Search Results (630)

Triple-negative breast cancer (TNBC) is a clinically aggressive malignancy with extremely limited effective targeted therapies. Natural products are promising alternatives for anticancer drug discovery, whereas integrated computational approaches serve as efficient tools for novel lead identification. Herein, four novel spiro-polycyclic sesquiterpene [4+2] trimers (Inubritantrimers A–D) and eight synthetic derivatives from Inula britannica L. were investigated via DFT calculations at the ωB97xD/6-311++G(2d,p) level (for geometric, electronic, spectral, and reactivity parameters), network pharmacology, molecular docking against seven core breast cancer-related targets, 500 ns all-atom molecular dynamics (MD) simulation, and MM/PBSA analysis. The results showed that the endo-type cycloaddition products had superior structural stability, with all reactions thermodynamically spontaneous (ΔG < 0). Compound 11 exhibited the most potent and balanced binding activity, with a docking free energy of −13.45 kcal/mol to MTOR; MD and MM/PBSA confirmed stable complex formation (total binding free energy −21.13 kcal/mol), driven predominantly by hydrophobic interactions. This study first established a comprehensive stereochemistry–electronic structure–property–activity relationship for this rare sesquiterpene trimer class and identified compound 11 as a promising MTOR-targeted TNBC lead. It provided a theoretical basis for developing high-efficiency, low-toxicity natural anticancer agents. Full article

Molecular mechanics/the Poisson–Boltzmann surface area (MM-PBSA) is a popular method for binding energy estimation. Several programs have been developed for performing MM-PBSA calculations in conjunction with Gromacs, the most popular molecular dynamics (MD) software. However, current programs are limited to Linux-based systems and lack cross-platform usability. To address this, we present s_mmpbsa, a lite and cross-platform MM-PBSA program, to support binding energy calculation on native Windows platforms without a subsystem. By incorporating electrostatic screening and interaction entropy, s_mmpbsa achieves improved binding free energy calculation accuracy, validated on a dataset of HIV-1 protease inhibitor complexes. In addition, s_mmpbsa achieves enhanced performance with g_mmpbsa in the same parameters and conditions. Indeed, s_mmpbsa offers an efficient and practical solution for interaction energy calculation from MD simulations in Gromacs, providing valuable protocols for further molecular design applications such as computational enzyme design and molecular screening. Full article

Histone deacetylase inhibitors (HDACi) are an emerging class of epigenetic anticancer drugs that exert their activity through coordination to the catalytic Zn²⁺ ion within the active site of histone deacetylases (HDACs). Due to the limited isoform-selectivity of hydroxamic acid-based inhibitors, benzamide-based HDACi (BBHDACi) have been developed as subtype-selective alternatives. Clinically relevant representatives include Chidamide, Entinostat, Mocetinostat, Zabadinostat, and Tacedinaline. Although these compounds share a conserved o-aminoanilide zinc-binding group (ZBG), they differ in linker and cap region structure, raising questions regarding their intrinsic Zn²⁺ affinity and coordination behavior. Herein, density functional theory (DFT) calculations were performed at the B3LYP/6-311++g(d,p) level of theory combined with the PCM solvation in methanol (ε = 33) and water (ε = 78). Geometry optimization confirmed that the trans (E) isomer of Chidamide is thermodynamically preferred. Coordination studies showed that the remaining BBHDACi adopt stable geometries, with the o-aminoanilide group preferentially forming tetracoordinated complexes that are more stable than hexacoordinated ones in polar media. Interestingly, calculated substitution free energies differed by less than ± 2 kcal.mol⁻¹, indicating nearly identical intrinsic Zn²⁺ affinities across the series. These results suggest that the ZBG contributes similarly to metal coordination across all BBHDACi, whereas the overall binding strength is mainly governed by interactions of the linker and cap regions rather than by the conserved zinc-binding group itself. Full article

