Journal of Computer-Aided Molecular Design最新文献

Aptamers are short oligonucleotides capable of binding to various molecular targets with high affinity and specificity. These short sequences are conventionally selected through the systematic evolution of ligands by exponential enrichment (SELEX) process. In this study, the non-SELEX in silico strategy was used to simulate the process of aptamer synthesis and subsequent affinity evaluation. We hypothesized that a candidate RNA aptamer could function as an antagonist to nuclear thyroid hormone receptors (TRs), thereby inhibiting their interaction with thyroid hormone response elements (TREs). Using knowledge-based approaches, TRE sequences were retrieved from the literature, and representative loci across the human genome were modeled. Through RNA structure prediction, molecular docking, and molecular dynamics simulations, several single-stranded RNA aptamers with strong binding affinity toward TRs were identified. Among them, one candidate demonstrated the most favorable interaction with thyroid hormone receptor alpha. Pending experimental validation, this aptamer holds potential as a novel therapeutic agent for hyperthyroidism by acting as a TR-blocking molecule.

Tumor angiogenesis, largely driven by VEGFR2 signalling, is a critical hallmark of cancer progression. In this study, we employed a structure-based virtual screening approach of pyrrolopyrimidine analogs from a natural product database, combined with density functional theory (DFT), molecular docking, and molecular dynamics (1 μs) simulations, to identify potential VEGFR2 inhibitors. Binding free energy (MM-GBSA) calculations were used to refine candidate selection. Three top-ranking compounds, CNP0279613, CNP0102100, and CNP0004587, were identified, with CNP0279613 showing the most favourable stability and binding affinity. Biophysical validation using isothermal titration calorimetry confirmed strong binding of CNP0279613 to VEGFR2, while in vitro MTT assays in HUVEC cells demonstrated its superior anti-angiogenic activity compared to the other candidates. Notably, its inhibitory effect was comparable to that of Ramucirumab, an FDA-approved VEGFR2 inhibitor. Together, these computational and experimental findings highlight CNP0279613 as a promising lead scaffold for the development of next-generation anti-angiogenic therapies and warrant further optimization and in vivo evaluation.

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BRAF mutations were first discovered by Davies et al. in 2002. BRAF^V600E mutation is the most prevalent, accounting for approximately 90% of all BRAF mutations. BRAF^V600E mutations have been identified at varying frequencies across multiple human cancers, including malignant melanoma (70–90%), thyroid cancer (45–50%), colorectal cancer (5–20%), and others. In this study, we designed a series of pyrimidine-sulfonamide hybrids, inspired by first- and second-generation FDA-approved BRAF inhibitors such as sorafenib, dabrafenib, and vemurafenib. The designed compounds were intended to target the αC-OUT/DFG-IN conformation of the BRAF^V600E mutant protein. Eighteen compounds (B1–B18) were synthesized and characterized using spectral techniques. Molecular docking and MD simulations were carried out to assess their binding affinity and stability with the BRAF^V600E protein. Kinase inhibition was assessed using a BRAF^V600E specific assay, and anticancer activity was tested against HCT-116, A375, HT-29, and TPC-1 cell lines. Among the tested derivatives, B14 exhibited the highest cytotoxicity against HCT-116, B8 was most effective against A375, B18 showed potent inhibition in HT-29, and B3 demonstrated the strongest activity in TPC-1 cells. All four compounds exhibited activity comparable to sorafenib. Notably, B4 emerged as the most potent BRAF^V600E kinase inhibitor in assays.

A series of six new compounds (1–6) were synthesized through the implementation of chemical reactions, employing the starting material 4-aminobenzophenone and six distinct aldehyde derivatives. The antiproliferative activities of the compounds 1–6 were evaluated to assess their potential as anticancer agents. Considering that structurally similar compounds have been reported as tubulin polymerization inhibitors, in silico studies were conducted to investigate the binding interactions of the synthesized derivatives with the colchicine-binding site of tubulin. Molecular docking studies indicated favorable binding affinities for all compounds toward the target site. Furthermore, molecular dynamics (MD) simulations confirmed the stability of the ligand–tubulin complexes, supporting the potential of these 4-aminobenzophenone derivatives as candidate tubulin-targeting anticancer agents.

