Computational modeling of cyclic peptide inhibitor-MDM2/MDMX binding through global docking and Gaussian accelerated molecular dynamics simulations

MDM2 and MDMX are potential targets for p53-dependent cancer therapy. Peptides are key in cellular immunology and oncology, and cyclic peptides generally have higher half-life than their linear counterparts. However, prediction of cyclic peptide-protein binding is challenging with normal molecular simulation approaches because of high peptide flexibility. Here, we used global peptide docking, normal molecular dynamics, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF) profiles, and solvated interaction energy (SIE) techniques to investigate the interactions of MDM2/MDMX with three N-to-C-terminal cyclic peptide-based inhibitors. We determined the possible cyclic peptide-MDM2/MDMX complex structures via 2D PMF profiles and SIE calculations. Our findings increase the accuracy of peptide-protein structural prediction, which may facilitate cyclic peptide drug design. Advancements in the computational methods and computing power may further aid in addressing the challenges in cyclic peptide drug design. Communicated by Ramaswamy H. Sarma

Automated summary

MDM2 and MDMX are potential targets for p53-dependent cancer therapy. Researchers used advanced computational methods to investigate the interactions between MDM2/MDMX and cyclic peptide-based inhibitors, aiming to improve the accuracy of peptide-protein structural prediction for cyclic peptide drug design. Advancements in computing power and methods hold promise for addressing challenges in cyclic peptide drug design.