SAR and QSAR in Environmental Research最新文献

MMP-2 overexpression is strongly related to several diseases including cancer. However, none of the MMP-2 inhibitors have been marketed as drug candidates due to various adverse effects. Here, a set of sulphonyl pyrrolidines was subjected to validation of molecular modelling followed by binding mode analysis to explore the crucial structural features required for the discovery of promising MMP-2 inhibitors. This study revealed the importance of hydroxamate as a potential zinc-binding group compared to the esters. Importantly, hydrophobic and sterical substituents were found favourable at the terminal aryl moiety attached to the sulphonyl group. The binding interaction study revealed that the S1' pocket of MMP-2 similar to 'a basketball passing through a hoop' allows the aryl moiety for proper fitting and interaction at the active site to execute potential MMP-2 inhibition. Again, the sulphonyl pyrrolidine moiety can be a good fragment necessary for MMP-2 inhibition. Moreover, some novel MMP-2 inhibitors were also reported. They showed the significance of the 3^rd position substitution of the pyrrolidine ring to produce interaction inside S2' pocket. The current study can assist in the design and development of potential MMP-2 inhibitors as effective drug candidates for the management of several diseases including cancers in the future.

Dengue fever, prevalent in Southeast Asian countries, currently lacks effective pharmaceutical interventions for virus replication control. This study employs a strategy that combines machine learning (ML)-based quantitative-structure-activity relationship (QSAR), molecular docking, and molecular dynamics simulations to discover potential inhibitors of the NS3 protease of the dengue virus. We used nine molecular fingerprints from PaDEL to extract features from the NS3 protease dataset of dengue virus type 2 in the ChEMBL database. Feature selection was achieved through the low variance threshold, F-Score, and recursive feature elimination (RFE) methods. Our investigation employed three ML models - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) - for classifier development. Our SVM model, combined with SVM-RFE, had the best accuracy (0.866) and ROC_AUC (0.964) in the testing set. We identified potent inhibitors on the basis of the optimal classifier probabilities and docking binding affinities. SHAP and LIME analyses highlighted the significant molecular fingerprints (e.g. ExtFP69, ExtFP362, ExtFP576) involved in NS3 protease inhibitory activity. Molecular dynamics simulations indicated that amphotericin B exhibited the highest binding energy of -212 kJ/mol and formed a hydrogen bond with the critical residue Ser196. This approach enhances NS3 protease inhibitor identification and expedites the discovery of dengue therapeutics.

In the search for natural and non-toxic products alternatives to synthetic pesticides, the fumigant and repellent activities of 35 essential oils are predicted in the human head louse (Pediculus humanus capitis) through the Quantitative Structure-Activity Relationships (QSAR) theory. The number of constituents of essential oils with weight percentage composition greater than 1% varies from 1 to 15, encompassing up to 213 structurally diverse compounds in the entire dataset. The 27,976 structural descriptors used to characterizing these complex mixtures are calculated as linear combinations of non-conformational descriptors for the components. This approach is considered simple enough to evaluate the effects that changes in the composition of each component could have on the studied bioactivities. The best linear regression models found, obtained through the Replacement Method variable subset selection method, are applied to predict 13 essential oils from a previous study with unknown property data. The results show that the simple methodology applied here could be useful for predicting properties of interest in complex mixtures such as essential oils.

Human neutrophil elastase (HNE) plays a key role in initiating inflammation in the cardiopulmonary and systemic contexts. Pathological auto-proteolysed two-chain (tc) HNE exhibits reduced binding affinity with inhibitors. Using AutoDock Vina v1.2.0, 66 flavonoid inhibitors, sivelestat and alvelestat were docked with single-chain (sc) HNE and tcHNE. Schrodinger PHASE v13.4.132 was used to generate a 3D-QSAR model. Molecular dynamics (MD) simulations were conducted with AMBER v18. The 3D-QSAR model for flavonoids with scHNE showed r² = 0.95 and q² = 0.91. High-activity compounds had hydrophobic A/A2 and C/C2 rings in the S1 subsite, with hydrogen bond donors at C5 and C7 positions of the A/A2 ring, and the C4' position of the B/B1 ring. All flavonoids except robustaflavone occupied the S1'-S2' subsites of tcHNE with decreased AutoDock binding affinities. During MD simulations, robustaflavone remained highly stable with both HNE forms. Principal Component Analysis suggested that robustaflavone binding induced structural stability in both HNE forms. Cluster analysis and free energy landscape plots showed that robustaflavone remained within the sc and tcHNE binding site throughout the 100 ns MD simulation. The robustaflavone scaffold likely inhibits both tcHNE and scHNE. It is potentially superior to sivelestat and alvelestat and can aid in developing therapeutics targeting both forms of HNE.

