SAR and QSAR in Environmental Research最新文献

Novel antimycobacterial compounds are needed to expand the existing toolbox of therapeutic agents, which sometimes fail to be effective. In our study we extracted, filtered, and aggregated the diverse data on antimycobacterial activity of chemical compounds from the ChEMBL database version 24.1. These training sets were used to create the classification and regression models with PASS and GUSAR software. The IOC chemical library consisting of approximately 200,000 chemical compounds was screened using these (Q)SAR models to select novel compounds potentially having antimycobacterial activity. The QikProp tool (Schrödinger) was used to predict ADME properties and find compounds with acceptable ADME profiles. As a result, 20 chemical compounds were selected for further biological evaluation, of which 13 were the Schiff bases of isoniazid. To diversify the set of selected compounds we applied substructure filtering and selected an additional 10 compounds, none of which were Schiff bases of isoniazid. Thirty compounds selected using virtual screening were biologically evaluated in a REMA assay against the M. tuberculosis strain H37Rv. Twelve compounds demonstrated MIC below 20 µM (ranging from 2.17 to 16.67 µM) and 18 compounds demonstrated substantially higher MIC values. The discovered antimycobacterial agents represent different chemical classes.

A novel method is introduced for estimating the degree of interactions occurring between two different compounds in a binary mixture resulting in deviations from ideality as predicted by Raoult's law. Metrics of chemical similarity between binary mixture components were used as descriptors and correlated with the Root-Mean Square Error (RMSE) associated with Raoult's law calculations of total vapour pressure prediction, including Abraham descriptors, sigma moments, and several chemical properties. The best correlation was for a quantitative structure-activity relationship (QSAR) equation using differences in Abraham parameters as descriptors (r² = 0.7585), followed by a QSAR using differences in COSMO-RS sigma moment descriptors (r² = 0.7461), and third by a QSAR using differences in the chemical properties of log K_AW, melting point, and molecular weight as descriptors (r² = 0.6878). Of these chemical properties, Δlog K_AW had the strongest correlation with deviation from Raoult's law (RMSE) and this property alone resulted in an r² of 0.6630. These correlations are useful for assessing the expected deviation in Raoult's law estimations of vapour pressures, a key property for estimating inhalation exposure.

Meprins, zinc-dependent metalloproteinases belonging to the metzincin family, have been associated with various inflammatory diseases due to their abnormal expression and activity. In this study, we utilized pharmacophore modelling to identify crucial features for discovering potential dual inhibitors targeting meprins α and β. We screened four pharmacophoric features against a library of 270,540 natural compounds from the Zinc database, resulting in 84,092 matching compounds. Molecular docking was then performed on these compounds, targeting the active sites of meprins α and β. Docking results revealed six compounds capable of interacting with both isoforms, with binding affinities ranging from -10.0 to -10.5 kcal/mol and -6.9 to -9.9 kcal/mol for meprin α and β, respectively. Among these compounds, ZINC000008790788 and ZINC000095099469 displayed superior docking scores and MM-GBSA binding free energy compared to reference ligands. Furthermore, these two compounds exhibited acceptable predicted pharmacokinetic properties and stable interactions with meprins α and β during molecular dynamics simulations. This study presents a comprehensive approach for identifying potential dual inhibitors of meprin α and β, offering insights into the development of therapeutic interventions for inflammatory diseases associated with meprin dysregulation.

The fast-increasing number of commercially produced chemicals challenges the experimental ecotoxicity assessment methods, which are costly, time-consuming, and dependent on the sacrifice of animals. In this regard, Quantitative Structure-Property/Activity Relationships (QSPR/QSAR) have led the way in developing ecotoxicity assessment models. In this study, QSAR models have been developed using the pEC₅₀ values of 82 diverse agrochemicals or agro-molecules against a planktonic crustacean Daphnia magna with easily interpretable 2D descriptors. Moreover, a link among octanol-water partition coefficient (K_OW), bio-concentration factor (BCF), and critical body residue (CBR) has been addressed, and their imputation for the prediction of the toxicity endpoint (EC₅₀) has been done with an objective of the advanced exploration of several ecotoxicological parameters for toxic chemicals. The developed partial least squares (PLS) models were validated rigorously and proved to be robust, sound, and immensely well-predictive. The final Daphnia toxicity model derived from experimental derived properties along with computed descriptors emerged better in statistical quality and predictivity than those obtained solely from computed descriptors. Additionally, the pEC₅₀ and other important properties (log K_OW, log BCF, and log CBR) for a set of external agro-molecules, not employed in model development, were predicted to show the predictive ability of the models.

The spleen tyrosine kinase (Syk) plays a pivotal role in immune cells' signal transduction mechanism. While fostamatinib, an FDA-approved Syk inhibitor, is currently used to treat immune thrombocytopenia, the search for improved Syk-targeted medications to treat autoimmune diseases is still underway. Herein, we screened 38,493 compounds against Syk and selected eight leads based on the docking score and ADMET properties, and performed 3$\times$200 ns long molecular dynamics simulations of the apo and Syk-ligand complexes. We considered R406, the active component of fostamatinib, as a control. The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations demonstrated the lead1 ($\Delta G_{bind}$ = -30.35 kcal/mol) exhibited a similar binding free energy as the control ($\Delta G_{bind}$= -29.82 kcal/mol). The Syk stabilizing effect of lead1 was also indicated in its network features, sampling space, and residual correlation motion analysis. We further generated 100 structural analogues of lead1 using deep learning, and one of the analogues displayed a better binding free energy ($\Delta G_{bind}$= -47.58 kcal/mol) compared to the control or lead1, facilitated by more favourable van der Waals interactions and lesser binding-opposing net polar forces. This analogue may be further exploited to develop effective therapeutics against Syk-associated diseases after validation in vitro and in vivo.

MMP-2 is potentially contributing to several cancer progressions including leukaemias. Therefore, considering MMP-2 as a promising target, novel anticancer compounds may be designed. Here, 32 in-house arylsulfonyl L-(+) glutamines were subjected to various structure-based computational modelling approaches to recognize crucial structural attributes along with the spatial orientation for higher MMP-2 inhibition. Again, the docking-based 2D-QSAR study revealed that the Coulomb energy conferred by Tyr142 and total interaction energy conferred by Ala84 was crucial for MMP-2 inhibition. Importantly, the docking-dependent CoMFA and CoMSIA study revealed the importance of favourable steric, electrostatic, and hydrophobic substituents at the terminal phenyl ring. The MD simulation study revealed a lower fluctuation in the RMSD, RMSF, and Rg values indicating stable binding interactions of MMP-2 and these molecules. Moreover, the residual hydrogen bond and their interaction analysis disclosed crucial amino acid residues responsible for forming potential hydrogen bonding for higher MMP-2 inhibition. The results can effectively aid in the design and discovery of promising small-molecule drug-like MMP-2 inhibitors with greater anticancer potential in the future.

Deep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results through feature engineering (FE), the model regression results based on the CNN model for feature extraction have been substantially improved. The results showed that all six models (FE-SVM, FE-RF, FE-MLP, CNN-SVM, CNN-RF, and CNN-MLP) had good prediction accuracy, but the results based on the CNN model were better. The hyperparameters of six models were optimized by grid search and the 10-fold cross validation. Compared with the existing models in the literature, the model performance has been further improved. The model could be used as an intelligent tool to guide the design or screening of low-toxicity ILs.

Bisphenol A (BPA), as an environmental endocrine disruptor can cause damage to the reproductive, nervous and immune systems. Laccase can be used to degrade BPA. However, laccase is easily deactivated, especially in organic solvents, but the specific details are not clear. Molecular dynamics simulations were used to investigate the reasons for changes in laccase activity in acetonitrile (ACN) and dimethyl formamide (DMF) solutions. In addition, the effects of ACN and DMF on the activity of laccase and surfactant rhamnolipid (RL) on the degradation of BPA by laccase were investigated. Results showed that addition of ACN changed the structure of the laccase, not only decreasing the van der Waals interaction that promoted the binding of laccase with BPA, but also increasing the polar solvation free energy that hindered the binding of laccase with BPA, so it weakened the laccase activity. DMF greatly enhanced the van der Waals interaction between laccase and BPA, and played a positive role in their binding. The addition of surfactant RL alleviated the effect of organic solvent on the activity of laccase by changing the polar solvation energy. The mechanism of surfactant RL affecting laccase activity in ACN and DMF is described, providing support for understanding the effect of organic solvents on laccase.

The MET signalling pathway regulates fundamental cellular processes such as growth, division, and survival. While essential for normal cell function, dysregulation of this pathway can contribute to cancer by triggering uncontrolled proliferation and metastasis. Targeting MET activity holds promise as an effective strategy for cancer therapy. Among potential sources of anti-cancer agents, marine organisms have gained attention. In this study, we screened 47,450 natural compounds derived from marine sources within the CMNPD database against the Met crystal structure. By employing HTVS, SP, and XP docking modes, we identified three compounds (CMNPD17595, CMNPD14026, and CMNPD19696) that outperformed a reference molecule in binding affinity to the Met structure. These compounds demonstrated desirable ADME properties. Molecular Dynamics (MD) simulations for 200 ns confirmed the stability of their interactions with Met. Our findings highlight CMNPD17595, CMNPD14026, and CMNPD19696 as potential inhibitors against Met-dependent cancers. Additionally, these compounds offer new avenues for drug development, leveraging their inhibitory effects on Met to combat carcinogenesis.

Diet is an important exposure route of endocrine-disrupting chemicals (EDCs), but many unfiltered potential EDCs remain in food. The in silico prediction of EDCs is a popular method for preliminary screening. Potential EDCs in food were screened using Endocrine Disruptome, an open-source platform for inverse docking, to predict the binding probabilities of 587 food chemical contaminants with 18 human nuclear hormone receptor (NHR) conformations. In total, 25 contaminants were bound to multiple NHRs such as oestrogen receptor α/β and androgen receptor. These 25 compounds mainly include pesticides and per- and polyfluoroalkyl substances (PFASs). The prediction results were validated with the in vitro data. The structural features and the crucial amino acid residues of the four NHRs were also validated based on previous literature. The findings indicate that the screening has good prediction efficiency. In addition, the epidemic evidence about endocrine interference of PFASs in food on children was further validated through this screening. This study provides preliminary screening results for EDCs in food and a priority list for in vitro and in vivo research.