Data processing and analysis in positional proteomics

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Abstract

Proteolytic cleavage is an irreversible post translational modification (PTM), and dysregulation of protease activity is often a hallmark in disease. Aberrant proteolysis can alter protein abundance or function, disturbing cellular state and resulting in disease specific biomarkers or therapeutic targets. Positional proteomics facilitates global identification and precise quantification of position-specific peptides such as those located N- or C-terminal in the protein sequence. These techniques enable the study of both natural and protease generated protein termini, as well as associated PTMs. Despite its importance, proteolysis remains understudied due to experimental challenges and complex data processing. In this review, we outline key strategies for data analysis and processing in positional proteomics, emphasizing how identification, quantification, and interpretation of proteolytic cleavage sites differs from standard proteomics data analysis pipelines. We discuss differences in common approaches for terminomics-focused workflows, comparing N- vs. C-terminomics as well as different labeling strategies and acquisition methods. Additionally, we highlight considerations for proper normalization approaches, specifically the need to normalize cleavage abundances relative to protein and/or protease abundance, and we explain the importance of integrating structural data, solvent accessibility, and tissue expression profiles during data analysis to better evaluate the biological significance of experimental results.
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Data processing and analysis in positional proteomics | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL PROTEOMICS This is a preprint and has not been peer reviewed. Data may be preliminary. 2 June 2025 V1 Latest version Share on Data processing and analysis in positional proteomics Authors : Aleksander Moldt Haack and Konstantinos Kalogeropoulos 0000-0003-3907-9281 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174888419.94715236/v1 Published PROTEOMICS Version of record Peer review timeline 253 views 247 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Proteolytic cleavage is an irreversible post translational modification (PTM), and dysregulation of protease activity is often a hallmark in disease. Aberrant proteolysis can alter protein abundance or function, disturbing cellular state and resulting in disease specific biomarkers or therapeutic targets. Positional proteomics facilitates global identification and precise quantification of position-specific peptides such as those located N- or C-terminal in the protein sequence. These techniques enable the study of both natural and protease generated protein termini, as well as associated PTMs. Despite its importance, proteolysis remains understudied due to experimental challenges and complex data processing. In this review, we outline key strategies for data analysis and processing in positional proteomics, emphasizing how identification, quantification, and interpretation of proteolytic cleavage sites differs from standard proteomics data analysis pipelines. We discuss differences in common approaches for terminomics-focused workflows, comparing N- vs. C-terminomics as well as different labeling strategies and acquisition methods. Additionally, we highlight considerations for proper normalization approaches, specifically the need to normalize cleavage abundances relative to protein and/or protease abundance, and we explain the importance of integrating structural data, solvent accessibility, and tissue expression profiles during data analysis to better evaluate the biological significance of experimental results. Supplementary Material File (review draft_ data analysis and processing in positional proteomics.pdf) Download 3.20 MB Information & Authors Information Version history V1 Version 1 02 June 2025 Peer review timeline Published PROTEOMICS Version of Record 3 Nov 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection PROTEOMICS Authors Affiliations Aleksander Moldt Haack Technical University of Denmark View all articles by this author Konstantinos Kalogeropoulos 0000-0003-3907-9281 [email protected] Technical University of Denmark View all articles by this author Metrics & Citations Metrics Article Usage 253 views 247 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Aleksander Moldt Haack, Konstantinos Kalogeropoulos. Data processing and analysis in positional proteomics. Authorea . 02 June 2025. DOI: https://doi.org/10.22541/au.174888419.94715236/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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