In silico investigation of mulberry phytochemicals targeting CDK1 in cancer using molecular docking and computational approaches
The overexpression of CDK1 promotes uncontrolled cell proliferation, tumour survival, and migration. The present study explores novel phytochemicals from Morus alba (mulberry) that can modulate CDK1 expression to arrest tumour growth. Molecular docking of mulberrofuran L, mulberrofuran X, mulberrofuran Y, euparin, 6-methoxy-tremetone, and dinaciclib (Reference drug) exhibits favourable docking results, with scores of − 8.5, − 8.2, − 8.0, − 7.7, and − 7.7, and − 6.6 kcal/mol, respectively. Furthermore, the physicochemical analysis showed compliance with all essential parameters and followed the Lipinski rule of five, except the lipophilicity parameter. Moreover, predictive pIC50, evaluated using CODD-PRED, shows the highest value for mulberrofuran L (5.48), indicating its inhibitory action against CDK1. Moreover, the toxicity profile ADMET was evaluated using the DeepPK server, showing a harmless profile for mulberrofuran L and mulberrofuran X and toxicity for mulberrofuran Y, euparin, and 6-methoxy-tremetone. Molecular dynamics (MD) simulation trajectories, including RMSD, RMSF, Rg, and SASA, show stable dynamics for mulberrofuran L, mulberrofuran X, and dinaciclib (standard drug). The free energy landscape (FEL) confirms protein–ligand system stability, showing more clearly defined energy minima for mulberrofuran X, mulberrofuran L and dinaciclib. The principal component analysis (PCA) showed a broader distribution along PC1 and PC2 for mulberrofuran L, indicating higher conformational flexibility. The MM-GBSA binding free energy (ΔG) indicates that mulberrofuran X possesses the most favourable total binding energy of − 15.66 kcal/mol, suggesting stable interactions with the target. DFT evaluation showed the highest electronic softness for dinaciclib, while mulberrofuran L and X also indicate stability and good reactivity.
Authors
- Rakshit Singh Saini
- Imran Ansari
- Mudassir Alam
- Saima Nisar
- Mohd Junaid
- S. Abdullah
- Mantasha Irshad
Institutions
- Aligarh Muslim University (IN)
- Université Paris-Saclay (FR)
- Government Medical College, Amritsar (IN)
- Indian Biological Sciences and Research Institute (IN)
- Jamia Millia Islamia (IN)
Publication Details
- Journal
- Discover Chemistry.
- Published
- 2026-10-05
- DOI
- https://doi.org/10.1007/s44371-026-01020-w
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00