Template-independent polymerases such as poly(U) polymerase (PUP) hold promise for enzymatic RNA synthesis but are limited by inefficient incorporation of modified nucleotides. Here, we describe a multi-round, closed-loop workflow integrating Gaussian accelerated molecular dynamics (GaMD), machine learning (ML), and generative artificial intelligence (AI) to engineer PUP variants with enhanced activity and stability. Our engineering strategy commenced with a deep mechanistic analysis of PUP using GaMD simulations. This provided the blueprint for our first key step: engineering PUPdel, a truncated variant that achieved a pivotal breakthrough by incorporating 3'-terminally blocked nucleotides and enabling controlled template-independent synthesis. Subsequently, we screened single-point mutations using protein language models (e.g. ESM1v) combined with Rosetta-based stability predictions, yielding a 47.78% hit rate for functionally active variants. Iterative ML models predicted synergistic multi-mutant combinations, increasing success rates to 63%. Finally, ESM3-based generative design produced PUPdel2, with 16 mutations conferring 3.4°C higher thermostability, 3.7-fold improved expression, and up to 5.4-fold enhanced catalytic efficiency for 3'-O-allyl-UTP. Structural analyses revealed that mutations enhance β-trapdoor flexibility and substrate binding via electrostatic and dynamic mechanisms. This AI-driven approach navigates vast sequence space efficiently, enabling superior enzymes for biotechnological applications in RNA therapeutics and beyond.
Yang et al. (2026) studied this question.