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Related Concept Videos

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
DNA has a double-helix structure. The...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Nucleic Acids and Nucleotides01:20

Nucleic Acids and Nucleotides

Nucleic acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and have instructions for its functioning. The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA).
Deoxyribonucleic Acid (DNA)
DNA is the genetic material in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is in the nucleus of eukaryotes and the organelles such as chloroplasts and mitochondria. In...
Nucleic acids02:43

Nucleic acids

Nucleic acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and carry instructions for its functioning.
DNA and RNA
The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes, the...
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...

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

Analyzing and Building Nucleic Acid Structures with 3DNA

Published on: April 26, 2013

NAPS: a residue-level nucleic acid-binding prediction server.

Matthew B Carson1, Robert Langlois, Hui Lu

  • 1Department of Bioengineering/Bioinformatics, University of Illinois at Chicago, Chicago, IL, USA.

Nucleic Acids Research
|May 19, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a computational method to identify amino acid residues that bind to DNA and RNA. This aids in understanding nucleic acid-binding proteins and guides future research.

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RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Area of Science:

  • Biochemistry
  • Bioinformatics
  • Molecular Biology

Background:

  • Nucleic acid-binding proteins are crucial for cellular functions.
  • Identifying specific binding residues is key to understanding protein mechanisms.
  • Accurate prediction of these residues aids functional annotation and experimental design.

Purpose of the Study:

  • To develop a computational method for predicting DNA- and RNA-binding residues.
  • To utilize sequence-based attributes for residue identification.
  • To provide a tool for functional annotation and experimental guidance.

Main Methods:

  • Employed the C4.5 algorithm.
  • Integrated bootstrap aggregation and cost-sensitive learning.
  • Developed sequence-based models for DNA- and RNA-binding residue prediction.

Main Results:

  • Achieved 79.1% accuracy for the DNA-binding prediction model.
  • Reached 73.2% accuracy for the RNA-binding prediction model.
  • Developed the NAPS web server for accessible predictions.

Conclusions:

  • The developed method effectively predicts nucleic acid-binding residues.
  • The NAPS web server offers a valuable resource for researchers.
  • This work facilitates functional annotation and experimental planning for nucleic acid-binding proteins.