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Nucleic Acid Structure

Nucleic Acid Structure

The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms a 5′ to 3′ phosphodiester linkage.
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Related Experiment Video

Updated: Sep 17, 2025

Author Spotlight: Characterizing DNA G-Quadruplex by Bis-3-Chloropiperidine Based Chemical Mapping

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GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning

Paolo Climaco¹, Noelle M Mitchell², Matthew Tyler³

¹Institut für Numerische Simulation, University of Bonn, Bonn, 53115, NRW, Germany.

Mathematical Biosciences

|June 29, 2025

Summary

This summary is machine-generated.

We developed GMfold, a Python tool for rapid DNA aptamer secondary structure prediction. This high-throughput method aids in analyzing large aptamer pools from SELEX, advancing biosensor and therapeutic development.

Keywords:

Aptamer DNA descriptors DNA folding Motzkin paths SELEX Spectral clustering Subgraph matching Topic modeling

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Area of Science:

Biotechnology
Computational Biology
Molecular Biology

Background:

Aptamers are DNA molecules that bind targets with high affinity.
Predicting DNA secondary structures is crucial for understanding aptamer function.
Existing tools like mfold are not suitable for high-throughput analysis of large aptamer pools.

Purpose of the Study:

To develop a high-throughput computational method for predicting DNA aptamer secondary structures.
To enable real-time analysis of thousands of DNA sequences for aptamer selection.
To facilitate machine-learning integration into aptamer discovery and design.

Main Methods:

Developed GMfold, a Python code for high-throughput aptamer secondary structure determination.
Utilized subgraph matching methods to group aptamer candidates by structural similarity.
Improved the SeqFold open-source code by incorporating subgraph matching concepts.
Represented secondary structures as lowest-energy bipartite subgraph matchings.

Main Results:

GMfold enables high-throughput prediction of DNA secondary structures.
The new methods allow comparison of thousands of DNA sequences based on secondary structures.
The approach is effective for analyzing large sequence pools generated by SELEX.

Conclusions:

GMfold and improved SeqFold provide efficient tools for aptamer secondary structure analysis.
These computational advancements support machine-learning-informed aptamer selection.
This work is foundational for developing improved aptamer-based biosensors and therapeutics.