Nature rarely joins nucleic acids to proteins covalently, yet synthetic protein-nucleic acid conjugates enable diverse applications from nanotechnology to therapeutics, which require or benefit from defined, stable, one-to-one linkages of known geometry. Here I evaluate biochemical routes to such conjugates against four ideals: enzymatic bond formation, addressability on both partners, minimal engineering of the protein of interest, and scarless linkage to unmodified nucleic acid. With an eye on biochemical mechanisms, I cover both established tools and natural systems that might become one. The available methods fall into two broad architectures. In cis, the enzyme itself becomes part of the product, allowing native chemistry on unmodified nucleic acid but requiring a folded fusion, usually at a terminus. In trans, a separate enzyme modifies the target protein and leaves a short motif, but the burden often shifts to the nucleic acid, which must carry a reactive group. No current method satisfies all four ideals, and the trade-offs largely reflect the substrate class recognized by the bond-forming enzyme. Nucleic-acid readers offer the most direct route to scarless, fully enzymatic conjugation but their requirement for large fusions limit protein-side addressability. Small-molecule readers invert this profile, enabling compact tags and greater placement flexibility at the cost of synthetic handles and a chemical final bond. Peptide readers combine short tags with enzymatic ligation but constrain attachment geometry and linkage chemistry. Site-specific, minimally invasive, scarless, and fully biochemical ProNAc formation therefore remains a tetralemma, with enzymatic activation of nucleic acids offering a promising route toward resolving it.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.