Coronavirus disease 2019 continues to pose global health challenges, with the pandemic significantly burdening several economies, healthcare systems, and the social lives of individuals. Furthermore, new cases continue to be reported, underscoring the need for therapeutic strategies targeting conserved regions and host–virus interactions. Building on earlier virtual screening for small molecules, all-atom molecular dynamics simulations and binding-free-energy calculations were performed to elucidate how the two previously identified small molecules (NSC36398 and NSC281245) may affect the dynamic behaviour of the interaction between heat shock 70 kDa protein 8 (HSPA8) and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. Post-MD analyses refined prior docking predictions, where NSC281245 was found to bind tightly to the complex with limited perturbations at the HSPA8-spike protein interaction surface, whereas NSC36398 appeared to induce allosteric-like domain-level destabilisation effects while maintaining stable polar contacts with the protein. Our findings demonstrate the potential of NSC36398 as a promising modulator for disrupting the HSPA8-spike protein complex, which may serve as a structural lead for designing next-generation inhibitors of host–virus interactions. Full article

Background/Objectives: Immune evasion remains a critical barrier to effective hepatocellular carcinoma (HCC) therapy. Lactate dehydrogenase A (LDHA) drives lactate accumulation and histone lysine lactylation (Kla), reshaping the immunosuppressive microenvironment, while bromodomain-containing protein 4 (BRD4) sustains B7-H3 transcription via super-enhancer occupancy. Despite their synergistic roles in the lactate–Kla–B7-H3 immunosuppressive axis, no dual-target inhibitor simultaneously engaging both proteins has been reported. This study aimed to discover dual LDHA/BRD4 inhibitors from natural product libraries using an integrated AI-driven computational pipeline. Methods: We established a multi-tier virtual screening cascade comprising Lipinski/QED drug-likeness filtration, DiffDock-based AI docking, QuickVina binding energy validation, PLIP interaction profiling, 200 ns all-atom molecular dynamics simulations, MM-GBSA binding free energy calculations, and density functional theory analysis. Natural product libraries from COCONUT and CMNPD databases (84,730 compounds post-filtration) were screened against both targets. Results: High-throughput DiffDock screening identified 11 dual-target hits, from which CNP0038114.1 and CMNPD16582 emerged as prioritized lead candidates. All four protein–ligand complexes maintained structural stability throughout MD simulations, with MM-GBSA binding free energies ranging from −27.24 to −32.45 kcal/mol, predominantly driven by van der Waals interactions. DFT calculations revealed distinct electronic profiles: CNP0038114.1 exhibited a narrow HOMO–LUMO gap (2.718 eV) favoring charge-transfer reactivity, whereas CMNPD16582 displayed a larger gap (4.822 eV), suggesting superior chemical stability. Conclusions: This computational study furnishes two novel natural product leads for targeting the lactate–Kla–B7-H3 immunosuppressive axis in HCC, establishing a generalizable AI-driven workflow for dual-target inhibitor discovery. Full article

Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10⁹ compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design–make–test cycle, increase hit novelty, and improve decision-making in early drug development programs. Full article

Background/Objectives: Longer oligoarginines are very effective cell-penetrating peptides. It has been shown that a minimal number of positively charged side chains is necessary for efficient cellular uptake. But a highly positively charged peptide may interact with its cargo molecule, thereby reducing its efficiency. Several chemical modifications were tested to improve the internalization of short tetraarginine derivatives. Aromatic groups, such as Dabcyl at the N-terminus, Trp in the sequence, and AMBA or PABA in the backbone, were used to improve internalization. The other useful modification was the aza-glycine substitution in the case of penetratin. Methods: In this study, the effect of aza-glycine insertion into the peptide Dabcyl-RRRRK(Cf) on internalization was studied and compared with that of the Trp-modified peptide Dabcyl-RRWRRK(Cf). To explain the noticed difference in the biological activity of peptides, DFT calculations and the prediction of membrane-binding free energy (ΔΔF) from a peptide sequence were performed. Results: It turned out that the position of the aza-glycine moiety does not have an influence on the cellular uptake. The aza-glycine-containing peptide showed higher internalization than the Dabcyl-RRRRK(Cf) peptide. Besides this, these peptides have similar or higher cellular uptake than that of octaarginine at lower concentrations (c < 2 µM). The aza-glycine affected not only cellular uptake but also the entry mechanism. The structure of peptides depended on the amino acids (Trp, Gly, or azaGly) in their sequences and their positions. Conclusions: These may result in the different amphiphilicity of peptides, and thus changes in the hydrophobic moment and in the binding affinity of peptides to the negatively charged membrane surface. Full article

