Adaptive Evolution of Peptide Inhibitors for Mutating SARS‐CoV‐2. Issue 12 (8th October 2020) - Chaturvedi, Parth; Han, Yanxiao; Král, Petr; Vuković, Lela
Abstract: The SARS‐CoV‐2 virus is currently causing a worldwide pandemic with dramatic societal consequences for the humankind. In the past decades, disease outbreaks due to such zoonotic pathogens have appeared with an accelerated rate, which calls for an urgent development of adaptive (smart) therapeutics. Here, a computational strategy is developed to adaptively evolve peptides that could selectively inhibit mutating S protein receptor binding domains (RBDs) of different SARS‐CoV‐2 viral strains from binding to their human host receptor, angiotensin‐converting enzyme 2 (ACE2). Starting from suitable peptide templates, based on selected ACE2 segments (natural RBD binder), the templates are gradually modified by random mutations, while retaining those mutations that maximize their RBD‐binding free energies. In this adaptive evolution, atomistic molecular dynamics simulations of the template‐RBD complexes are iteratively perturbed by the peptide mutations, which are retained under favorable Monte Carlo decisions. The computational search will provide libraries of optimized therapeutics capable of reducing the SARS‐CoV‐2 infection on a global scale. Abstract : Adaptive evolution of peptides for their strong binding to target proteins is developed computationally. Random mutations are iteratively introduced into peptides, the systems are briefly simulated, and the mutations are accepted or rejected according to their free energy of binding (Metropolis). The strategy is tested in peptide inhibitors of the S protein receptor binding domain in different SARS‐CoV‐2 viral strains.