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This study investigates the potential of isolated compounds from Paeonia lactiflora to mitigate heat stress-induced male infertility in Drosophila melanogaster, with egg-hatching rates as a quantitative fertility indicator. Exposure to thermal stress (27.5 °C) significantly impaired male fertility, resulting in egg viability declining to 16.18–23.08%. Supplementation with 10 µM of paeoniflorin (1), benzoic acid (2), and albiflorin (4) significantly restored egg-hatching rates to 55.17–93.48%, demonstrating protective effects against heat stress-induced reproductive impairment. Immunofluorescence analysis of testis tissue revealed that these compounds maintained spermatogonia structural integrity under thermal stress conditions. Molecular docking analyses identified specific binding interactions between compounds 1, 2, and 4 with Vasa protein, characterized by distinct patterns of hydrogen bonding, van der Waals forces, and hydrophobic interactions. Paeoniflorin (1) exhibited the highest binding affinity (− 9.64 kcal/mol), followed by compound 4 (− 9.14 kcal/mol), while compound 2 demonstrated a lower binding affinity. Molecular dynamics simulations conducted over 200 ns confirmed the thermodynamic stability of these complexes, with root mean square deviation values converging around 0.2 nm for all compounds. Analyses of root mean square fluctuation, hydrogen bond numbers, and molecular contact surface area provided further evidence of complex stability. Moreover, the free energy landscape and MM/PBSA analyses revealed that van der Waals and electrostatic interactions make significant favorable contributions to the thermodynamics of the system. These findings elucidate the molecular mechanisms by which secondary metabolites from P. lactiflora protect against heat stress-induced male reproductive dysfunction, offering potential therapeutic strategies for mitigating heat-induced infertility.

Traditional usage and in vitro studies have previously proven the effects of soluble epoxide hydrolase (sEH) inhibitors isolated from Phyllostachys bambusoides. A phytochemical investigation of Phyllostachys bambusoides led to the isolation of six known compounds: one phenolic amide moschamine (1), three flavonoids, including tricin (2), salcolin A (3), and luteolin 6-C-α-L-arabinopyranoside (4), as well as two neolignans (5–6). The structures of these compounds were determined spectroscopically; their nuclear magnetic resonance spectra were compared to reported spectra. The sEH inhibitory activity of all isolated compounds was examined. Compounds 1‒4 exhibited strong sEH inhibitory activity with IC₅₀ values of 30.6, 57.5, 16.8, and 11.7 µM, respectively. Kinetic analyses of most potent compounds, 3 and 4, revealed that they were non-competitive inhibitors of sEH. The resulting molecular docking and molecular dynamics simulations have increased our understanding of the dynamic behavior of receptor–ligand binding between these compounds. Our findings suggest that flavonolignan and flavone derivatives from P. bambusoides leaves show promise as potential natural sEH inhibitors.

A library of 39 amino-benzoxazole derivatives, selected from 57 benzoxazole compounds in the NCI database, was evaluated for their potential as KDR inhibitors using computational docking methods, including CDocker, LibDock, and AutoDock Vina. At a screening concentration of 100 µM, 11 compounds demonstrated over 40% KDR inhibition, with six showing notable activity. The IC50 values of the top six compounds ranged from 6.855 to 50.118 µM, with compound 1 showing the highest inhibitory activity (IC₅₀ = 6.855 µM). Docking studies revealed that compound 1 achieved an AutoDock Vina score of − 7.5 kcal/mol, CDocker energy of − 41.4, and a LibDock score of 140.9 against KDR, indicating strong binding affinity compared with the positive control, sorafenib (AutoDock Vina − 10.7 kcal/mol, CDocker − 43.76, LibDock 96.7). Anti-proliferative assays against A549 and MCF-7 cancer cell lines showed that compounds 16 and 17 were the most effective against A549 cells, achieving inhibition rates of 79.42% and 85.81%, respectively. Compounds 16 and 17 also exhibited the highest activity against MCF-7 cells (IC50 = 6.98, 11.18 µM), respectively. The docking scores for compounds 16 (KDR: Vina − 8.9, CDocker − 32.15, LibDock 105.7) and 17 (KDR: Vina − 11.1, CDocker − 19.15, LibDock 121.9) support their potent interactions with the KDR target. These results suggest that selection of aminobenzoxazole derivatives may serve as promising anticancer agents, potentially through inhibition of KDR, EGFR, and FGFR1 pathways. Future work will focus on optimizing compound 1 to enhance therapeutic efficacy and exploring the roles of EGFR and FGFR1 pathways in the activities of compounds 16 and 17. Additionally, the relatively limited dataset constrained the statistical power for quantitative modeling; we plan to expand the aminobenzoxazole library and develop a validated 3D-QSAR model to visualize pharmacophoric hotspots and guide structure-based lead optimization.

