Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF.
Nat Commun
; 15(1): 3956, 2024 May 10.
Article
en En
| MEDLINE
| ID: mdl-38730277
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Péptidos
/
Espectrometría de Masas en Tándem
/
Aprendizaje Profundo
Límite:
Humans
Idioma:
En
Año:
2024
Tipo del documento:
Article