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  1. Home
  3. Indeepnet: A Web Platform For Predicting Functional Binding Sites In Proteins Using Indeep.
  1. Home
  3. Indeepnet: A Web Platform For Predicting Functional Binding Sites In Proteins Using Indeep.

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InDeepNet: a web platform for predicting functional binding sites in proteins using InDeep.

Fabien Mareuil1, Rachel Torchet1, Luis Checa Ruano2

  • 1Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015 Paris, France.

Nucleic Acids Research
|May 8, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

InDeepNet predicts protein binding sites and their suitability for drug development. This platform aids researchers in identifying potential drug targets by assessing protein-ligand interactions and conformational changes.

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Area of Science:

  • Computational biology
  • Structural biology
  • Drug discovery

Background:

  • Predicting protein binding sites is essential for understanding protein-protein interactions (PPIs) and identifying drug targets.
  • Existing computational methods often struggle to evaluate the ligandability of PPIs, particularly considering conformational changes.

Purpose of the Study:

  • To introduce InDeepNet, a web-based platform that integrates deep learning for binding site prediction with an assessment of ligand-bound conformations.
  • To provide researchers with an accessible tool for predicting binding sites and evaluating their potential for small-molecule binding, thereby enhancing structure-based drug design.

Main Methods:

  • Developed InDeepNet, a platform combining the InDeep deep-learning model for binding site prediction and InDeepHolo for evaluating ligand-bound (holo) conformations.
  • Enabled users to upload protein structures from various sources (in-house, PDB, AlphaFold) for analysis.
  • Integrated Mol* for interactive 3D visualization of predicted binding sites.

    • Main Results:

    • InDeepNet successfully predicts potential binding sites for proteins and small molecules.
    • The platform facilitates the selection of optimal conformations for small-molecule binding, crucial for drug design.
    • InDeepNet offers an intuitive interface, eliminating the need for specialized coding or high-performance computing.

    Conclusions:

    • InDeepNet streamlines the assessment of PPI targets and ligandability prediction.
    • The platform democratizes access to advanced predictive modeling for drug discovery and therapeutic development targeting PPIs.