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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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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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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Updated: Sep 11, 2025

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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GATRsite: RNA-Ligand Binding Site Prediction Using Graph Attention Networks and Pretrained RNA Language Models.

Chuance Sun1, Linghao Zhang1, Lingfeng Zhang2

  • 1Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China.

Journal of Chemical Information and Modeling
|August 15, 2025
PubMed
Summary

Researchers developed GATRsite, a deep learning tool to predict RNA-ligand binding sites. This method enhances understanding of RNA function and aids in designing new small molecule therapies targeting RNA.

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Area of Science:

  • Computational biology
  • Drug discovery
  • Molecular modeling

Background:

  • Identifying functional RNA sites, especially small molecule binding sites, is vital for biological understanding and drug development.
  • RNA-targeted therapies offer novel strategies beyond traditional protein-based treatments.
  • Accurate computational prediction of RNA-small molecule interactions remains a significant challenge.

Purpose of the Study:

  • To introduce GATRsite, an efficient deep learning framework for predicting RNA-ligand binding sites.
  • To develop a computational model that accurately characterizes RNA and predicts binding sites.
  • To provide an accessible tool for researchers in RNA biology and drug design.

Main Methods:

  • Utilized graph attention networks (GATs) and Pretrained RNA Language Models.
  • Represented RNA nucleotides as nodes in a graph, incorporating sequential and structural features.
  • Integrated advanced embeddings from RNA Language Models to capture complex RNA characteristics.

Main Results:

  • GATRsite demonstrated superior performance compared to existing state-of-the-art methods.
  • Achieved high recall rates, Matthew's correlation coefficient, and F1 scores on benchmark datasets.
  • Showcased significant robustness in predicting RNA structures.

Conclusions:

  • GATRsite is an effective deep learning framework for predicting RNA-ligand binding sites.
  • The tool offers improved accuracy and robustness for RNA-based drug discovery.
  • A user-friendly online server is available for GATRsite at https://malab.sjtu.edu.cn/GATRsite/.