SAR and QSAR in Environmental Research最新文献

In this study, an integrative computational approach combining molecular descriptors, genetic algorithm - multiple linear regression (GA-MLR), and toxicokinetic simulations was employed to characterize the preliminary profile of Persistent Organic Pollutants (POPs) in various pelagic fish species from the Adriatic Sea. The molecular basis and toxicokinetic profile based on previously obtained experimental data on POP concentrations were followed by preliminary environmental characterization to assess the relationship between fish-specific parameters (e.g. species, lipid content) and determined pollutant concentrations. A GA coupled with MLR was applied to develop predictive models identifying the most influential physico-chemical properties of POPs that contribute to non-carcinogenic and carcinogenic health risks associated with chronic fish consumption. The toxicokinetic modelling and Hansen solubility parameters (HSPs) were used to assess the consumers' risk, gastrointestinal absorption and bioaccumulation. The use of GA-MLR and 3D-MoRSE descriptors directly linked chemical structure to environmental fate, bioaccumulation, and toxicological outcomes. The modelled concentrations of POPs in key organs and tissues were compared to in vivo data reported in the literature. This integrated analysis establishes a scientific basis for future toxicological and risk assessments of POPs in Adriatic pelagic fish, with emphasis on experimental validation and toxicokinetic profiling relevant to human exposure.

Accurate prediction of immunotoxic effects is essential for chemical safety evaluation and drug development. However, existing methodologies are limited by the scarcity of in vitro data and the inherent complexity of immune responses. This study introduces an interpretable quantitative structure-activity relationship (QSAR)-based modelling framework aimed at assessing immunosuppressive toxicity utilizing IC₅₀ data obtained from three human immune cell lines: Jurkat, peripheral blood mononuclear cells (PBMC) and THP-1. Three tree-based machine learning algorithms, in conjunction with robust feature selection techniques, were employed to identify critical molecular determinants associated with immunosuppressive effects. The implementation of SHapley Additive exPlanations (SHAP) enhanced model interpretability and facilitated the extraction of potential structural alerts, thereby providing mechanistic insights into immunotoxicity pathways. Our findings indicate that the integration of immune cell-specific experimental data with interpretable modelling approaches significantly enhances the reliability of immunotoxicity predictions. This research establishes a scientifically grounded framework that not only supports the early identification of immunotoxic chemicals but also promotes safer chemical design and informed decision-making in drug development and toxicological risk assessment.

The epidermal growth factor receptor (EGFR), a validated therapeutic target in oncology, demonstrates overexpression across multiple neoplastic cell types and plays a critical role in tumorigenesis. This investigation reports the strategic synthesis and evaluation of antiproliferative efficacy for novel sorbamide derivatives designed as EGFR inhibitors. Several synthesized compounds exhibited moderate inhibitory effects against EGFR-overexpressed A431 carcinoma cells, and among them, compound 7d demonstrated superior potency relative to the reference agent Gefitinib with an IC₅₀ value of 19.1 µM. A predictive 4D-QSAR model was successfully developed, exhibiting satisfactory statistical parameters ($r_{tr}^2$ = 0.81, $Q_{LOO}^2$ = 0.62, $Q_{LMO}^2$ = 0.60, $r_{\mathrm{Pred}}^2$ = 0.71). Complementary computational analyses through covalent docking and molecular dynamics simulations elucidated the molecular interaction mechanism, revealing covalent bond formation between the sorbamide scaffold and the conserved Cys797 residue in the EGFR catalytic domain.

The persistent challenge posed by multi-drug resistant Staphylococcus aureus infections worldwide necessitates new solutions. We describe the creation of a multi-epitope vaccine aimed at offering cross-strain immunity. Antigens α-haemolysin (Hla) and staphylococcal enterotoxin B (SEB) were chosen considering their high immunodominance and sequence conservation levels. B-cell and T-cell epitopes were combined into a multi-epitope vaccine with the proper adjuvant and linker sequences included to allow for maximum immunogenicity and structural stability. Physicochemical characterization demonstrated that the construct is non-allergenic, heat-stable, and immunogenic. Structural optimization and modelling were performed, with confirmation by Ramachandran plot analysis and ProSA z-score, which verified the correctness of the model. Molecular docking indicated robust and stable interactions between the vaccine and major immune receptors, such as TLR3, MHC class I, and MHC class II. In addition, 200 ns molecular dynamics simulations and binding free energy calculations indicated stability and longevity of these complexes. Codon optimization and in silico cloning indicated efficient expression in E. coli. Immune simulations also anticipated strong activation of humoral and cellular immune elements such as B-cells, cytotoxic T lymphocytes, and antigen-presenting cells, and rising Ig levels. The vaccine's ability to induce overall immune protection against S. aureus requires further experimental confirmation.

