Selection of 2'-Deoxy-2'-Fluoroarabino Nucleic Acid (FANA) Aptamers that Bind HIV-1 Integrase with Picomolar Affinity
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Systematic Evolution of Ligands by Exponential Enrichment (SELEX) is the iterative process by which nucleic acids that can bind with high affinity and specificity (termed aptamers) to specific protein targets are selected. Using a SELEX protocol adapted for Xeno-Nucleic Acid (XNA) as a suitable substrate for aptamer generation, 2′-fluoroarabinonucleic acid (FANA) was used to select several related aptamers to HIV-1 integrase (IN). IN bound FANA aptamers with equilibrium dissociation constants ( K D,app ) of ∼50–100 pM in a buffer with 200 mM NaCl and 6 mM MgCl 2 . Comparisons to published HIV-1 IN RNA and DNA aptamers as well as IN genomic binding partners indicated that FANA aptamers bound more than 2 orders of magnitude more tightly to IN. Using a combination of RNA folding algorithms and covariation analysis, all strong binding aptamers demonstrated a common four-way junction structure, despite significant sequence variation. IN aptamers were selected from the same starting library as FA1, a FANA aptamer that binds with pM affinity to HIV-1 Reverse Transcriptase (RT). It contains a 20-nucleotide 5′ DNA sequence followed by 59 FANA nucleotides. IN-1.1 (one of the selected aptamers) potently inhibited IN activity and intasome formation in vitro. Replacing the FANA nucleotides of IN-1.1 with 2′-fluororibonucleic acid (F-RNA), which has the same chemical formula but with a ribose rather than arabinose sugar conformation, dramatically reduced binding, suggesting that FANA adopts unique structural conformations that promote binding to HIV-1 IN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".