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Related Experiment Video

Updated: Jan 8, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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Using structured libraries, selection, and machine learning to rapidly explore the sequence space of a fluorescent

Jaroslav Kurfürst1,2, Martin Volek1,3, Raman Samusevich1,4

  • 1Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague 166 10, Czech Republic.

Nucleic Acids Research
|December 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for exploring nucleic acid sequence space by using constrained libraries and machine learning. This approach significantly accelerates the discovery of functional motifs compared to traditional random mutagenesis techniques.

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Area of Science:

  • Nucleic acid engineering
  • Molecular biology
  • Computational biology

Background:

  • Exploring sequence space for functional nucleic acid motifs is challenging with standard random mutagenesis.
  • Current methods offer limited coverage of distant sequence variants.

Purpose of the Study:

  • To develop a more efficient method for exploring nucleic acid sequence space.
  • To rapidly identify functional variants and elucidate sequence-function relationships.

Main Methods:

  • Utilized secondary structure libraries based on desired motif constraints.
  • Employed a single round of selection followed by high-throughput sequencing.
  • Applied machine learning to analyze sequence-function relationships.

Main Results:

  • The new method identified ~40-fold more unique catalytic sequences compared to random mutagenesis.
  • Machine learning models accurately predicted variant activity and identified top performers.
  • Demonstrated accelerated and more efficient exploration of sequence space.

Conclusions:

  • Combining secondary structure libraries, selection, and machine learning offers a powerful approach for nucleic acid engineering.
  • This integrated strategy significantly enhances the speed and efficiency of discovering functional motifs.
  • The method provides a quantitative understanding of sequence-function relationships.