Pheromone-binding proteins (PBPs) help insects communicate effectively and regulate social behaviour by binding and transporting odorants. However, the precise atomic-level interactions of PBP1 in Loxostege sticticalis (LstiPBP1) with odorants remain poorly understood. Therefore, the three-dimensional structure of LstiPBP1 was constructed using homology modelling, after which complex structures of LstiPBP1 with six odorants (cis-3-hexenyl acetate, naphthalene, heptaldehyde, phenethyl alcohol, α-ionone, and (E)-11-tetradecenol), respectively, were obtained by molecular docking. Each complex underwent molecular dynamics simulations to investigate their detailed interactions. In silico site-directed mutagenesis was performed on the key residues to verify the accuracy of the simulation models. Energy analysis and interaction patterns revealed that hydrophobic interactions, mainly stemming from van der Waals interactions, are critical for the interaction between LstiPBP1 and these odorants. Additionally, hotspot residues on LstiPBP1 involved in interacting with different odorants were identified, providing further insight into the specific molecular interactions that govern their recognition. These results facilitate the development of inhibitors targeting the insect olfactory system.

Infectious diseases remain a significant public health threat with global socio-economic impacts. The increasing resistance to current antimicrobial therapies highlights the urgent need for new treatments with novel mechanisms of action. This study investigates the antimicrobial potential of two series of thiazolidinone derivatives, previously synthesized, using conventional and microwave-assisted methods. Antimicrobial activity was evaluated using the microdilution method, and the ability to inhibit Candida albicans biofilm formation was assessed. AutoDock 4.2® software was employed to explore potential molecular targets in bacteria and fungi. Series A compounds exhibited moderate to weak antibacterial activity (MIC/MBC: 1.50-6.00/3.00-12.0 mg/mL), with A8 being the most active (MIC: 1.5-6.0 mg/mL). Series B showed stronger antibacterial effects (MIC/MBC: 0.37-3.00/1.50-6.00 mg/mL), particularly compound B4 (MIC: 0.375-1.50 mg/mL). For antifungal activity, series A compounds were more effective (MIC/MFC: 0.37-3.00/0.75-6.00 mg/mL), with A3 showing the best results (MIC: 0.37-0.75 mg/mL). Series A also inhibited C. albicans biofilm formation, with A2 (57.7%), A4 (65.44%), and A8 (50.35%) outperforming ketoconazole (47%). These findings highlight A2, A4, and A8 as promising candidates for antibiofilm development, with A8 emerging as a lead compound due to its dual antibacterial and antifungal potency.

Enhancer of Zeste Homolog 2 (EZH2) inhibitors have demonstrated selective efficacy, but their broader therapeutic potential remains limited, highlighting the need to clarify the structural basis of their activity. The central aim of our study is to systematically analyse the structural diversity and activity patterns of known EZH2 inhibitors to provide insights that may guide incremental scaffold optimization. We examined 531 potential EZH2 inhibitors retrieved from ChEMBL through a cheminformatics workflow encompassing clustering, scaffold identification, activity cliff detection, and chemical space visualization. Using RDKit and NetworkX, 94 clusters were generated, of which 13 contained ten or more compounds. Notably, clusters 6, 16, 20, 21, and 31 exhibited favourable balances of structural homogeneity and enrichment scores, suggesting chemical cohesiveness and biological relevance for structure - activity relationship (SAR) prioritization. Statistical analyses revealed significant differences in mean pIC₅₀ values across clusters, underscoring distinct activity distributions linked to structural groups. Scaffold analysis highlighted pyrrole - benzamide derivatives, particularly those incorporating morpholine and piperidine motifs, as enriched among potent inhibitors. Substructure evaluation further indicated that aromatic rings and aromatic amine groups were positively correlated with bioactivity. These findings delineate key SAR features of EZH2 inhibitors and provide guidance for scaffold refinement, hit identification, and lead optimization.