Coumarins represent a distinctive class of non-classical carbonic anhydrase inhibitors that interact with the entrance region of the catalytic pocket rather than directly coordinating the catalytic Zn²⁺ ion. In this study, a series of glycosylated coumarins was synthesized through a copper-catalyzed multicomponent reaction involving propargyl glycosides, salicylaldehyde, and tosyl azide, providing efficient access to iminocoumarin-based glycosides derived from natural carbohydrates. The inhibitory activity of the synthesized compounds was evaluated against human carbonic anhydrase isoforms I, II, IX, and XII using a stopped-flow CO₂ hydrase assay. The compounds showed negligible inhibition of the cytosolic isoforms hCA I and hCA II, while displaying moderate activity toward the tumor-associated isoforms hCA IX and hCA XII, with Ki values ranging from 12.9 to 41.8 μM. Among the series, 6-O-(2H-chromene-2-one-3-yl-methyl)-D-galactopyranose (10a) emerged as the most potent inhibitor of hCA IX and XII. Structure–activity relationship analysis indicated that deprotected glycosyl derivatives exhibit improved inhibitory activity compared to protected analogues. To rationalize these observations, molecular docking followed by molecular dynamics simulations and MM-GBSA binding free energy calculations were performed for both anomeric forms of compound 10a. The computational results revealed a clear preference for the β-anomer, particularly in hCA IX and XII, where favorable interactions with catalytic threonine residues and isoform-specific aromatic residues stabilize the ligand within the active-site entrance. These findings provide a molecular explanation for the experimentally observed selectivity and highlight glycosyl coumarins as potential starting points for further optimization toward selective inhibitors of tumor-associated carbonic anhydrases. Full article

Understanding the intrinsic Li–H₂ interaction, decoupled from substrate effects, is essential to rationalize the performance of lithium-decorated hydrogen storage materials. To address the current lack of a clean theoretical baseline, we characterized the sequential H₂ adsorption on the gas-phase Li₆ superatomic cluster using high-level density functional theory (DFT), complemented by Energy Decomposition Analysis (EDA), QTAIM, and NICS(0) calculations. Li₆ acts as a structurally rigid platform (RMSD < 0.032 Å) where ligand-induced polarization progressively strengthens its σ-aromaticity (NICS(0) from −2.917 to −13.98 ppm) and increases the HOMO–LUMO gap up to 5.05 eV. EDA identifies the binding as field-activated physisorption, electrostatically dominated (65–67%) and mechanistically distinct from Kubas coordination, as confirmed by QTAIM closed-shell interaction parameters. Negative cooperativity governs an effective loading capacity of n = 2 molecules under cryogenic conditions (T_eq = 143.76 and 114.64 K), while an entropic bottleneck renders higher loading non-spontaneous at all temperatures. These results establish Li₆(H₂)_n as a foundational gas-phase reference, providing a systematic, contamination-free descriptor set for the intrinsic Li–H₂ interaction. This framework is essential for isolating the electronic role of the lithium superatom and unambiguously identifying substrate-induced modulations in supported hydrogen storage materials. Full article

Human cytomegalovirus (HCMV) remains a significant cause of morbidity in immunocompromised patients, necessitating the development of improved antivirals. Using an integrated in silico and in vitro approach, we identified a novel ligand (NL) as a letermovir analog with enhanced binding affinity and reduced cytotoxicity. A pUL56 terminase subunit model generated with AlphaFold 3 was used for the virtual screening of a 15,000-compound library. Among the 73 candidates with structural similarity to letermovir (Tanimoto ≥ 0.6), NL exhibited superior predicted binding affinity (ΔG_bind = −10.7 kcal/mol). In silico toxicity prediction (ProTox 3.0) classified NL as having low toxicity (class 4, LD50 ≈ 1000 mg/kg), which was confirmed in vitro, where NL demonstrated 158-fold less toxic (CC50 = 2.69 mg/mL) in MRC-5 cells than letermovir (0.017 mg/mL). Molecular dynamics simulations over 500 ns revealed that the pUL56-NL complex forms a more thermodynamically stable interaction, with a lower calculated free energy of binding (MMGBSA: −40.89 ± 7.40 kcal/mol vs. −32.76 ± 4.96 kcal/mol) and a narrower free energy landscape. These results establish NL as a promising, low-cytotoxicity candidate with enhanced target engagement, warranting further investigation as a potential anti-HCMV therapeutic. Full article

