Computational Prediction of Cyclic Peptide Structural Ensembles and Application to the Design of Keap1 Binders

The Nrf2 transcription factor is a master regulator of
the cellular
response to oxidative stress, and Keap1 is its primary negative regulator.
Activating Nrf2 by inhibiting the Nrf2–Keap1 protein–protein
interaction has shown promise for treating cancer and inflammatory
diseases. A loop derived from Nrf2 has been shown to inhibit Keap1
selectively, especially when cyclized, but there are no reliable design
methods for predicting an optimal macrocyclization strategy. In this
work, we employed all-atom, explicit-solvent molecular dynamics simulations
with enhanced sampling methods to predict the relative degree of preorganization
for a series of peptides cyclized with a set of bis-thioether “staples”.
We then correlated these predictions to experimentally measured binding
affinities for Keap1 and crystal structures of the cyclic peptides
bound to Keap1. This work showcases a computational method for designing
cyclic peptides by simulating and comparing their entire solution-phase
ensembles, providing key insights into designing cyclic peptides as
selective inhibitors of protein–protein interactions.