | - Joel D Ernst
- Department of Medicine, Division of Experimental Medicine, University of California San Francisco, San Francisco, California 94143, United States
| | - Danielle L Swaney
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California 94158, United States.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States.,J. David Gladstone Institutes, San Francisco, California 94158, United States
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