DeepPeptide predicts cleaved peptides in proteins using conditional random fields.
Ontology highlight
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.
SUBMITTER: Teufel F
PROVIDER: S-EPMC10585352 | biostudies-literature | 2023 Oct
REPOSITORIES: biostudies-literature
Publications
DeepPeptide predicts cleaved peptides in proteins using conditional random fields.
Teufel Felix F Refsgaard Jan Christian JC Madsen Christian Toft CT Stahlhut Carsten C Grønborg Mads M Winther Ole O Madsen Dennis D
Bioinformatics (Oxford, England) 20231001 10
<h4>Motivation</h4>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.<h4>Results</h4>We present DeepPeptide, a deep learning model that pred ...[more]
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