Molegro Virtual Docker-Based Prediction of Rhodomyrtus tomentosa Metabolites Targeting Ribonucleotide Reductase as Potential Anticancer Agents

In Silico Optimization of Dihydropteridine Derivatives Targeting PLK1 for Glioblastoma: An Integrated QSAR, Docking, and Molecular Dynamics Study

A 2D quantitative structure–activity relationship (QSAR) model was developed for a series of 34 dihydropteridine derivatives to predict their antitumor activity against glioblastoma. The multiple linear regression model (MLR), based on four descriptors (ATS7s, AATS8e, AATS3p, and AATS5p), demonstrated satisfactory statistical performance (R² = 0.714, R²_adj = 0.660, Q² = 0.513, RMSE = 0.096, F = 13.134, p < 0.0001), with strong external validation (R²_test = 0645). Model-guided optimization led to the design and screening of new structural analogs, which were subsequently docked against Polo-like kinase 1 (PLK1, PDB ID: 3BD6), a key mitotic regulator overexpressed in glioblastoma. Among the compounds evaluated, X14 exhibited the most favorable docking affinity (-10.5 kcal / mol), compared to -6.6 kcal/mol for Temozolomide (TMZ) and 8.1 kcal/mol for the reference compound N27. Although generally favorable ADMET profiles were observed, hepatotoxicity alerts were predicted for all compounds, which represents an important limitation supporting the prioritization of X14. In general, this study provides. Molecular dynamics simulations over 100 ns supported stable complex formation, with RMSD backbone values stabilizing around 2.5 to 3.0 Å. The ligands remained bound within the binding pocket throughout the simulation, exhibiting RMSD values below 1.5 Å for X14 and temozolomide and below 2.5 Å for N27, while the PLK1–TMZ complex showed higher structural fluctuations. A plausible synthetic route was proposed to assess the experimental feasibility of X14. Overall, these computational findings support the prioritization of X14 for further experimental validation in glioblastoma therapy.

Molecular Docking and Pharmacological In Silico Evaluation of Genistein and Hematoxylin as Promising Ribonucleotide Reductase-targeted Therapies for Cancer.

Genistein and hematoxylin demonstrate promising molecular interactions and pharmacological profiles as potential natural RR inhibitors and supports further preclinical development as anticancer agents.

Computational blueprint and stereochemical validation of a small peptide derived from Lacticaseibacillus casei VITCM05 targeting estrogen receptor alpha and human epidermal growth factor receptor 2

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.

In silico evaluation of novel metabolites from Tricholoma pardinum: Insights into drug-likeness, toxicity and target interactions via molecular docking and dynamics.

Natural products from fungi are a significant source for drug discovery. This study investigates six previously uncharacterized metabolites isolated from the poisonous mushroom Tricholoma pardinum using an integrated in silico approach to evaluate their therapeutic potential. Pharmacokinetic (ADMET) profiling predicted varied drug-likeness and toxicity profiles, with several compounds showing potential to cross the blood-brain barrier. Molecular docking and MM-GBSA calculations identified compound 1 as a potent inhibitor of Poly [ADP-ribose] polymerase 1 (PARP1), with a strong binding affinity (XP GScore: -6.756; ΔGbind: -48.75 kcal/mol). This interaction is anchored by a robust network of hydrogen bonds with key residues, including ASP914, CYS908, and THR866. Similarly, compound 6 emerged as a strong binder to Phosphatidylinositol 5-phosphate 4-kinase type-2 gamma (PIP4K2γ) (XP GScore: -7.705; ΔGbind: -45.94 kcal/mol), with its binding stabilized by extensive hydrophobic interactions complemented by a critical hydrogen bond with the residue Methionine 206 (MET206). Subsequent 100 ns molecular dynamics simulations confirmed the high stability of both protein-ligand complexes, validating the persistence of these key interactions. These computational findings highlight the potential of metabolites from T. pardinum as novel scaffolds for developing anticancer agents targeting PARP1 and PIP4K2γ, warranting further experimental validation.

Pharmacophore-based identification and molecular docking of CCR5 inhibitors: insights from dynamic simulations

Abstract The chemokine receptor type 5 (CCR5) plays a crucial role in HIV-1 entry into host cells, making it a key therapeutic target. This study aimed to identify novel CCR5 inhibitors through pharmacophore-guided virtual screening and molecular dynamics (MD) simulations using ZINC-derived compounds. The 3D structure of CCR5 (PDB ID: 4MBS) was used as the target receptor. A ligand-based pharmacophore model was generated using the Pharmit server, with Maraviroc as a reference molecule. Virtual screening of ZINC compounds was conducted based on pharmacophore features and Lipinski’s Rule of Five. The top 10 compounds were subjected to molecular docking, ADMET analysis, and MD simulations using GROMACS to evaluate stability and binding dynamics. Among all screened ligands, ZINC000000867238 exhibited the strongest binding affinity (–10.0 kcal/mol), forming hydrogen bonds with Tyr251 and Glu283, and hydrophobic interactions with Trp86, Phe182, and Tyr108. The compound demonstrated favorable ADMET characteristics, including high absorption, non-mutagenicity, and absence of hERG inhibition. MD simulation confirmed its stability, with RMSD fluctuations between 0.20–0.38 nm, low RMSF deviations in active site residues, and a compact radius of gyration (∼2.48 nm) compared to apo-CCR5. The integrated computational approach identified ZINC000000867238 as a potent and stable CCR5 inhibitor candidate, warranting further in vitro and in vivo validation as a potential HIV-1 entry blocker.

Integrated molecular docking, toxicity prediction, and molecular dynamics simulations studies to identify potential antimalarial candidates targeting enoyl-acyl carrier protein reductase enzyme