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 len...

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Bibliographic Details
Published in:Biomedicines 2021-03, Vol.9 (3), p.276
Main Authors: Casey, Rory, Adelfio, Alessandro, Connolly, Martin, Wall, Audrey, Holyer, Ian, Khaldi, Nora
Format: Article
Language:English
Subjects:
Amino acids
Artificial intelligence
Biological activity
Blood glucose
Chronic illnesses
Diabetes
Diabetes mellitus
drug discovery
Fatty liver
Glucagon
Glucose
Glucose transporter
Hemoglobin
Insulin
Kinases
Laboratory animals
Learning algorithms
Machine learning
peptide
Peptide hormones
Peptides
Steatosis
type 2 diabetes
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Summary: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.
ISSN:2227-9059
2227-9059
DOI:10.3390/biomedicines9030276