Computation and Structure-Guided Arginine Scanning Engineers a Hyperactive AP Endonuclease for Multiplex Viral RNA...

Area of Science:

• Biotechnology
• Molecular Biology
• Bioinformatics

Background:

• DNA-binding proteins, such as AP endonucleases, are valuable biotechnological tools.
• Rewiring DNA-contact surfaces of these proteins is challenging.
• Existing methods lack generalizability for protein engineering.

Purpose of the Study:

• To develop a generalizable AI framework for engineering DNA-binding proteins.
• To enhance AP endonuclease activity and specificity.
• To create novel nucleic acid sensing technologies.

Main Methods:

• Development of ARGENT (AI-guided Arginine scanning Engine for Nucleic-acid Tuning), an interpretable framework.
• Integration of protein-DNA structures and homologous sequences for mutation prediction.
• Application to AP endonuclease 1 (APE1) and subsequent integration into NAPTUNE-V2.0 for viral detection.

Main Results:

• ARGENT identified key arginine substitution sites on APE1.
• Engineered APE1 variants showed increased AP-site cleavage and enhanced catalytic rates.
• The NAPTUNE-V2.0 system enabled multiplex detection of viral pseudotypes and influenza virus RNA.
• ARGENT demonstrated applicability to other DNA-binding proteins like Cas9d.

Conclusions:

• AI-guided arginine scanning is a practical approach for engineering hyperactive and high-fidelity AP endonucleases.
• Tuning DNA-binding interfaces can lead to next-generation nucleic acid technologies.
• This framework facilitates advanced viral RNA sensing applications.