Essential oils (EOs) are natural products currently used to control arthropods, and their interaction with insect odorant-binding proteins (OBPs) is fundamental for the discovery of new repellents. This in silico study aimed to predict the potential of EO components to interact with odorant proteins. A total of 684 EO components from PubChem were docked against 23 odorant binding proteins from Protein Data Bank using AutoDock Vina. The ligands and proteins were optimized using Gaussian 09 and Sybyl-X 2.0, respectively. The nature of the protein-ligand interactions was characterized using LigandScout 4.0, and visualization of the binding mode in selected complexes was carried out by Pymol. Additionally, complexes with the best binding energy in molecular docking were subjected to 500 ns molecular dynamics simulations using Gromacs. The best binding affinity values were obtained for the 1DQE-ferutidine (-11 kcal/mol) and 2WCH-kaurene (-11.2 kcal/mol) complexes. Both are natural ligands that dock onto those proteins at the same binding site as DEET, a well-known insect repellent. This study identifies kaurene and ferutidine as possible candidates for natural insect repellents, offering a potential alternative to synthetic chemicals like DEET.

The 3C-like Proteinase (3CLpro) of novel coronaviruses is intricately linked to viral replication, making it a crucial target for antiviral agents. In this study, we employed two fingerprint descriptors (ECFP_4 and MACCS) to comprehensively characterize 889 compounds in our dataset. We constructed 24 classification models using machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), extreme Gradient Boosting (XGBoost), and Deep Neural Networks (DNN). Among these models, the DNN- and ECFP_4-based Model 1D_2 achieved the most promising results, with a remarkable Matthews correlation coefficient (MCC) value of 0.796 in the 5-fold cross-validation and 0.722 on the test set. The application domains of the models were analysed using d_STD-PRO calculations. The collected 889 compounds were clustered by K-means algorithm, and the relationships between structural fragments and inhibitory activities of the highly active compounds were analysed for the 10 obtained subsets. In addition, based on 464 3CLpro inhibitors, 27 QSAR models were constructed using three machine learning algorithms with a minimum root mean square error (RMSE) of 0.509 on the test set. The applicability domains of the models and the structure-activity relationships responded from the descriptors were also analysed.

Resveratrol is converted to various metabolites by gut microbiota. Human and rat liver 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) are critical for glucocorticoid activation, while 11β-HSD2 in the kidney does the opposite reaction. It is still uncertain whether resveratrol and its analogues selectively inhibit 11β-HSD1. In this study, the inhibitory strength, mode of action, structure-activity relationship (SAR), and docking analysis of resveratrol analogues on human, rat, and mouse 11β-HSD1 and 11β-HSD2 were performed. The inhibitory strength of these chemicals on human 11β-HSD1 was dihydropinosylvin (6.91 μM) > lunularin (45.44 μM) > pinostilbene (46.82 μM) > resveratrol (171.1 μM) > pinosylvin (193.8 μM) > others. The inhibitory strength of inhibiting rat 11β-HSD1 was pinostilbene (9.67 μM) > lunularin (17.39 μM) > dihydropinosylvin (19.83 μM) > dihydroresveratrol (23.07 μM) > dihydroxystilbene (27.84 μM) > others and dihydropinosylvin (85.09 μM) and pinostilbene (>100 μM) inhibited mouse 11β-HSD1. All chemicals did not inhibit human, rat, and mouse 11β-HSD2. It was found that dihydropinosylvin, lunularin, and pinostilbene were competitive inhibitors of human 11β-HSD1 and that pinostilbene, lunularin, dihydropinosylvin, dihydropinosylvin and dihydroxystilbene were mixed inhibitors of rat 11β-HSD1. Docking analysis showed that they bind to the steroid-binding site of human and rat 11β-HSD1. In conclusion, resveratrol and its analogues can selectively inhibit human and rat 11β-HSD1, and mouse 11β-HSD1 is insensitive to the inhibition of resveratrol analogues.

