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The DNA Helix

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...

Nucleic Acid Structure

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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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DNA as a Genetic Template

DNA as a Genetic Template

Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...

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Updated: Jul 4, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Predicting DNA structure using a deep learning method.

Jinsen Li¹, Tsu-Pei Chiu¹, Remo Rohs^2,3,4,5

¹Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA.

Nature Communications

|February 9, 2024

Summary

This summary is machine-generated.

Deep DNAshape, a novel deep learning method, accurately predicts DNA shape features by considering flanking DNA sequences. This tool enhances understanding of protein-DNA binding and gene regulation.

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Area of Science:

Genomics
Structural Biology
Bioinformatics

Background:

Protein-DNA binding is crucial for gene regulation.
Three-dimensional DNA structure (DNA shape) significantly influences these binding mechanisms.
Current methods for predicting DNA shape often rely on k-mer approaches and may not fully capture the impact of flanking regions.

Purpose of the Study:

To introduce Deep DNAshape, a deep learning-based method for high-throughput prediction of DNA shape features.
To accurately account for the influence of extended flanking regions on DNA shape without extensive simulations.
To provide insights into how flanking regions affect protein-DNA binding mechanisms.

Main Methods:

Developed a deep learning model, Deep DNAshape, for predicting DNA structural features.
The method inherently incorporates the effects of extended flanking DNA sequences.
Evaluated the method's ability to predict DNA shape and its impact on downstream machine learning models.

Main Results:

Deep DNAshape accurately predicts DNA structural features, considering extended flanking regions.
The method demonstrates that flanking regions quantitatively affect DNA shape readout mechanisms.
Incorporating Deep DNAshape features into machine learning models improved prediction accuracy.

Conclusions:

Deep DNAshape offers a powerful, high-throughput tool for predicting DNA shape and understanding the influence of flanking sequences.
The findings provide valuable insights into the detailed structural readout mechanisms of protein-DNA binding.
This method can be broadly applied to various DNA structure-related studies in genomics and bioinformatics.