Backbone Hydrocarbon-Constrained Nucleic Acids Modulate Hybridization Kinetics for RNA
Bibliographic record
Abstract
The binding affinity of therapeutic oligonucleotides (ONs) for their cognate RNA is determined by the rates of association ( k a ) and dissociation ( k d ). Single-stranded ONs are highly flexible and can adopt multiple conformations in solution, some of which may not be conducive for hybridization. We investigated if restricting rotation around the sugar–phosphate backbone, by tethering two adjacent backbone phosphonate esters using hydrocarbon bridges, can modulate hybridization kinetics of the modified ONs for complementary RNA. Given the large number of possible analogues with different tether lengths and configurations at the phosphorus atoms, we employed molecular dynamic simulations to optimize the size of the hydrocarbon bridge to guide the synthetic efforts. The backbone-constrained nucleotide trimers with stereodefined configurations at the contiguous backbone phosphorus atoms were assembled using a ring-closing metathesis reaction, then incorporated into oligonucleotides by an in situ synthesis of the phosphoramidites followed by coupling to solid supports. Evaluation of the modified oligonucleotides revealed that 15-membered macrocyclic-constrained analogues displayed similar or slightly improved on-rates but significantly increased off-rates compared to unmodified DNA ONs, resulting in reduced duplex stability. In contrast, LNA ONs with conformationally preorganized furanose rings showed similar on-rates to DNA ONs but very slow off-rates, resulting in net improvement in duplex stability. Furthermore, the experimental data generally supported the molecular dynamics simulation results, suggesting that this strategy can be used as a predictive tool for designing the next generation of constrained backbone ON analogues with improved hybridization properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".