Technical bottlenecks in prime‑editing enzyme kinetics and the limitations of random laboratory evolution. The Prime Editing system is an innovative tool that achieves precise genome correction without double‑strand breaks (DSBs); however, the catalytic activity of the native reverse transcriptase (RT) domain and its slow extension kinetics severely limit overall editing efficiency. Conventional random laboratory‑evolved approaches attempted to overcome this but required astronomically large sequence‑library screening costs and failed to resolve the trade‑off between enzymatic thermodynamic stability and replication fidelity, leaving a blind spot that generates unintended transcriptional errors and off‑target noise.

Structure‑Based AI Computational Redesign Framework: Amino‑Acid Geometric Topology Simulation. In the study published in Nature Biotechnology on May 21, we combined AI‑driven high‑order protein structure prediction models with precise computational optimization algorithms to activate a ‘computational redesign’ architecture that reprograms the RT domain of existing evolved prime editors. The team encoded the geometric topology of key amino‑acid residues that physically contact the pegRNA guide and target DNA strand during reverse transcription into the AI simulator environment. This allowed minimization of the free‑energy barrier to enzymatic activity and optimization of the catalytic core conformation; the optimal combination of point mutations was back‑tracked in silico to identify the final engineered design.

Target editing efficiency increased >300% and off‑target genotoxicity reduced to less than half. Incorporation of the AI‑designed next‑generation RT structure into the prime‑editing system resulted in a >3‑fold (≥300%) surge in cellular target‑gene correction efficiency compared with standard protocols. Molecular dynamics analyses showed that the engineered RT domain stabilizes the binding affinity (K_D) to the pegRNA primer‑binding site (PBS) and accelerates template‑strand extension rates exponentially, completing transcription before interference by intracellular nucleases. Moreover, the mathematically corrected replication fidelity reduced chronic off‑target indel rates to ≤50% of the original level, demonstrating overwhelming clinical integrity.

Establishment of a next‑generation genome‑engineering platform and acceleration of ultra‑precise nucleic‑acid therapeutic design. The structural‑biology and AI‑protein‑engineering dataset generated by this work delivers a profoundly disruptive impact on the global biopharma industry and CRISPR gene‑editing R&D. By fully bypassing the labor‑intensive physical screening limits of laboratory‑based approaches through in‑silico design, we have set a kinetic benchmark for ‘custom synthetic enzyme design.’ This high‑efficiency RT backbone dramatically lowers the effective dose threshold of viral vectors for rare, multi‑gene, refractory disease therapeutics, providing a powerful commercial advantage by evading immune toxicity. Moreover, beyond human therapeutics, it will serve as a master reference for next‑generation genome‑correction platforms in agricultural synthetic biology and green‑bio sectors, enabling error‑free, ultra‑fast multiplex editing to lock in desired genotypes.