Related Experiment Video
Updated: Jun 27, 2026
11:34
Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
Published on: August 9, 2019
From randomness to recognition: modeling the evolution of DNA sequence information during enrichment for binding
Varun Maher1,2,3, Daniel Martin1, David Spetzler1
1Precision Medicine Target and Drug Discovery, Caris Life Sciences, 350 W. Washington St., 4th Floor, Tempe, AZ, 85288, United States.
Bioinformatics Advances
|June 26, 2026
Summary
Quantitative models for aptamer selection are lacking. This study uses a masked language model (MLM) to create sequence representations that capture functional characteristics and predict binding, offering insights into molecular information evolution.
Area of Science:
- Bioinformatics
- Computational Biology
- Molecular Biology
Background:
- Aptamer selection enriches nucleic acid libraries for target binding but lacks quantitative models for molecular information evolution.
- Understanding this evolution is crucial for improving aptamer design and selection processes.
Purpose of the Study:
- To develop a quantitative model for tracking molecular information evolution during aptamer enrichment.
- To demonstrate that latent space representations can capture functional library attributes and predict binding outcomes.
Main Methods:
- Trained a masked language model (MLM) on unlabeled DNA sequences.
- Used the MLM to generate latent space sequence representations of partially enriched libraries.
- Correlated representation divergence with experimental binding affinity and specificity.
Main Results:
- Latent representations converged for independent replicate enrichments against the same target, indicating capture of functional characteristics.
- Representation divergence strongly correlated with experimental binding.
- Classifiers based on latent embeddings accurately predicted target specificity, even for single amino acid differences.
Conclusions:
- Latent space models provide a quantitative measure of molecular information evolution during aptamer enrichment.
- These models offer conceptual insights and practical strategies for designing better initial libraries and optimizing enrichment protocols.
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