Jul 2026· Indonesian Journal of Cancer Chemoprevention· Vol 16, pp. 107· 0 citations· 33 references
TL;DR
R. tomentosa B has potential as a natural product-based anticancer agent targeting RNR, providing a basis for further in vitro and in vivo studies and ADMET analysis indicated variability in pharmacokinetic properties.
Abstract
The increasing number of cancer cases has prompted the search for new drug candidates from natural ingredients, particularly plant-derived compounds considered safer and more effective. Rhodomyrtus tomentosa (Aiton) Hassk. contains various metabolites responsible for various biological activities. This study aimed to predict the anticancer potential of R. tomentosa metabolites against the Ribonucleotide Reductase (RNR) enzyme using an in silico approach. The RNR protein structure (PDB ID: 2WGH) was obtained from the RCSB Protein Data Bank. Molecular docking was performed on 25 compounds previously reported in the literature as metabolites of R. tomentosa using Molegro Virtual Docker (MVD) version 7.0 to evaluate ligand-receptor binding affinity based on MolDock Score values using a validated docking protocol (RMSD≤2.0 Å), followed by interaction analysis and pharmacokinetic evaluation using ADMET parameters. The results indicated that most compounds exhibited favorable binding affinities toward RNR, as reflected by negative MolDock Score values. Rhodomyrtosone B (−137.144 kcal/ mol) showed the best binding affinity, followed by Malvidin-3-glucoside (−135.173 kcal/ mol), Delphinidin-3-galactoside (-132.359 kcal/mol), Rhodomyrtosone I (−130.004 kcal/ mol), and Cyanidin-3-galactoside (-127.741 kcal/mol). Interaction analysis revealed stable interactions with key amino acid residues (Arg256, Asp226, and Ser269) through hydrogen bonding, hydrophobic, and electrostatic interactions. ADMET analysis indicated variability in pharmacokinetic properties, including absorption, distribution, metabolism, and toxicity. In conclusion, Rhodomyrtosone B has potential as a natural product-based anticancer agent targeting RNR, providing a basis for further in vitro and in vivo studies.Keywords: anticancer, Rhodomyrtus tomentosa, molecular docking, Ribonucleotide Reductase; in silico.
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Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, necessitating the development of safer and more targeted therapeutic strategies. This study computationally extends our previous experimental investigation of a peptide derived from
Lacticaseibacillus casei
by evaluating its interactions with two clinically relevant breast cancer targets, estrogen receptor alpha (ERα; PDB ID: 3ERT) and human epidermal growth factor receptor 2 (HER2; PDB ID: 1N8Z).
The peptide structure was predicted using PEP-FOLD and its stereochemical quality was assessed using a Ramachandran plot. Molecular docking was performed against ERα and HER2, followed by molecular dynamics simulations to evaluate structural stability. Binding free energy, binding affinity, dissociation constant, principal component analysis (PCA), free energy landscape (FEL), molecular mechanics (MM)/Poisson–Boltzmann surface area (PBSA) calculations, and
in silico
ADMET and toxicity predictions were performed to comprehensively characterise peptide–protein interactions.
The predicted peptide model exhibited 84.8% of residues located in the most favoured regions, while 15.2% were located in additionally allowed regions of the Ramachandran plot, indicating satisfactory stereochemical quality. Molecular docking demonstrated favourable interactions with both ERα and HER2, with HER2 showing a marginally more favourable docking score. Molecular dynamics simulations indicated stable peptide–protein complexes throughout the simulation period, as supported by root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen-bond analyses. MM/PBSA calculations predicted stronger binding for the HER2 (1N8Z) complex (ΔG = −28.83 kJ/mol) than for the ERα (3ERT) complex (ΔG = −11.65 kJ/mol), highlighting the complementary nature of docking and dynamic free-energy estimation, which produced different receptor rankings. PCA and FEL analyses further demonstrated stable conformational sampling for both complexes. ADMET predictions suggested favourable peptide-like physicochemical properties while identifying pharmacokinetic and toxicity parameters that require further experimental validation.
This computational study suggests that the
L. casei
-derived peptide exhibits favourable predicted interactions with ERα and HER2 and forms structurally stable peptide–protein complexes under simulated physiological conditions. These findings provide a computational framework for prioritising this probiotic-derived peptide for subsequent experimental validation and further investigation as a potential peptide-based therapeutic candidate for breast cancer.
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