Designing a DNA-encoded library of aptamer-like oligomers that target an antibody drug.
Bibliographic record
Abstract
Aptamers are oligonucleotide sequences that have shown promise as alternatives to antibodies due to their high binding affinities with various targets.While aptamers possess some advantages over their protein counterparts, including the ease of chemical modification and the ability to undergo in vitro selection from a randomized pool, their limited chemical diversity, stemming from the four canonical nucleosidic bases, restricts their binding capabilities.Many methods have been used to broaden the chemical space of aptamers including SOMAmer technology and or click-SELEX, but methods rely on nucleosidic monomers that are compatible with DNA polymerases or ligases.Previously, the Sleiman and McKeague labs introduced Aptamer-Like ENcoded OligoMERs (Alenomers) to address the limited chemical diversity of aptamers.This approach incorporates synthetic, non-nucleosidic building blocks into sequence-defined oligo strands to expand the range of molecular interactions.The proof-of-concept study made use of the thrombin binding aptamer (TBA) as a template, a well-known stable G-quadruplex.We also include a DNA code strand that is covalently linked to the oligomer through a nucleoside-based branching unit to identify the corresponding oligo strand.Solid-phase phosphoramidite synthesis and the split-and-pool strategy were used to create a combinatorial library of nearly 300,000 alenomers.Libraries were subject to biomolecule selection, separation, and code amplification.Alenomers identified from nextgeneration sequencing of the DNA code showed improvements in binding affinity and serum stability, underpinning our strategy as an effective method to identify new and useful sequencedefined oligomer for biomolecule binding.The goal of this thesis was to study the important parameters of the alenomer library structure and design that can be tuned for efficient aptamer function.First, we identified a model aptamer that
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".