Computational design of functional cyclic peptides using deep learning

Cyclic peptides have gained significant traction as a therapeutic modality. Given their ease of synthesis, expansive chemical space, and the promising pharmacokinetic properties of existing cyclic peptide drugs, cyclic peptides have been proposed as a mid-point between biologics and small molecules. Computational design of structured cyclic peptides has been successful using Rosetta, even design of membrane traversing cyclic peptides, but efforts to design binders to protein targets have led to only a handful of successful cases. Deep learning (DL) networks have recently shown considerable opportunities for accurate structure prediction and design of biomolecules that are potent inhibitors of therapeutically relevant protein interfaces. This work describes the application of a cyclic offset to the AlphaFold2 network as well RFdiffusion, resulting in accurate prediction and design of structured de novo cyclic peptides and high affinity cyclic peptide binders against protein targets of interest of diverse shape and function.

Author dc:creator

• Rettie, Stephen Allan

Advisor dc:contributor.advisor

• Bhardwaj, Gaurav

• Computational protein design
• Peptides
• Biochemistry
• Computational chemistry

Statement dc:rights

• CC BY-NC

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1773/53023

OAI identifier oai:identifier

oai:digital.lib.washington.edu:1773/53023