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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein Networks02:26

Protein Networks

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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Related Experiment Video

Updated: Mar 26, 2026

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

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Predicting Long Noncoding RNA and Protein Interactions Using Heterogeneous Network Model.

Ao Li1, Mengqu Ge2, Yao Zhang2

  • 1School of Information Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China; Centers for Biomedical Engineering, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China.

Biomed Research International
|February 4, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces LPIHN, a novel computational method for predicting long noncoding RNA-protein interactions. LPIHN utilizes a heterogeneous network model to identify potential interactions, aiding in understanding lncRNA functions in diseases.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Long noncoding RNAs (lncRNAs) play roles in biological processes and diseases.
  • The functions of many lncRNAs remain largely unknown.
  • Understanding lncRNA-protein interactions is crucial for elucidating their roles.

Purpose of the Study:

  • To develop a computational method for predicting lncRNA-protein interactions.
  • To identify novel lncRNA-protein interactions using a network-based approach.
  • To enhance the understanding of lncRNA functions in complex diseases.

Main Methods:

  • Construction of a heterogeneous network integrating lncRNA-lncRNA similarity, lncRNA-protein interactions, and protein-protein interaction (PPI) networks.
  • Application of a random walk with restart algorithm on the heterogeneous network.
  • Validation using leave-one-out cross-validation.

Main Results:

  • The proposed method, LPIHN, achieved a high Area Under the Curve (AUC) of 96.0% in cross-validation.
  • Predicted lncRNA-protein interactions were confirmed by recent research and databases.
  • Demonstrated the efficiency of LPIHN in predicting novel interactions.

Conclusions:

  • LPIHN is an effective computational tool for predicting lncRNA-protein interactions.
  • The method contributes to understanding the functional roles of lncRNAs in biological processes and diseases.
  • Facilitates further research into lncRNA functions and their involvement in disease.