Computational evolution of poly(U) polymerase for efficient and controlled RNA oligonucleotide synthesis

Area of Science:

• Biochemistry
• Molecular Biology
• Computational Biology

Background:

• Template-independent polymerases like poly(U) polymerase (PUP) are crucial for enzymatic RNA synthesis.
• Current PUPs exhibit limitations in incorporating modified nucleotides efficiently.

Purpose of the Study:

• To engineer novel PUP variants with enhanced activity, stability, and modified nucleotide incorporation using an integrated AI-driven workflow.
• To overcome limitations in template-independent RNA synthesis.

Main Methods:

• Gaussian accelerated molecular dynamics (GaMD) for mechanistic analysis.
• Machine learning (ML) and generative artificial intelligence (AI) for variant screening and design.
• Protein language models (ESM1v, ESM3) and Rosetta for mutation prediction and stability analysis.

Main Results:

• Developed PUPdel, a truncated variant enabling controlled synthesis with 3'-terminally blocked nucleotides.
• Achieved high hit rates (47.78% to 63%) for functionally active variants through iterative ML models.
• Engineered PUPdel2 with 16 mutations, showing 3.4°C higher thermostability, 3.7-fold improved expression, and up to 5.4-fold enhanced catalytic efficiency for 3'-O-allyl-UTP.

Conclusions:

• An AI-driven workflow integrating GaMD, ML, and generative AI efficiently navigates sequence space for enzyme engineering.
• The engineered PUPdel2 variant offers superior performance for biotechnological applications, including RNA therapeutics.
• Mutations enhance enzyme flexibility and substrate binding through electrostatic and dynamic mechanisms.