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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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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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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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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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Updated: Aug 17, 2025

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins
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CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features.

Lishen Zhang1, Chengqian Lu1, Min Zeng1

  • 1Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, China.

Briefings in Bioinformatics
|December 13, 2022
PubMed
Summary
This summary is machine-generated.

A new computational method, CRMSS, accurately identifies binding sites between circular RNAs (circRNAs) and RNA-binding proteins (RBPs). This advancement improves understanding of disease mechanisms involving circRNA-RBP interactions.

Keywords:
Deep learningRNA-binding proteincircular RNA

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Area of Science:

  • Computational Biology
  • Molecular Biology
  • Bioinformatics

Background:

  • Circular RNAs (circRNAs) are crucial regulators in disease pathogenesis due to their interactions with RNA-binding proteins (RBPs).
  • Existing computational methods for predicting circRNA-RBP binding sites lack accuracy, robustness, and interpretability.
  • Understanding these interactions is vital for developing novel therapeutic strategies.

Purpose of the Study:

  • To develop a novel computational method, CRMSS, for accurate prediction of circRNA-RBP binding sites.
  • To integrate multi-scale sequence and structure features of both circRNAs and RBPs for improved prediction.
  • To provide a robust and interpretable model for analyzing circRNA-RBP interactions.

Main Methods:

  • CRMSS utilizes sequence k-mer embedding and local secondary structure probabilities for circRNAs.
  • Features for RBPs are derived from sequence and structure frequencies within RNA-binding domains.
  • Multi-scale residual blocks, BiLSTM, and attention mechanisms are employed to capture binding patterns and contextual information.

Main Results:

  • CRMSS demonstrates superior predictive performance compared to existing state-of-the-art methods across 37 RBPs.
  • The model effectively integrates diverse sequence and structural features of both circRNAs and RBPs.
  • Case studies confirm the reliability of CRMSS predictions, including the identification of experimentally validated circRNA-RBP pairs.

Conclusions:

  • CRMSS offers a significant advancement in predicting circRNA-RBP binding sites, outperforming current methods.
  • The method's ability to incorporate multi-scale features enhances its accuracy and robustness.
  • CRMSS provides a valuable tool for researchers studying the roles of circRNA-RBP interactions in disease.