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Related Concept Videos

The DNA Helix01:07

The DNA Helix

Deoxyribonucleic acid, or DNA, is the genetic material responsible for passing traits from generation to generation in all organisms and most viruses. DNA is composed of two strands of nucleotides that wind around each other to form a spring-like structure called a double helix. However, the double helix is not perfectly symmetrical. Instead, there are regularly occurring grooves in the structure. The major groove occurs where the sugar-phosphate backbones are relatively far apart. This space...
The DNA Helix01:16

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Nucleic Acid Structure01:25

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.
DNA Structure
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Single-Strand DNA Binding Proteins01:03

Single-Strand DNA Binding Proteins

For successful DNA replication, the unwinding of double-stranded DNA must be accompanied by stabilization and protection of the separated single strands of the DNA. This crucial task is performed by single-strand DNA-binding (SSB) proteins. They bind to the DNA in a sequence-independent manner, which means that the nitrogenous bases of the DNA need not be present in a specific order for binding of SSB proteins to it. The binding of SSB proteins straightens single-stranded DNA (ssDNA) and makes...
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The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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RNA Structure01:19

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Updated: Jul 12, 2026

Analyzing and Building Nucleic Acid Structures with 3DNA
16:24

Analyzing and Building Nucleic Acid Structures with 3DNA

Published on: April 26, 2013

3dDNAi: An Integrated Approach for 3D Structure Prediction of Single-Stranded DNAs.

Yi Zhang1, Yiduo Xiong2, Yi Xiao2

  • 1School of Biomedical Engineering and Health, State Key Laboratory of New Textile Materials and Advanced Processing Technologies, Wuhan Textile University, Wuhan 430073, Hubei, China.

Journal of Chemical Theory and Computation
|July 9, 2026
PubMed
Summary

We developed 3dDNAi, a novel method integrating physics-based and deep-learning approaches to predict the 3D structures of single-stranded DNA (ssDNA) aptamers, overcoming data scarcity for improved accuracy.

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Area of Science:

  • Biomolecular structure prediction
  • Computational biology
  • Genomics

Background:

  • Machine learning accurately predicts protein and RNA structures.
  • RNA structure prediction accuracy is limited by scarce experimental data.
  • Single-stranded DNA (ssDNA) structure prediction faces even greater challenges due to data scarcity.

Purpose of the Study:

  • To develop an accurate method for predicting the 3D structures of DNA aptamers.
  • To address the limitations of current methods in predicting ssDNA structures.
  • To leverage existing RNA structure prediction models for DNA aptamer structure prediction.

Main Methods:

  • Integration of a physics-based method (3dDNA) with an RNA language model and a deep-learning RNA structure prediction model.
  • Development of a novel computational framework, 3dDNAi, for ssDNA structure prediction.
  • Utilizing deep learning to overcome data scarcity in ssDNA structure prediction.

Main Results:

  • 3dDNAi significantly improves global and backbone-level structural agreement for ssDNA aptamers.
  • The integrated approach effectively addresses the data scarcity problem for ssDNA.
  • Performance comparison with AlphaFold3 is dependent on the specific metrics used.

Conclusions:

  • 3dDNAi offers a promising solution for accurate DNA aptamer 3D structure prediction.
  • The developed framework can potentially be applied to other biomolecular systems with limited experimental data.
  • This work advances computational methods for nucleic acid structure prediction.