Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

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...

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

Protein-protein Interfaces

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...

Protein Networks

Protein Networks

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,...

Ligand Binding and Linkage

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked. In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...

Ligand Binding and Linkage

Ligand Binding and Linkage

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Divergent Tumor Immune Microenvironment and Response to Neoadjuvant Chemoimmunotherapy in Resectable Non-Small Cell Lung Cancer: A Single-Arm Phase II Trial.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026

Same author

Nutritional screening, assessment, and management of peri-operative gastric cancer patients: a best practice implementation project.

JBI evidence implementation·2026

Same author

Staged uniportal video-assisted thoracoscopic bilateral lower lobectomy for bilateral intralobar pulmonary sequestration complicated by <i>Aspergillus</i> infection: a case report.

Frontiers in surgery·2026

Same author

Microwave-Assisted In Situ Encapsulation of Uniform Ag Nanoclusters in ZSM-5 for Enhanced Catalytic and Antibacterial Performance.

The journal of physical chemistry letters·2026

Same author

From CAT-like to POD-like enzymatic activity of Cu-BHT tuning by substrate engineering.

Physical chemistry chemical physics : PCCP·2026

Same author

Ursodeoxycholic acid mitigates cerebral ischemia/reperfusion injury by inhibiting thrombin-induced lipid peroxidation through activation of ALDH3A1.

Redox biology·2026

Search research articles

Related Experiment Video

Updated: Feb 26, 2026

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

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

Published on: July 9, 2021

LPI-ETSLP: lncRNA-protein interaction prediction using eigenvalue transformation-based semi-supervised link

Huan Hu¹, Chunyu Zhu, Haixin Ai

¹School of Life Science, Liaoning University, Shenyang, 110036, China. liuhongsheng@lnu.edu.cn.

Molecular Biosystems

|July 14, 2017

Summary

This study introduces LPI-ETSLP, a new computational method for predicting long non-coding RNA-protein interactions. This approach aids in understanding gene regulation and disease progression without needing negative samples.

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

Related Experiment Videos

Last Updated: Feb 26, 2026

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

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

Published on: July 9, 2021

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA-protein interactions are crucial for cellular processes and disease progression.
Experimental validation of long non-coding RNA-protein interactions is challenging and costly.
Existing computational methods for predicting these interactions are limited.

Purpose of the Study:

To develop a novel, efficient computational method for predicting long non-coding RNA-protein interactions.
To provide a reliable bioinformatics resource for researchers studying gene regulation and disease.

Main Methods:

Developed an eigenvalue transformation-based semi-supervised link prediction model (LPI-ETSLP).
The method operates in a semi-supervised manner, eliminating the need for negative samples.
Performance was evaluated using 5-fold cross-validation.

Main Results:

LPI-ETSLP achieved a high Area Under the Curve (AUC) of 0.8876 and an Area Under the Precision-Recall Curve (AUPR) of 0.6438.
The model demonstrated superior performance compared to three other computational methods.
A case study confirmed many predicted long non-coding RNA-protein interactions experimentally.

Conclusions:

LPI-ETSLP is a reliable and effective tool for predicting long non-coding RNA-protein interactions.
The method offers a valuable bioinformatics resource for advancing biomedical research.
This approach can accelerate the discovery of novel RNA-protein associations relevant to disease.