Translating L-peptides into non-canonical linear and macrocyclic peptides

30 September 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Peptide-based drug discovery efforts has made significant advances in the recent past, enabling targeting of previously undruggable protein-protein interactions. Current efforts of high-throughput library screening involves L-peptide libraries, while non-canonical linear and macrocyclic peptides have been shown to be more metabolically stable, while having similar or higher biological activity. Here, we present a method to translate L-peptides into their non-canonical variants using a genetic algorithm-based approach. We optimize against a dual objective function of matching the chemical similarity of the mutated sequence to the reference L-peptide, and maximizing the binding affinity, characterized by the docking score against the target protein. We demonstrate the applicability of this method by discovering previously unknown non-canonical linear and macrocyclic peptides with high binding affinity against DRD2 kinase inhibitor. This work will provide a chemistry-informed approach for the discovery of non-canonical peptides from L-peptide library screening, thereby accelerating drug development efforts.


chemical similarity
artificial intelligence
genetic algorithm


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