Discovery through Machine Learning and Preclinical Validation of Novel Anti-Diabetic Peptides

While there have been significant advances in drug discovery for diabetes mellitus over the past couple of decades, there is an opportunity and need for improved therapies. While type 2 diabetic patients better manage their illness, many of the therapeutics in this area are peptide hormones with lengthy sequences and a molecular structure that makes them challenging and expensive to produce. Using machine learning, we present novel anti-diabetic peptides which are less than 16 amino acids in length, distinct from human signalling peptides. We validate the capacity of these peptides to stimulate glucose uptake and Glucose transporter type 4 (GLUT4) translocation in vitro. In obese insulin-resistant mice, predicted peptides significantly lower plasma glucose, reduce glycated haemoglobin and even improve hepatic steatosis when compared to treatments currently in use in a clinical setting. These unoptimised, linear peptides represent promising candidates for blood glucose regulation which require further evaluation. Further, this indicates that perhaps we have overlooked the class of natural short linear peptides, which usually come with an excellent safety profile, as therapeutic modalities.

Automated summary

Novel anti-diabetic peptides under 16 amino acids in length have shown promising effects in stimulating glucose uptake and improving liver fat deposition in obese insulin-resistant mice, suggesting a potential therapy for blood glucose regulation. Further studies are needed to evaluate the efficacy and safety of these short linear peptides as therapeutic modalities.