Apta FastZ: An Algorithm for the Rapid Identification of Aptamers with Defined Binding Affinities
Bibliographic record
Abstract
Real-time biomolecular monitoring requires biosensors based on affinity reagents, such as aptamers, with moderate to low affinities for the best binding dynamics and signal gain. We recently reported Pro-SELEX, an approach that utilizes parallelized SELEX and high-content bioinformatics for the selection of aptamers with predefined binding affinities. The Pro-SELEX pipeline relies on an algorithm, termed AptaZ, that can predict the binding affinities of selected aptamers. The original AptaZ algorithm is computationally complex and slows the overall throughput of Pro-SELEX. Here, we present Apta FastZ, a rapid equivalent of AptaZ. The Apta FastZ algorithm considers the spare nature of the sequences from SELEX and is coded to avoid unnecessary comparison between sequences. As a result, Apta FastZ achieved a 10 to 40-fold faster computing speed compared to the original AptaZ algorithm while maintaining identical outcomes, allowing the bioinformatics to be completed within 1-10 h for large-scale data sets. We further validated the affinity of myeloperoxidase aptamers predicted by Apta FastZ by experiments and observed a high level of linear correlation between predicted scores and measured affinities. Taken together, the implementation of Apta FastZ could greatly accelerate the current Pro-SELEX workflow, allowing customized aptamers to be discovered within 3 days using preselected DNA libraries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".