DeepPeptide predicts cleaved peptides in proteins using conditional random fields.
Bioinformatics
; 39(10)2023 10 03.
Article
en En
| MEDLINE
| ID: mdl-37812217
ABSTRACT
MOTIVATION Peptides are ubiquitous throughout life and involved in a wide range of biological processes, ranging from neural signaling in higher organisms to antimicrobial peptides in bacteria. Many peptides are generated post-translationally by cleavage of precursor proteins and can thus not be detected directly from genomics data, as the specificities of the responsible proteases are often not completely understood. RESULTS:
We present DeepPeptide, a deep learning model that predicts cleaved peptides directly from the amino acid sequence. DeepPeptide shows both improved precision and recall for peptide detection compared to previous methodology. We show that the model is capable of identifying peptides in underannotated proteomes. AVAILABILITY AND IMPLEMENTATION DeepPeptide is available online at ku.biolib.com/DeepPeptide.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Péptido Hidrolasas
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Péptidos
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2023
Tipo del documento:
Article