Unraveling Determinants of Affinity Enhancement in Dimeric Aptamers for a Dimeric Protein
Bibliographic record
Abstract
Abstract High-affinity aptamers can be derived de novo by using stringent conditions in SELEX (Systematic Evolution of Ligands by EXponential enrichment) experiments or can be engineered post SELEX via dimerization of selected aptamers. Using electrophoretic mobility shift assays, we studied a series of heterodimeric and homodimeric aptamers, constructed from two DNA aptamers with distinct primary sequences and secondary structures, previously isolated for VEGF-165, a homodimeric protein. We investigated four factors envisaged to impact the affinity of a dimeric aptamer to a dimeric protein: (1) length of the linker between two aptamer domains, (2) linking orientation, (3) binding-site compatibility of two component aptamers in a heterodimeric aptamer, and (4) steric acceptability of the two identical aptamers in a homodimeric aptamer. All heterodimeric aptamers for VEGF-165 were found to exhibit monomeric aptamer-like affinity and the lack of affinity enhancement was attributed to binding-site overlap by the constituent aptamers. The best homodimeric aptamer showed 2.8-fold better affinity than its monomeric unit (Kd = 13.6 ± 2.7 nM compared to 37.9 ± 14 nM), however the barrier to further affinity enhancement was ascribed to steric interference of the constituent aptamers. Our findings point to the need to consider the issues of binding-site compatibility and spatial requirement of aptamers for the development of dimeric aptamers capable of bivalent recognition. Thus, determinants highlighted herein should be assessed in future multimerization efforts.
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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 | 0.001 | 0.001 |
| 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".