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Title: | Predicting the number of sulfur atoms in peptides and small proteins based on the observed aggregated isotope distribution | Authors: | CLAESEN, Jurgen VALKENBORG, Dirk BURZYKOWSKI, Tomasz |
Issue Date: | 2021 | Publisher: | WILEY | Source: | RCM. Rapid communications in mass spectrometry, 35 (19) (Art N° e9162) | Abstract: | Rationale Identification of peptides and proteins is a challenging task in mass spectrometry-based proteomics. Knowledge of the number of sulfur atoms can improve the identification of peptides and proteins. Methods In this article, we propose a method for the prediction of S-atoms based on the aggregated isotope distribution. The Mahalanobis distance is used as dissimilarity measure to compare mass- and intensity-based features from the observed and theoretical isotope distributions. Results The relative abundance of the second and the third aggregated isotopic variants (as compared to the monoisotopic one) and the mass difference between the second and third aggregated isotopic variants are the most important features to predict the number of S-atoms. Conclusions The mass and intensity accuracies of the observed aggregated isotopic variants are insufficient to accurately predict the number of atoms. However, using a limited set of predictions for a peptide, rather than predicting a single number of S-atoms, has a reasonably high prediction accuracy. | Notes: | Claesen, J (corresponding author), Vrije Univ Amsterdam, Dept Epidemiol & Data Sci, Amsterdam UMC, Amsterdam, Netherlands. j.claesen@amsterdamumc.nl |
Document URI: | http://hdl.handle.net/1942/35393 | ISSN: | 0951-4198 | e-ISSN: | 1097-0231 | DOI: | 10.1002/rcm.9162 | ISI #: | WOS:000693422600003 | Rights: | 2021 The Authors. Rapid Communications in Mass Spectrometry published by John Wiley & Sons Ltd . This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
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rcm9162.pdf | Published version | 1.46 MB | Adobe PDF | View/Open |
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