Fig. 1: Application of the GUIDE computationally driven drug engineering platform to Omicron.
From: Computationally restoring the potency of a clinical antibody against Omicron
Given a parental antibody and target antigens, a design space was defined and a collection of co-structures were estimated (left). Within the computational design phase (centre), a sequence generator used predictions of multiple properties to propose multi-point mutant antibody candidates, and a Bayesian optimization agent selected proposed sequences that were then simulated. On the basis of Pareto optimality, mutational distance and sequence diversity, 376 computationally evaluated sequences were selected and experimentally evaluated for binding in immunoassays (centre right). The best sequences were then evaluated for neutralization of SARS-CoV-2 variants, and the single best sequence was identified (right). See Supplementary Methods for details. FEP, free energy perturbation; MD, molecular dynamics; SFE, structural fluctuation estimation.
