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Related Concept Videos

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...
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...
RNA Structure01:19

RNA Structure

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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA) involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three...
RNA Structure01:23

RNA Structure

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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
RNA Structure01:23

RNA Structure

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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...

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Related Experiment Video

Updated: Jun 14, 2026

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

Struct-NB: predicting protein-RNA binding sites using structural features.

Fadi Towfic1, Cornelia Caragea, David C Gemperline

  • 1Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA 50011-1040, USA. ftowfic@cs.iastate.edu

International Journal of Data Mining and Bioinformatics
|March 20, 2010
PubMed
Summary
This summary is machine-generated.

We analyzed protein-RNA interactions using machine learning. Structural features improved predictions of binding sites more than sequence features alone, enhancing our understanding of these crucial molecular interactions.

Keywords:
propensityprotein-RNA interactionsstructural features

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Last Updated: Jun 14, 2026

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

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Published on: June 12, 2019

Area of Science:

  • Structural biology
  • Bioinformatics
  • Computational biology

Background:

  • Protein-RNA interactions are vital for cellular processes.
  • Predicting these interfaces is crucial for understanding gene regulation and disease.
  • Existing methods often rely solely on sequence information.

Purpose of the Study:

  • To investigate the utility of sequence and structural features for predicting protein-RNA interfaces.
  • To develop and evaluate machine learning models for this prediction task.
  • To compare the performance of models using different feature sets.

Main Methods:

  • Utilized RB-147, a curated dataset of protein-RNA complexes from the Protein Data Bank (PDB).
  • Developed and trained machine learning classifiers, including Naive Bayes models.
  • Employed both sequence-derived and structure-derived features of proteins.

Main Results:

  • A Naive Bayes classifier incorporating structural features (Struct-NB) demonstrated superior performance.
  • Struct-NB outperformed models that relied exclusively on sequence-based features.
  • The findings highlight the importance of structural context in predicting protein-RNA binding sites.

Conclusions:

  • Structural features significantly enhance the accuracy of predicting protein-RNA interfaces.
  • Machine learning models, particularly those leveraging structural data, offer a powerful approach for interface prediction.
  • This work provides a foundation for more accurate computational modeling of protein-RNA recognition.