The widespread use of pyrethroid and organophosphate pesticides necessitates accurate toxicity predictions for regulatory compliance. In this study QSAR and SSD models for six pyrethroid and four organophosphate compounds using QSAR Toolbox and SSD Toolbox have been developed. The QSAR models, described by the formula 48 h-EC50 or 96 h-LC50 = x + y * log Kow, were validated for predicting 48 h-EC50 values for acute Daphnia toxicity and 96 h-LC50 values for acute fish toxicity, meeting criteria of n ≥10, r² ≥0.7, and Q² >0.5. Predicted 48 h-EC50 values for pyrethroids ranged from 3.95 × 10^-5 mg/L (permethrin) to 8.21 × 10^-3 mg/L (fenpropathrin), and 96 h-LC50 values from 3.89 × 10^-5 mg/L (permethrin) to 1.68 × 10^-2 mg/L (metofluthrin). For organophosphates, 48 h-EC50 values ranged from 2.00 × 10^-5 mg/L (carbophenothion) to 3.76 × 10^-2 mg/L (crufomate) and 96 h-LC50 values from 3.81 × 10^-3 mg/L (carbophenothion) to 12.3 mg/L (crufomate). These values show a good agreement with experimental data, though some, like Carbophenothion, overestimated toxicity. HC05 values, indicating hazardous concentrations for 5% of species, range from 0.029 to 0.061 µg/L for pyrethroids and 0.030 to 0.072 µg/L for organophosphates. These values aid in establishing environmental quality standards (EQS). Compared to existing EQS, HC05 values for pyrethroids were less conservative, while those for organophosphates were comparable.

Nowadays, β-lactam antibiotics are one of the most consumed OTC (over-the-counter) medicines in the world. Its frequent use against several infectious diseases leads to the development of antibiotic resistance. Another unavoidable risk factor of β-lactam antibiotics is environmental toxicity. Numerous terrestrial as well as aquatic species have suffered due to the excessive use of these pharmaceuticals. In this present study, we have performed a toxicity assessment employing a novel in silico technique like quantitative structure-toxicity relationships (QSTRs) to explore toxicity against zebrafish (Danio rerio). We have developed single as well as inter-endpoint QSTR models for the β-lactam compounds to explore important structural attributes responsible for their toxicity, employing median lethal (LC₅₀) and median teratogenic concentration (TC₅₀) as the endpoints. We have shown how an inter-endpoint model can extrapolate unavailable endpoint values with the help of other available endpoint values. To verify the models' robustness, predictivity, and goodness-of-fit, several universally popular metrics for both internal and external validation were extensively employed in model validation (single endpoint models: r² = 0.631 - 0.75, Q²_F1 = 0.607 - 0.684; inter-endpoint models: r² = 0.768 - 0.84, Q²_F1 = 0.678 - 0.76). Again, these models were engaged in the prediction of these two responses for a true external set of β-lactam molecules without response values to prove the reproducibility of these models.

Curcumin, an extensively utilized natural pigment in the food industry, has attracted considerable attention due to its potential therapeutic effects, such as anti-tumorigenic and anti-inflammatory activities. The enzyme 17β-Hydroxysteroid dehydrogenase 1 (17β-HSD1) holds a crucial position in oestradiol production and exhibits significant involvement in oestrogen-responsive breast cancers and endometriosis. This study investigated the inhibitory effects of curcuminoids, metabolites, and analogues on 17β-HSD1, a key enzyme in oestradiol synthesis. Screening 10 compounds, including demethoxycurcumin (IC₅₀, 3.97 μM) and dihydrocurcumin (IC₅₀, 5.84 μM), against human and rat 17β-HSD1 revealed varying inhibitory potencies. These compounds suppressed oestradiol secretion in human BeWo cells at ≥ 5-10 μM. 3D-Quantitative structure-activity relationship (3D-QSAR) and molecular docking analyses elucidated the interaction mechanisms. Docking studies and Gromacs simulations suggested competitive or mixed binding to the steroid or NADPH/steroid binding sites of 17β-HSD1. Predictive 3D-QSAR models highlighted the importance of hydrophobic regions and hydrogen bonding in inhibiting 17β-HSD1 activity. In conclusion, this study provides valuable insights into the inhibitory effects and mode of action of curcuminoids, metabolites, and analogues on 17β-HSD1, which may have implications in the field of hormone-related disorders.