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<p><i>In silico</i> druggability assessment helps shorten early drug discovery by identifying small molecules worth experimental testing as potential protein modulators. CCN1 is a multifunctional protein involved in various physiological processes and its dysregulation has been implicated in pathological conditions such as aging, fibrosis, inflammation, and cancer. The diverse, and sometimes contradictory, functions of CCN1 make it an important candidate for druggability assessment. In this study, we evaluated its druggability by predicting its 3D structure using AlphaFold 3, identifying binding pockets with Fpocket, and assessing ligand affinity with SwissDock. Our integrative <i>in silico</i>&#xa0;workflow identified multiple high-confidence druggable pockets within the CCN1 protein, with the top-scoring site located between the thrombospondin type 1 (TSP-1) and C-terminal cystine knot (CTCK) domains. Molecular docking predicted strong interactions with several clinically relevant compounds, including antioxidants and senolytics, with Metformin showing the highest affinity (SwissDock AC score: -200.26). Importantly, these ligand-binding interactions remained stable even after deletion of amino acids forming the predicted pocket and across naturally occurring CCN1 variants arising from SNPs, indicating that CCN1 is a genetically robust drug target. This study is the first to computationally demonstrate the druggability of CCN1 and to identify candidate small molecules with the potential to modulate its activity in aging- and disease-related contexts. Our findings provide both mechanistic insight and a scalable workflow for rapid screening of CCN1-targeted therapeutics.</p>

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Computational analysis of CCN1 as a druggable target predicts interactions with bioactive compounds

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月13日

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  • Roudy Bou Francis,
  • Racha Kerek,
  • Mohamad Rima

摘要

In silico druggability assessment helps shorten early drug discovery by identifying small molecules worth experimental testing as potential protein modulators. CCN1 is a multifunctional protein involved in various physiological processes and its dysregulation has been implicated in pathological conditions such as aging, fibrosis, inflammation, and cancer. The diverse, and sometimes contradictory, functions of CCN1 make it an important candidate for druggability assessment. In this study, we evaluated its druggability by predicting its 3D structure using AlphaFold 3, identifying binding pockets with Fpocket, and assessing ligand affinity with SwissDock. Our integrative in silico workflow identified multiple high-confidence druggable pockets within the CCN1 protein, with the top-scoring site located between the thrombospondin type 1 (TSP-1) and C-terminal cystine knot (CTCK) domains. Molecular docking predicted strong interactions with several clinically relevant compounds, including antioxidants and senolytics, with Metformin showing the highest affinity (SwissDock AC score: -200.26). Importantly, these ligand-binding interactions remained stable even after deletion of amino acids forming the predicted pocket and across naturally occurring CCN1 variants arising from SNPs, indicating that CCN1 is a genetically robust drug target. This study is the first to computationally demonstrate the druggability of CCN1 and to identify candidate small molecules with the potential to modulate its activity in aging- and disease-related contexts. Our findings provide both mechanistic insight and a scalable workflow for rapid screening of CCN1-targeted therapeutics.