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

Abstract

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.

Assessing the Performance of Peptide Force Fields for Modeling the Solution Structural Ensembles of Cyclic Peptides

“…Cyclic peptides have emerged as promising drug candidates, combining the advantages of small molecules, such as membrane permeability and oral availability, with the ability to target large protein interfaces akin to biologics . An understanding of the structural characteristics of cyclic peptides in water can greatly aid effective cyclic peptide design. − However, solution structural characterization of cyclic peptides through experimental methods such as nuclear magnetic resonance (NMR) is challenging due to their conformational flexibility in solution. ,, Molecular dynamics (MD) simulations offer a viable means to investigate the conformational ensembles of cyclic peptides in solution. , However, the accuracy of MD simulations heavily relies on the quality of the force fields employed. In 2016, Geng et al employed replica-exchange molecular dynamics (REMD) simulations with four different force fields (OPLS-AA/L+TIP4P-Ew, , Amber99SB-ILDN+TIP3P, , RSFF1+TIP4P-Ew, and RSFF2+TIP3P) to simulate 20 cyclic peptides with known crystal structures .…”

“… 1 An understanding of the structural characteristics of cyclic peptides in water can greatly aid effective cyclic peptide design. 2 − 4 However, solution structural characterization of cyclic peptides through experimental methods such as nuclear magnetic resonance (NMR) is challenging due to their conformational flexibility in solution. 2 , 5 , 6 Molecular dynamics (MD) simulations offer a viable means to investigate the conformational ensembles of cyclic peptides in solution.…”