Peptide quantitative structure-activity relationship (pQSAR) has been widely used in the computational peptidology community to model, predict and explain the activity and function of bioactive peptides. Various amino acid descriptors (AADs) have been developed to characterize the residue building blocks of peptides at sequence level. However, a significant issue is that the total number of AAD-characterized descriptors is proportional to peptide length, thus causing inconsistency in the resulting descriptor vector matrix for a panel of length-varying peptide sequences (LVPSs), which cannot be engaged in pQSAR modelling. Currently, only one AAD-based scaling approach, termed auto-cross covariance (ACC) that was proposed thirty years ago, is available for treating such issue. In this study, we described the second AAD-based multivariate method to do so, namely Residue Descriptor-Distance Vector (RDDV). The strategy characterizes a peptide sequence by using an inter-residue pseudo-interaction potential between different pre-assigned amino acid types involved in the sequence, which results in a given (invariable) number of descriptor parameters for different LVPSs. Here, the RDDV was tested, examined and validated in an in-house pQSAR-oriented bioactive peptide data cluster, which was explored systematically with combinations of different AADs and regression tools. We also compared RDDV with the traditional ACC in multiple aspects.

Phosphorylation plays an important role in the activity of CDK2 and inhibitor binding, but the corresponding molecular mechanism is still insufficiently known. To address this gap, the current study innovatively integrates molecular dynamics (MD) simulations, deep learning (DL) techniques, and free energy landscape (FEL) analysis to systematically explore the action mechanisms of two inhibitors (SCH and CYC) when CDK2 is in a phosphorylated state and bound state of CyclinE. With the help of MD trajectory-based DL, key functional domains such as the loops L3 loop and L7 are successfully identified. The results of FEL analysis show that the binding of CyclinE significantly enhances conformational stability of key functional regions of CDK2 (such as the L3 loop, L7 loop, and αC helix), while phosphorylation modification increases conformational diversity of the CDK2-related system. Further verification by quantum mechanics/molecular mechanics-generalized Born surface area (QM/MM-GBSA) calculations shows that binding of CyclinE can enhance the binding ability of inhibitors, while phosphorylation weakens this binding effect. Residue-based free energy estimation reveals the hot spot regions of inhibitor-CDK2 binding, providing crucial target information for structure-based drug design. This study provides theoretical foundations for the development of highly selective CDK2 inhibitors and might be of great significance for cancer targeted therapy.

Brugia malayi, a causative agent of lymphatic filariasis, relies on its endosymbiont Wolbachia for survival. MurE ligase, a key enzyme in Wolbachia peptidoglycan biosynthesis, serves as a promising drug target for anti-filarial therapy. In this study, we employed a hierarchical virtual screening pipeline to identify phytochemical inhibitors targeting the MurE enzyme of the Wolbachia endosymbiont of B. malayi (wBmMurE). A validated high-quality model of wBmMurE was used to screen 17,967 phytochemicals, and the identified hits were subjected to toxicity profiling, and ADME filters to select potent drug-like candidates. Five phytochemicals such as biotin, quisqualic acid, succinic acid, 9,14-dihydroxyoctadecanoic acid, and N-isovaleroylglycine with permissible ADME profiles showed favourable binding affinities (GlideScore range: -12.86 to -10.57 kcal/mol), and stable interactions with catalytically important residues were selected from screened hits. Comparative analysis with reported MurE inhibitors validated the superior affinity and drug-like behaviour of our identified leads. Molecular dynamics simulations of 300 ns confirmed the conformational stability of ligand-bound complexes, while MM-GBSA analysis supported their favourable binding free energies. The results revealed that the identified compounds have the tendency of binding within substrate binding cavity of wBmMurE. These findings suggest that selected phytochemicals could serve as starting points for the development of novel anti-filarial agents.