DeepPeptide predicts cleaved peptides in proteins using conditional random fields

Felix Teufel, Jan Christian Refsgaard, Christian Toft Madsen, Carsten Stahlhut, Mads Grønborg, Ole Winther, Dennis Madsen

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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.

Original languageEnglish
Article numberbtad616
JournalBioinformatics (Oxford, England)
Volume39
Issue number10
Number of pages6
ISSN1367-4811
DOIs
Publication statusPublished - 2023

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Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press.

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