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http://hdl.handle.net/1942/39892| Title: | Machine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution | Authors: | AGTEN, Annelies CLAESEN, Jurgen BURZYKOWSKI, Tomasz VALKENBORG, Dirk |
Issue Date: | 2023 | Publisher: | Source: | RAPID COMMUNICATIONS IN MASS SPECTROMETRY, 37 (9) (Art N° e9480) | Abstract: | The observed isotope distribution is an important attribute for the identification of peptides and proteins in mass spectrometry-based proteomics. Sulphur atoms have a very distinctive elemental isotope definition and therefore, the presence of Sulphur atoms has a substantial effect on the isotope distribution of biomolecules. Therefore, knowledge on the number of Sulphur atoms can improve identification of peptides and proteins. | Document URI: | http://hdl.handle.net/1942/39892 | ISSN: | 0951-4198 | e-ISSN: | 1097-0231 | DOI: | 10.1002/rcm.9480 | ISI #: | 000945773800001 | Rights: | 2023John Wiley & Sons Ltd | Category: | A1 | Type: | Journal Contribution |
| Appears in Collections: | Research publications |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| rcm9480-sup-0001-appendix s1 (1).docx Restricted Access | Supplementary material | 439.88 kB | Microsoft Word | View/Open Request a copy |
| Rapid Comm Mass Spectrometry - 2023 - Agten - Machine learning approach for the prediction of the number of sulphur atoms.pdf Restricted Access | Peer-reviewed author version | 2.1 MB | Adobe PDF | View/Open Request a copy |
| Rapid Comm Mass Spectrometry - 2023 - Agten - Machine learning approach for the prediction of the number of sulphur atoms.pdf Restricted Access | Published version | 2.1 MB | Adobe PDF | View/Open Request a copy |
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