Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency.

ABSTRACT: Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treatment of type 2 diabetes, obesity and their associated complications. Candidates must have high potency at both receptors, but it is unclear whether the limited experimental data available can be used to train models that accurately predict the activity at both receptors of new peptide variants. Here we use peptide sequence data labelled with in vitro potency at human GCGR and GLP-1R to train several models, including a deep multi-task neural-network model using multiple loss optimization. Model-guided sequence optimization was used to design three groups of peptide variants, with distinct ranges of predicted dual activity. We found that three

SUBMITTER: Puszkarska AM 

PROVIDER: S-EPMC11374683 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature