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Related Concept Videos

RNA Structure01:19

RNA Structure

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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. 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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Overview
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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Protein-protein Interfaces02:04

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Nucleic Acid Structure01:25

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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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Ribosome Profiling02:24

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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An Assay for Quantifying Protein-RNA Binding in Bacteria
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Quantifying sequence and structural features of protein-RNA interactions.

Songling Li1, Kazuo Yamashita2, Karlou Mar Amada2

  • 1Laboratory of Systems Immunology, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871, Japan standley@ifrec.osaka-u.ac.jp.

Nucleic Acids Research
|July 27, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces aaRNA, a machine-learning tool predicting protein RNA binding sites using sequence and structural features. It improves accuracy by incorporating evolutionary conservation and surface properties, aiding in identifying potential RNA-binding residues.

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Structural Biology

Background:

  • Protein-RNA interactions are crucial in cellular processes.
  • Predicting RNA binding sites in proteins is essential for understanding these interactions.
  • Existing sequence-based predictors offer high sensitivity but low specificity, while structure-based methods show the opposite.

Purpose of the Study:

  • To develop a machine-learning approach that integrates both sequence and structural features for improved prediction of residue-level RNA binding sites in proteins.
  • To enhance prediction accuracy beyond current meta-prediction servers.
  • To provide a user-friendly web server for identifying potential RNA-binding sites.

Main Methods:

  • Utilized a machine-learning approach to quantify the predictive power of sequence and structure-based features.
  • Employed homology modeling to extract structural features for proteins lacking known structures.
  • Introduced novel features: hidden Markov model-based evolutionary conservation, Laplacian norm-based surface deformations, and partitioned relative solvent accessibility.

Main Results:

  • Achieved enhanced accuracy in predicting residue-level RNA-binding propensity compared to previous methods and meta-prediction servers.
  • Demonstrated the effectiveness of integrating evolutionary conservation, surface geometry, and solvent accessibility features.
  • Successfully developed and implemented the aaRNA web server.

Conclusions:

  • The combination of sequence and novel structural features significantly improves the prediction of RNA-binding sites.
  • The aaRNA web server provides a valuable tool for researchers investigating protein-RNA interactions.
  • This approach offers a more sensitive and specific method for identifying potential RNA-binding residues.