Advancing protein evolution with inverse folding models integrating structural and evolutionary constraints.

Abstract

Protein engineering enables artificial protein evolution through iterative sequence changes, but current methods often suffer from low success rates and limited cost effectiveness. Here, we present AI-informed constraints for protein engineering (AiCE), an approach that facilitates efficient protein evolution using generic protein inverse folding models, reducing dependence on human heuristics and task-specific models. By sampling sequences from inverse folding models and integrating structural and evolutionary constraints, AiCE identifies high-fitness single and multi-mutations. We applied AiCE to eight protein engineering tasks, including deaminases, a nuclear localization sequence, nucleases, and a reverse transcriptase, spanning proteins from tens to thousands of residues, with success rates of 11%-88%. We also developed base editors for precision medicine and agriculture, including enABE8e (5-bp window), enSdd6-CBE (1.3-fold improved fidelity), and enDdd1-DdCBE (up to 14.3-fold enhanced mitochondrial activity). These results demonstrate that AiCE is a versatile, user-friendly mutation-design method that outperforms conventional approaches in efficiency, scalability, and generalizability.