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

## TL;DR

This paper uses computational methods to identify potential drug targets on the CCN1 protein, suggesting it could be a viable target for treating aging and disease.

## Contribution

The study is the first to computationally demonstrate CCN1's druggability and identify candidate small molecules for modulating its activity.

## Key findings

- Multiple high-confidence druggable pockets were identified in CCN1 using AlphaFold 3 and Fpocket.
- Metformin showed the highest ligand affinity with a strong SwissDock AC score of -200.26.
- Ligand interactions remained stable across CCN1 variants and after amino acid deletions, indicating genetic robustness.

## Abstract

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.

The online version contains supplementary material available at 10.1038/s41598-025-34139-4.