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Updated: Jul 14, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
DeepPeptide predicts cleaved peptides in proteins using conditional random fields
Felix Teufel1,2, Jan Christian Refsgaard2, Christian Toft Madsen3
1Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, Copenhagen 2200, Denmark.
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.
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