ArticleJACS Au2024
Quantitative Characterization of Partitioning Stringency in SELEX.
Article in JACS Au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Maintaining stringent conditions in SELEX (Systematic Evolution of Ligands by EXponential enrichment) is crucial for obtaining high-affinity aptamers. However, excessive stringency greatly increases the risk of SELEX failure. Controlling stringency has remained a technical challenge, largely dependent on intuition, due to the absence of a clear, quantitative measure of stringency. This study was motivated by our insight that, while stringency is influenced by multiple factors, it can be quantified by its effect: increasing stringency reduces the quantity of binders normalized to that of nonbinders after partitioning. Based on this insight, we propose measuring stringency using the binder-to-nonbinder ratio (BNR), where a lower BNR indicates higher stringency. We derive an experimental method for determining BNR via quantitative PCR. Our theoretical analysis and SELEX experiments using two distinct proteins as selection targets underscore the importance of maintaining a BNR significantly greater than zero to avoid failure, a principle we call the SELEX nonfailure criterion. By employing inverse BNR to quantify stringency and applying this criterion, researchers can more rationally control SELEX progress. The quantitative stringency measure and nonfailure criterion can also be applied to other artificial evolution methods, provided that selected binders are quantifiable.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.