Biophysical fitness landscape design traps viral evolution - Vaibhav Mohanty; Eugene I. Shakhnovich (Proceedings of the National Academy of Sciences, 2026)
Evolutionary adaptation is often visualized as a population’s stochastic climb toward the top of a fitness landscape. While there exist approaches to design or synthetically evolve proteins into desired structures, there is a lack of methodology for designing, tuning, and quantitatively reshaping the fitness landscapes themselves on which protein evolution takes place. Here, we introduce foundational principles of fitness landscape design (FLD) to customize the structural peaks and valleys of biophysical fitness landscapes with quantitative accuracy, offering robust control of long-term evolutionary outcomes. Our FLD algorithms use stochastic optimization of a chemically derived biophysical fitness model to consistently discover optimal antibody ensembles which force a target protein to evolve according to a user-specified target fitness landscape. We then apply FLD to suppress the fitnesses of two SARS-CoV-2 genotype neutral networks and to discover proactive vaccines that preemptively restrict escape variant fitness trajectories before they arise.