Prediction of peptide cleavage sites using protein language models and graph neural networks.

ABSTRACT: The growing interest in using peptide molecules as therapeutic agents, driven by their high selectivity and efficacy, has become a significant trend in the pharmaceutical industry. However, their oral administration remains challenging due to their low bioavailability and vulnerability to proteases, which produce the cleavage of peptide bonds. To optimize peptide drug development, in silico tools based on machine learning algorithms have been developed for site of cleavage prediction. These tools, which rely on manual feature extraction, have limitations in capturing complex peptide structures, especially those involving non-natural amino acids or cyclic peptides. This study presents two novel in silico approaches for cleavage site prediction. The first approach uses protein language model

SUBMITTER: Cifuentes P 

PROVIDER: S-EPMC12575701 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature