SAR and QSAR in Environmental Research最新文献

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.

Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solving this apparent contradiction as they allow to generalize toxicity patterns across chemicals and species. However, despite the availability of large public toxicity datasets, the data is highly sparse, complicating model development. The aim of this study is to provide insights into how ML can predict toxicity using a large but sparse dataset. We developed models to predict LC50-values, based on experimental LC50-data covering 2431 organic chemicals and 1506 aquatic species from the ECOTOX-database. Several well-known ML techniques were evaluated and a new ML model was developed, inspired by recommender systems. This new model involves a simple linear model that learns low-rank interactions between species and chemicals using factorization machines. We evaluated the predictive performances of the developed models based on two validation settings: 1) predicting unseen chemical-species pairs, and 2) predicting unseen chemicals. The results of this study show that ML models can accurately predict LC50-values in both validation settings. Moreover, we show that the novel factorization machine approach can match well-tuned, complex, ML approaches.

The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q²). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.

A study of Quantitative Structure Activity Relationship (QSAR) was performed to assess the possible adverse effects of 25 pharmaceuticals commonly found in the Brazilian water compartments and to establish a ranking of environmental concern. The occurrence (O), the persistence (P), the mobility (M), and the toxicity (T) of these compounds in the Brazilian drinking water reservoirs were evaluated. Moreover, to verify the predicted OPMT dataset outcomes, a quality index (QI) was also developed and applied. The main results showed that: (i) after in silico predictions through VEGA QSAR, 19 from 25 pharmaceuticals consumed in Brazil were classified as persistent; (ii) moreover, after in silico predictions through OPERA QSAR, 15 among those 19 compounds considered persistent, were also classified as mobile or very mobile. On the other hand, the results of toxicity indicate that only 9 pharmaceuticals were classified with the highest toxicity level. Ultimately, the QI of 7 from 25 pharmaceuticals were categorized as 'optimal'; 15 pharmaceuticals were categorized as 'good'; and only 3 pharmaceuticals were categorized as 'regular'. Therefore, based on the QI criteria used, it is possible to assume that this OPMT prediction dataset had a good reliability. Efforts to reduce emissions of OPMT-pharmaceuticals in Brazilian drinking water reservoirs are encouraged.