Cyclic peptide structure prediction and design using AlphaFold2
Stephen Rettie, Katelyn V. Campbell, Asim K. Bera, Alex Kang, Simon Kozlov, Yensi Flores Bueso, Joshmyn De La Cruz, Maggie Ahlrichs, Suna Cheng, Stacey Gerben, Mila Lamb, Analisa Murray, Victor Adebomi, Guangfeng Zhou, Frank DiMaio, Sergey Ovchinnikov, Gaurav Bhardwaj
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. AfCycDesign: Cyclic offset to the relative positional encoding in AlphaFold2 enables accurate structure prediction, sequence redesign, and de novo hallucination of cyclic peptide monomers and binders.