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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.
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Updated: Nov 21, 2025

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

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Published on: July 9, 2021

DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms.

Dipan Shaw¹, Hao Chen², Minzhu Xie³

¹Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA. dshaw003@ucr.edu.

BMC Bioinformatics

|January 19, 2021

Summary

This study introduces DeepLPI, a novel computational method for predicting long non-coding RNA (lncRNA) and protein isoform interactions. DeepLPI improves prediction accuracy by integrating sequence, structure, and expression data, outperforming existing methods.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Long non-coding RNAs (lncRNAs) play crucial roles in biological processes through interactions with proteins.
Experimental identification of these interactions is costly and time-consuming.
Existing computational methods often overlook the complexity of protein isoforms and their distinct interactions with lncRNAs.

Purpose of the Study:

To develop a novel computational method, DeepLPI, for accurately predicting interactions between lncRNAs and specific protein isoforms.
To leverage multimodal data, including sequence, structure, and expression, for enhanced prediction.
To address the challenge of limited data for lncRNA-protein isoform interactions using a multiple instance learning (MIL) approach.

Main Methods:

DeepLPI employs a hybrid framework combining a multimodal deep learning neural network and a conditional random field.
It integrates intrinsic features from sequence and structure data with topological features from expression data.
A multiple instance learning (MIL) approach is utilized to handle the scarcity of known lncRNA-protein isoform interactions.

Main Results:

DeepLPI achieved a 4.7% improvement in AUC and a 5.9% improvement in AUPRC for human lncRNA-protein interactions compared to state-of-the-art methods.
Correlation analyses confirmed that co-expression information between lncRNAs and protein isoforms aids in predicting their interactions.
The method demonstrated superior performance in predicting mouse lncRNA-protein interactions and novel human interactions.

Conclusions:

The integration of protein isoforms and the MIL approach significantly enhances the performance of lncRNA-protein interaction prediction.
DeepLPI offers a promising advancement in understanding lncRNA-protein interactions.
The developed approach has potential applications in predicting other functional roles of RNAs and proteins.