Mareuil Fabien, Torchet Rachel, Ruano Luis Checa, Mallet Vincent, Nilges Michael, Bouvier Guillaume, Sperandio Olivier
Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015 Paris, France.
Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris F-75015, France.
Nucleic Acids Res. 2025 Jul 7;53(W1):W324-W329. doi: 10.1093/nar/gkaf403.
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.
预测蛋白质中的功能性结合位点对于理解蛋白质-蛋白质相互作用（PPI）和识别药物靶点至关重要。虽然存在各种计算方法，但许多方法无法评估PPI的配体结合能力，而这通常涉及构象变化。我们引入了InDeepNet，这是一个基于网络的平台，它将用于结合位点预测的深度学习模型InDeep与评估位点采用配体结合（全酶）构象倾向的InDeepHolo整合在一起。InDeepNet为研究人员提供了一个直观的界面，以便他们从内部数据、蛋白质数据库（PDB）或AlphaFold上传蛋白质结构，预测蛋白质或小分子的潜在结合位点。结果通过Mol*以交互式3D可视化的形式呈现，便于进行结构分析。借助InDeepHolo，该平台有助于选择最适合小分子结合的构象，从而改进基于结构的药物设计。InDeepNet可在https://indeep-net.gpu.pasteur.cloud/上访问，它消除了对专业编码技能或高性能计算的需求，使先进的预测模型广泛可用。通过简化PPI靶点评估和配体结合能力预测，它有助于研究并支持针对PPI的治疗开发。
