Cyclic peptide structure prediction and design using AlphaFold2.

Abstract

Small cyclic peptides have gained significant traction as a therapeutic modality; however, the development of deep learning methods for accurately designing such peptides has been slow, mostly due to the lack of sufficiently large training sets. Here, we introduce AfCycDesign, a deep learning approach for accurate structure prediction, sequence redesign, and de novo hallucination of cyclic peptides. Using AfCycDesign, we identified over 10,000 structurally-diverse designs predicted to fold into the designed structures with high confidence. X-ray crystal structures for eight tested de novo designed sequences match very closely with the design models (RMSD < 1.0 Å), highlighting the atomic level accuracy in our approach. Further, we used the set of hallucinated peptides as starting scaffolds to design binders with nanomolar IC₅₀ against MDM2 and Keap1. The computational methods and scaffolds developed here provide the basis for the custom design of peptides for diverse protein targets and therapeutic applications.