Evolving generalists in optimal cycling environments

To persist and thrive in ever changing conditions, living organisms must adapt to new challenges while maintaining performance to prior related tasks. How this ability to generalize evolves remains a puzzle. To evolve a proper defense against rapidly adapting pathogens, vertebrates’ adaptive immune system searches for broadly neutralizing antibodies through B cell affinity maturation. However, these generalist antibodies are hard to evolve and often outcompeted by specialists fitter in any particular environment. Using a generative approach, we find that switching between environments neither too similar nor too different can efficiently evolve fit generalists, via dynamically enlarging their attractor basins in sequence space. We further demonstrate that changing environments before populations reach a steady state can mobilize specialists but leave generalists undisturbed, thereby allowing specialists to evolve into generalists and not specialize again. Our framework predicts optimal correlations between vaccine antigens to be cycled at intermediate timescales for reliably evolving generalists. These design principles exploit nonequilibrium fitness ‘seascapes’ to drive populations into genotypes unevolvable in static environments.