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Related Concept Videos

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.
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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

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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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Updated: Sep 10, 2025

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LncPTPred: predicting lncRNA-protein interaction based on crosslinking and immunoprecipitation (CLIP-Seq) data.

Gourab Das1, Troyee Das1, Zhumur Ghosh1

  • 1Department of Biological Sciences, Bose Institute, Unified Academic Campus, Kolkata 700 091, West Bengal, India.

Briefings in Bioinformatics
|August 21, 2025
PubMed
Summary

We developed LncPTPred, a machine learning tool to predict long noncoding RNA (lncRNA)-protein interactions (LPI). This computational approach accelerates LPI discovery, outperforming existing methods and guiding experimental validation.

Keywords:
CLIP-Seqhyperparameter optimizationlncRNA–protein interactionmachine learning

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Long noncoding RNA (lncRNA)-protein interactions (LPI) are crucial regulators of cellular processes.
  • Experimental identification of LPIs is resource-intensive and time-consuming.
  • In silico prediction offers an efficient alternative for identifying LPIs.

Purpose of the Study:

  • To develop a state-of-the-art machine learning (ML)-based algorithm for predicting LPI.
  • To identify hidden patterns in cross-linking immunoprecipitation sequencing (CLIP-Seq) data for improved prediction accuracy.
  • To provide a user-friendly tool for predicting LPIs.

Main Methods:

  • Utilized CLIP-Seq data to identify patterns for training an ML model.
  • Developed and trained an ML-based prediction algorithm for LPI.
  • Compared the performance of the developed model against contemporary prediction tools.

Main Results:

  • The developed ML model demonstrated superior performance compared to existing LPI prediction tools.
  • The tool successfully identified interaction segments within lncRNA loci.
  • The LncPTPred tool is available as a web server and a standalone version.

Conclusions:

  • The LncPTPred tool offers an efficient and accurate method for predicting LPIs.
  • The identified interaction segments can guide experimental validation of LPIs.
  • This computational approach accelerates the study of lncRNA functions in biological systems.