摘要：The overarching goal of computational protein design is to gain complete control over protein structure and *** majority of sophisticated binders and enzymes,however,are large and exhibit diverse and complex folds that defy atomistic design ***,recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design *** these approaches,evolutionary constraints mitigate the risk from misfolding and aggregation,focusing atomistic design calculations on a small but highly enriched sequence *** methods have dramatically optimized diverse proteins,including vaccine immunogens,enzymes for sustainable chemistry,and proteins with therapeutic *** new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design,in principle,to any natural protein of known *** envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.