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

Abstract Predicting functional binding sites in proteins is crucial for understanding protein–protein interactions (PPIs) and identifying drug targets. While various computational approaches exist, many fail to assess PPI ligandability, which often involves conformational changes. We introduce InDeepNet, a web-based platform integrating InDeep, a deep-learning model for binding site prediction, with InDeepHolo, which evaluates a site’s propensity to adopt a ligand-bound (holo) conformation. InDeepNet provides an intuitive interface for researchers to upload protein structures from in-house data, the Protein Data Bank (PDB), or AlphaFold, predicting potential binding sites for proteins or small molecules. Results are presented as interactive 3D visualizations via Mol*, facilitating structural analysis. With InDeepHolo, the platform helps select conformations optimal for small-molecule binding, improving structure-based drug design. Accessible at https://indeep-net.gpu.pasteur.cloud/, InDeepNet removes the need for specialized coding skills or high-performance computing, making advanced predictive models widely available. By streamlining PPI target assessment and ligandability prediction, it assists research and supports therapeutic development targeting PPIs.