Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Charlotte Adams, Wassim Gabriel, Kris Laukens, Mario Picciani, Mathias Wilhelm, Wout Bittremieux () and Kurt Boonen ()
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Charlotte Adams: University of Antwerp
Wassim Gabriel: Technical University of Munich
Kris Laukens: University of Antwerp
Mario Picciani: Technical University of Munich
Mathias Wilhelm: Technical University of Munich
Wout Bittremieux: University of Antwerp
Kurt Boonen: University of Antwerp

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.

Date: 2024
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DOI: 10.1038/s41467-024-48322-0

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