Understanding active functional class (agonist vs antagonist) at the human μ-opioid receptor (μOR) is critical for drug discovery and safety assessment. While recent machine learning models such as ExtraTrees (ET) and message-passing neural networks (MPNNs) achieved ROC AUC scores of 0.915 ± 0.039 and 0.918 ± 0.044, respectively, it remains unclear how target-conditioned interaction features influence functional class detection and how resampling choices (e.g., SMOTE) impact robustness when evaluated under identical, fixed splits. Therefore, we introduce the μOR-Ligand framework—a target-aware view-based hybrid feature selection to improve performance in identifying whether an active ligand is an agonist or antagonist. To realize μOR-Ligand, three views have been constructed: (1) fingerprint, (2) ligand descriptors, and (3) molecular interaction features, yielding a comprehensive feature space of 114,552 variables (1190 fingerprints, 1618 ligand descriptors, 111,741 interaction descriptors). Feature selection is performed per view to obtain three view-specific subsets; each trains a base learner, and their out-of-fold predictions are fused via a linearly weighted multimodel feature selection stage. In parallel, the three selected feature sets are merged and trained with a stacking model (ensemble feature selection). Finally, μOR-Ligand forms a view-based hybrid feature selection by linearly combining the multimodel and ensemble outputs. Such a target-aware view-based hybrid feature selection for the stacked ensembles framework achieved an improved ROC AUC of 0.930 ± 0.026, supported by a promising significant p-value of 0.046 and a t-statistic of 1.707 (> t-critical=1.663) against the recent model, MPNNs. Also, μOR-Ligand further increased ROC AUC to 0.977 on internal cross-validation, as the highest ROC AUC score. In addition, μOR-Ligand is evaluated under a resampling-controlled μOR evaluation protocol that pairs ± SMOTE on identical, fixed splits. Overall, the study (1) demonstrates that target-aware interaction features, though weak alone, contribute a complementary signal in multimodel fusion, improving performance in functional classification and stability, and (2) establishes a resampling-controlled evaluation protocol for μOR modeling, and (3) identifies correlations between top features and μOR pocket chemistry/residues, and (4) case study to show effectiness on unseen external data, as a real-world application. Overall, the study demonstrates that hybridizing ligand-based and target-conditioned views—via target-aware view-based hybrid feature selection for stacked ensembles—adds complementary signal beyond ligand-only baselines, particularly for functional class (agonist vs antagonist).

Zerumbone is a natural sesquiterpene compound from Zingiber zerumbet plant. While it significantly exhibits analgesic properties through the μ-opioid receptor (μOR) found in animal models, its precise molecular mechanism at the receptor level remains poorly investigated. The present work involves 1-µs molecular dynamics (MD) simulations, MM/PBSA binding-free energy analyses, principal component analysis (PCA) as well as Markov state modeling (MSM) to address how the dynamic basis of zerumbone-μOR interactions compared to morphine, which is a known full agonist. MD trajectories reported greater receptor backbone fluctuations, improved loop mobility, reduced stable hydrogen bonds, and moderate receptor compaction in the zerumbone-bound state in contrast to morphine. MM/PBSA calculations indicated similar total binding affinity and the driven for zerumbone affinity was primarily hydrophobic interaction. PCA recognized notable intermediate conformational substates that were stabilized by zerumbone. In the interim, highly stabilized intermediate-activation macrostate with high-kinetic barriers (~ 8–16 k_BT) and millisecond-scale residency was also revealed through MSM analysis. In agreement with analgesic activities reported previously, these computational insights identify zerumbone as a low-efficacy partial agonist, providing comprehensive molecular explanation to its analgesic profile to serve as a source of safer and more opioid-like drugs.