Abstract

Colorectal cancer (CRC) remains a major global health burden with high mortality in advanced stages, highlighting the urgent need for more effective and safer therapies. Aberrant β-catenin stabilization and nuclear accumulation promote oncogenic transcriptional programs and remains an attractive yet challenging therapeutic target. Here, an in silico screen of 25 naturally derived compounds was performed against β-catenin (PDB: 1JDH) using AutoDock Vina 1.2.5. Ligands and receptors were prepared in PyMOL 3.0 and AutoDockTools 1.5.7. Blind docking was conducted using a whole-protein search space centered at x = −2.857, y = 9.859, z = 40.811 with a box size of approximately 107 × 59 × 121 Å in triplicate runs. Binding poses and interaction patterns were visualized in PyMOL 3.0 and BIOVIA Discovery Studio 2024 (3D and 2D interaction maps). Nine compounds achieved predicted binding affinities (ΔG_bind) of ≤ −7.0 kcal/mol, led by silibinin (−9.9 kcal/mol), followed by quercetin (-7.8 kcal/mol), luteolin (-7.7 kcal/mol), ellagic acid (-7.5 kcal/mol), garcinol (-7.5 kcal/mol), betulinic acid (-7.4 kcal/mol), ursolic acid (-7.4 kcal/mol), derricin (-7.0 kcal/mol) , and epigallocatechingallate (EGCG) (-7.0 kcal/mol). Silibinin showed a consistent predicted pose with multiple hydrogen-bond and pi-alkyl hydrophobic contacts within a putative pocket. Drug-likeness analysis using Lipinski’s Rule of Five indicated that most of the top hit ligands complied with criteria for molecular weight, hydrogen bond donors/acceptors, and lipophilicity, suggesting favorable oral bioavailability, while EGCG exceeded the recommended limits for hydrogen bond donors and acceptors, and garcinol surpassed the molecular weight and cLogP thresholds. Additionally, ADMET predictions highlighted potential concerns for quercetin due to a predicted mutagenic/tumorigenic risk. Overall, by applying a curated ligand set under a single standardized docking–ADMET workflow, this study reports novel screening outputs, including docking scores, predicted binding poses, and residue-level interaction profiles, together with an ADMET-informed prioritization. Based on these in silico results, silibinin emerged as the leading scaffold for prioritized experimental validation.

Keywords

References

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