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Iterative selection and mutagenesis strategies for creating novel proteins with enhanced or altered biochemical properties.
Current directed evolution methods struggle to predict and exploit epistatic interactions across multiple protein domains simultaneously. This research gap addresses the need for AI-driven frameworks that can map complex genetic interactions to guide evolution of multi-domain proteins with enhanced function.
There is no established methodology for incorporating live protein folding predictions into high-throughput screening workflows to pre-filter non-functional variants before phenotypic selection. This frontier addresses integrating AlphaFold2/ESMFold predictions directly into directed evolution pipelines.
Current directed evolution primarily optimizes catalytic function or binding, but lacks robust methods for engineering allosteric regulation and multi-state protein switches. This gap focuses on developing IVC-based selection systems that couple genotype-phenotype linkage with allosteric output detection.
Protein engineering is often trapped in local fitness maxima within rugged thermodynamic landscapes. This frontier explores ensemble-based approaches that simultaneously evolve multiple protein conformations to navigate broader fitness landscapes and discover genuinely novel protein functions.
Predicting protein function from sequences containing multiple simultaneous mutations remains computationally intractable, as combinatorial space grows exponentially. This research gap addresses developing graph neural network architectures that can predict emergent properties from high-order variant combinations.
Incorporating non-canonical amino acids (ncAAs) into directed evolution remains technically challenging due to incompatibility with cellular systems. This frontier addresses developing robust cell-free systems that enable directed evolution with expanded genetic codes and novel physicochemical properties.
Protein engineering has traditionally focused on folded domains, largely ignoring intrinsically disordered regions (IDRs) that comprise ~30% of eukaryotic proteomes. This gap addresses selection methodologies for evolving functional IDRs with specific binding, signaling, or regulatory outcomes.
Most directed evolution occurs within single protein scaffolds or closely related orthologs. This frontier explores systematic methods for directed evolution that can transfer functional improvements across phylogenetically distant protein families while maintaining functional diversity.