Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry

Autor: Sven H. Giese, Ludwig R. Sinn, Fritz Wegner, Juri Rappsilber
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
Druh dokumentu: article
ISSN: 2041-1723
DOI: 10.1038/s41467-021-23441-0
Popis: Predicting chromatographic retention times (RTs) has proven beneficial in proteomics but has not yet been achieved for crosslinked peptides. Here, the authors develop an RT prediction tool for crosslinked peptides and leverage predicted RTs to increase identifications in crosslinking mass spectrometry studies.
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