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Related Concept Videos

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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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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RNA-seq03:21

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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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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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.
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Related Experiment Video

Updated: Jan 11, 2026

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
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A Deep Learning Model to Predict the ncRNA-Protein Interactions Based on Sequences Information Only.

Maha Fm Sewailem1, Muhammad Arif1, Tanvir Alam1

  • 1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Bioinformatics and Biology Insights
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning model, RPI-SDA-XGBoost, accurately predicts noncoding RNA-protein interactions. This advancement aids in understanding gene regulation and diseases like cancer, paving the way for future computational biology research.

Keywords:
Noncoding RNAs (ncRNAs)conjoint triad feature (CTF)extreme gradient boosting (XGBoost)stacked auto-encoders (SAE)stacked denoising autoencoder (SDA)

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Noncoding RNAs (ncRNAs) are crucial regulators of fundamental biological processes.
  • Dysregulation of ncRNA interactions is linked to severe diseases, including cancer.
  • Accurate prediction of ncRNA-protein interactions is vital for biological research.

Purpose of the Study:

  • To develop a novel deep learning model for predicting ncRNA-protein interactions.
  • To enhance the understanding of ncRNA's role in biological processes and disease pathogenesis.

Main Methods:

  • Utilized 3-mer conjoint triad features (CTF) for protein sequence encoding.
  • Employed 4-mer frequency for RNA sequence encoding, generating 599-dimensional feature vectors.
  • Developed a deep learning model based on stacked denoising autoencoder (SDA) and XGBoost meta-learner.

Main Results:

  • The RPI-SDA-XGBoost model demonstrated superior performance compared to baseline models across multiple benchmark datasets.
  • Achieved state-of-the-art accuracy on RPI_488, RPI_1807, and RPI_NPInter v2.0 datasets.
  • Obtained high precision rates of 87.9% and 94.6% on the RPI_2241 and RPI_NPInter v2.0 datasets, respectively.

Conclusions:

  • The RPI-SDA-XGBoost model offers a promising computational approach for ncRNA-protein interaction prediction.
  • This study provides valuable insights for future biological research and the development of advanced computational methods.
  • The findings underscore the importance of ncRNA-protein interactions in health and disease.