DeepLC can predict retention times for peptides that carry as-yet unseen modifications.
Nat Methods
; 18(11): 1363-1369, 2021 11.
Article
en En
| MEDLINE
| ID: mdl-34711972
The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC's ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Fragmentos de Péptidos
/
Algoritmos
/
Proteínas
/
Procesamiento Proteico-Postraduccional
/
Proteoma
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Nat Methods
Asunto de la revista:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Año:
2021
Tipo del documento:
Article
País de afiliación:
Bélgica