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Protein-protein Interfaces

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Related Experiment Video

Updated: Jan 5, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

A path-based computational model for long non-coding RNA-protein interaction prediction.

Hui Zhang¹, Zhong Ming², Chunlong Fan³

¹School of Life Science, Liaoning University, Shenyang 110036, China.

|October 23, 2019

Summary

This summary is machine-generated.

This study introduces a novel path-based model (PBLPI) to predict long non-coding RNA (lncRNA)-protein interactions. PBLPI offers a computationally efficient method to identify these crucial biological interactions.

Keywords:

Bioinformatics Interaction prediction Path-based Protein lncRNA

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

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Last Updated: Jan 5, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

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Published on: December 1, 2023

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

Area of Science:

* Computational biology
* Molecular biology
* Bioinformatics

Background:

* Long non-coding RNAs (lncRNAs) play critical roles in biological processes.
* Experimental prediction of lncRNA-protein interactions is time-consuming and costly.
* Computational network similarity methods offer an efficient alternative for predicting these interactions.

Purpose of the Study:

* To propose a novel path-based lncRNA-protein interaction (PBLPI) prediction model.
* To computationally predict potential interactions between lncRNAs and proteins.
* To facilitate understanding of lncRNA biological functions.

Main Methods:

* Integration of protein semantic similarity, lncRNA functional similarity, known human lncRNA-protein interactions, and Gaussian interaction profile kernel similarity.
* Construction of a heterogeneous graph using three interlinked sub-graphs.
* Inference of potential lncRNA-protein interactions via a depth-first search algorithm.

Main Results:

* The PBLPI model achieved reliable performance in 5-fold cross-validation.
* Achieved an average AUC of 0.9244 and AUPR of 0.6478.
* Case study using "Mus musculus" data further validated the method's reliability.

Conclusions:

* The proposed PBLPI model effectively predicts lncRNA-protein interactions.
* PBLPI offers a valuable computational tool for identifying potential lncRNA-protein interactions.
* This approach aids in understanding the biological functions of lncRNAs.