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by cholinergic dysfunction, amyloid-β aggregation, mitochondrial stress, and aberrant kinase activity. Carotenoids, naturally occurring pigments with antioxidant and neuroprotective properties, have emerged as promising candidates for AD intervention. In this study, we performed a systematic stepwise computational screening of a large carotenoid library (n = 1191) to identify multitarget candidates against AD–related proteins. The workflow consisted of predefined ADMET filtering (oral absorption > 90%, Caco-2 > 0.9, logBB > −1, and absence of major CYP inhibition and toxicity alerts), reducing the dataset to 61 compounds, followed by multi-target molecular docking against AChE, BChE, BACE-1, MAO-B, and GSK3-β. Compounds were ranked using an aggregated mean docking score across all five targets, and the top-performing candidate was subjected to detailed mechanistic analyses. Hopkinsiaxanthin emerged as the highest-ranked multitarget carotenoid and was further evaluated using frontier molecular orbital (FMO) analysis, pharmacophore modeling, 100 ns molecular dynamics (MD) simulations, MM/PBSA binding free energy calculations, and per-residue decomposition. Docking predicted favorable estimated binding affinities toward all targets. MD simulations confirmed stable receptor–ligand complexes with low RMSD values (0.278–0.285 nm). MM/PBSA analysis indicated favorable binding free energies, particularly for GSK3-β (−22.73 kcal/mol) and AChE (−21.50 kcal/mol). Per-residue decomposition identified key hotspot residues driving stabilization. Overall, this structured computational framework identifies Hopkinsiaxanthin as a promising multitarget scaffold and supports its prioritization for experimental validation in AD models. Full article

Obesity represents one of the most critical global public health challenges. Pancreatic lipase (PL) serves as a key therapeutic target for obesity control, whereas clinical synthetic PL inhibitors are greatly restricted by adverse reactions. Traditional Chinese medicines (TCMs) have a long-standing history in regulating lipid metabolism and ameliorating obesity-related disorders, and are characterized by remarkable structural diversity, low toxicity, and mild side effects, thus representing a promising source for developing safe and efficient PL inhibitors. In this work, an integrated strategy combining in silico screening and in vitro validation was employed to identify potential PL inhibitors from TCM components, including molecular docking, molecular dynamics simulation, MM/PBSA binding free energy computation, and in vitro enzymatic assay. Six compounds with docking scores ranging from −9.9 to −9.0 kcal/mol were selected for further investigation. Molecular dynamics simulations verified the favorable structural stability of the corresponding ligand–PL complexes, and MM/PBSA calculations demonstrated negative binding free energies from −21.24 ± 0.39 to −12.03 ± 0.40 kcal/mol. In vitro experiments indicated that three compounds (Hydroxygenkwanin, Atractylenolide I, and Peiminine) showed effective PL inhibitory activity, with IC₅₀ values of 0.128 ± 0.009, 0.584 ± 0.031, and 0.748 ± 0.042 mM, respectively. These values are comparable to quercetin (0.231 ± 0.034 mM) but significantly higher than orlistat (0.481 ± 0.023 μM), which is attributed to their non-covalent binding pattern. Collectively, this study validated the reliability of the integrated in silico and in vitro screening strategy, identified three effective pancreatic lipase inhibitors derived from TCMs, established a robust paradigm for the discovery of natural PL inhibitors, and laid a solid foundation for subsequent research on natural anti-obesity agents. Full article

