Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes.
Proteomics
; 22(10): e2100226, 2022 05.
Article
em En
| MEDLINE
| ID: mdl-35184383
ABSTRACT
Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry (MS) required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post-translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines - that is, Mascot, Mascot+Percolator, and PEAKS DB - as final identification step, as well as the features of target database on the ability to correctly identify non-spliced and cis-spliced peptides. We used ground truth datasets measured by MS to benchmark methods' performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis-spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Peptídeos
/
Ferramenta de Busca
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article