Using density functional theory (DFT) and the Gaussian 09 program, the study calculated Gibbs free energy to understand how easily each NP can transform. Results showed that only 2,6-dinitrophenol (2,6-DNP) and 2-chloro-6-nitrophenol (2-Cl-6-NP) had Gibbs free energies above 0 kJ/mol. The study also evaluated the toxicity of the NPs, leading to the identification of trinitrophenol (TNP), 2-chloro-4-nitrophenol (2-Cl-4-NP), and 2-nitrophenol (2-NP) with the highest risk scores. In the present study, binding energies were used only as comparative indicators of enzyme–substrate interaction favorability within a screening framework, rather than direct measures of catalytic degradation efficiency. The enzyme 1,2-dioxygenase from Acinetobacter baylyi ADP1 showed strong degradation effects on catechol, with significant binding energies for 2-NP, 2-Cl-4-NP, and TNP. The PS-AOP changed the degradation environment, which reduced enzymatic efficiency. The study also modified specific amino acids in enzymes to improve their performance. For example, the enzyme 1DLT-6 had a degradation increase of nearly 27% compared to the reference enzyme. Finally, we tried to measure the impact of different forces on the breakdown of nitrophenols by enzymes. We used a two-dimensional amino acid map based on enzyme–ligand interactions and a visualization of non-covalent interactions. Our findings show that van der Waals forces and electrostatic forces are the main factors affecting how well the material breaks down. From a sustainability perspective, the study highlights a promising strategy for mitigating secondary pollution, improving the environmental compatibility of PS-AOP-based remediation, and supporting safer and more sustainable restoration of petroleum hydrocarbon-contaminated soil and groundwater. These findings help strengthen the theoretical basis for developing greener post-oxidation remediation pathways. Full article

Cyclin-dependent kinases 4 and 6 (CDK4/6) play a central role in the regulation of cell cycle progression and represent important therapeutic targets in hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2−) breast cancer. The introduction of selective CDK4/6 inhibitors, including palbociclib, ribociclib, and abemaciclib, in combination with endocrine therapy, has significantly improved clinical outcomes and has become a standard treatment strategy in both metastatic and high-risk early-stage disease. Nevertheless, treatment resistance and disease progression remain major clinical challenges. A deeper understanding of the structural characteristics of CDK4/6 and the molecular basis of inhibitor binding is therefore essential for improving therapeutic strategies and guiding the development of new targeted agents. This review provides an integrated overview of the structural features of CDK4/6 and their role in cell cycle regulation, summarizes the clinical development and major clinical trials of currently approved CDK4/6 inhibitors, and discusses recent computational studies investigating inhibitor binding and conformational dynamics. Particular attention is given to the application of in silico approaches, including molecular docking, molecular dynamics simulations, and binding free-energy calculations, which provide insights into mechanisms of therapy resistance and potential strategies to overcome them and support the identification and optimization of novel CDK4/6-targeted therapeutic candidates. By integrating structural, clinical, and computational perspectives, this review highlights current knowledge and emerging directions in CDK4/6 research that may advance the development of more personalized therapies for HR+/HER2− breast cancer, while accounting for both intrinsic and de novo resistance mechanisms. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters host cells when the spike receptor-binding domain (RBD) engages angiotensin-converting enzyme 2 (ACE2). Cannabinoid scaffolds have recently been reported to bind S1/RBD, block spike-mediated membrane fusion, and modulate host inflammatory pathways, making them attractive candidates for entry inhibition. Here, we applied an integrated computational pipeline to prioritize cannabis-derived compounds as interfacial blockers of the RBD–ACE2 complex across variants. Eleven phytocannabinoids were docked into the wild-type (WT) RBD–ACE2 interface, identifying three cavities, with ligands preferentially occupying pocket 1. Complexes were subjected to triplicate 200 ns all-atom molecular dynamics (MD) simulations for WT, Delta, and Omicron BA.1 RBD–ACE2. Binding energetics were quantified using molecular mechanics/generalized Born surface area (MM/GBSA) and solvated interaction energy (SIE), and per-residue contributions were analyzed together with solvent-accessible surface area (SASA) and residue interaction networks. Among all compounds, cannabidiol (CBD) and cannabinol (CBN) were the only ligands that remained stably bound in pocket 1 for all variants. CBN showed the most favorable ligand–complex binding in WT, whereas CBD preserved favorable binding in Omicron BA.1 despite reduced interface burial, indicating that van der Waals/electrostatic complementarity and solvation, rather than surface coverage alone, govern affinity. Both ligands weakened modeled RBD–ACE2 binding by perturbing hot-spot residues centered on Y505 or N501Y in RBD and E37, A387, and R393 in ACE2. Overall, our results highlight CBD and CBN as tractable, variant-spanning interface disruptors and illustrate how MD-based free-energy calculations can support computational drug discovery against evolving viral protein–protein interfaces. Full article