SAR and QSAR in Environmental Research最新文献

P38α has been identified as a key target for drug design to treat a wide range of diseases. In this study, multiple independent Gaussian accelerated molecular dynamics (GaMD) simulations, deep learning (DL), and the molecular mechanics generalized Born surface area (MM-GBSA) method were used to investigate the binding mechanism of inhibitors (SB2, SK8, and BMU) to DFG-in and DFG-out P38α and clarify the effect of conformational differences in P38α on inhibitor binding. GaMD trajectory-based DL effectively identified important functional domains, such as the A-loop and N-sheet. Post-processing analysis on GaMD trajectories showed that binding of the three inhibitors profoundly affected the structural flexibility and dynamical behaviour of P38α situated at the DFG-in and DFG-out states. The MM-GBSA calculations not only revealed that differences in the binding ability of inhibitors are affected by DFG-in and DFG-out conformations of P38α, but also confirmed that van der Waals interactions are the primary force driving inhibitor-P38α binding. Residue-based free energy estimation identifies hot spots of inhibitor-P38α binding across DFG-in and DFG-out conformations, providing potential target sites for drug design towards P38α. This work is expected to offer valuable theoretical support for the development of selective inhibitors of P38α family members.

Peroxisome proliferator-activated receptor gamma (PPARγ), a critical nuclear receptor, plays a pivotal role in regulating metabolic and inflammatory processes. However, various environmental contaminants can disrupt PPARγ function, leading to adverse health effects. This study introduces a novel approach to predict the inhibitory activity (IC50 values) of 140 chemical compounds across 13 categories, including pesticides, organochlorines, dioxins, detergents, flame retardants, and preservatives, on PPARγ. The predictive model, based on the light-gradient boosting machine (LightGBM) algorithm, was trained on a dataset of 1804 molecules showed r² values of 0.82 and 0.59, Mean Absolute Error (MAE) of 0.38 and 0.58, and Root Mean Square Error (RMSE) of 0.54 and 0.76 for the training and test sets, respectively. This study provides novel insights into the interactions between emerging contaminants and PPARγ, highlighting the potential hazards and risks these chemicals may pose to public health and the environment. The ability to predict PPARγ inhibition by these hazardous contaminants demonstrates the value of this approach in guiding enhanced environmental toxicology research and risk assessment.

Drug discovery's success lies in potent inhibition against a target and optimum pharmacokinetic and toxicokinetic properties of drug molecules. Membrane permeability is a crucial factor in determining the absorption, distribution, metabolism, and excretion of drug molecules, thereby determining the pharmacokinetic and toxicokinetic properties important for drug development. Intrinsic permeability (P₀) is more crucial than apparent permeability (Papp) in assessing the transport of drug molecules across a membrane. It gives more consistent results due to its non-dependency on external/site-specific factors. In the present work, our focus is on the construction of a machine learning (ML)-based quantitative read-across structure-property relationship (q-RASPR) model of intrinsic permeability of drug molecules by utilizing both linear and non-linear algorithms. The Support Vector Regression (SVR) q-RASPR model was found to be the best model having superior predictive ability (Q²_F1 = 0.788, Q²_F2 = 0.785, MAE_test = 0.637). The contribution of important descriptors in the final model is explained to get a mechanistic interpretation of intrinsic permeability. Overall, the present study unveils the application of the q-RASPR framework for significant improvement of the external predictivity of the traditional QSPR model in the case of intrinsic permeability to get a better assessment of the total permeability of drug molecules.

Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic neuronal loss in Alzheimer's disease. We developed a QSAR model to facilitate the identification of effective butyrylcholinesterase inhibitors. The model employs multi-feature selection and feature learning, improving the in silico screening efficiency and accelerating drug discovery efforts. This study aims to integrate Human Intestinal Absorption (HIA) values of butyrylcholinesterase (BChE) target inhibitors and their 50% inhibitory concentration (IC₅₀) with machine learning tools. The model was developed using chemical descriptors in combination with supervised machine learning classification algorithms. Random Forest Classifier algorithm proved to be the ultimate best fit for classification model metrics including log loss probability (0.04225), accuracy score (98.88%) and Matthew's correlation coefficient (0.98). Furthermore, a subset of the active dataset was used to study the regression based on HIA values using multi-feature selection and feature learning. The models were validated using precision, recall and F1 score for regression modelling. After integrating HIA data with existing machine learning algorithms, we observed a significant reduction of 89.63% in the number of inhibitors. The findings provide valuable pharmacological insights that can help in future design of drug development schemes different from conventional methods.

Methyllysine reading protein Spindlin 1 (SPIN1) plays a crucial role in histone post-translational modifications and serves as an effective target for the treatment of various malignant tumours. Although several inhibitors targeting SPIN1 expression have been identified, the atomic-level interactions between SPIN1 and inhibitors remain unclear. In this study, six potential SPIN1 inhibitors A366, EML631, MS31, MS8535, vinspinln, and XY49-92B were selected for molecular docking with SPIN1. Conformational changes in SPIN1 induced by these inhibitors, as well as their interactions, were investigated using molecular dynamics simulation (MD) and energy prediction methods including molecular mechanics generalized Born surface area (MM-GBSA) and solvation interaction energy (SIE). The findings indicate that the binding pockets within domain II, specifically Phe141, Trp151, Tyr170, and Tyr177, engage in cation-π interactions with these inhibitors, while also contributing to van der Waals hydrophobic interactions of varying strengths. These van der Waals hydrophobic interactions are critical for their binding affinity, while electrostatic interactions are significantly counterbalanced by polar solvation effects. In addition, through virtual screening and molecular docking, a new lead compound CXY49 was found presenting an effective binding to SPIN1. The structural and energetic changes identified in this study provide valuable insights for the development of new SPIN1 inhibitors.

A scheme for constructing models of the 'structure-glass transition temperature of a polymer' is proposed. It involves the representation of the molecular structure of a polymer through the architecture of monomer units represented through a simplified molecular input-line entry system (SMILES) and the fragments of local symmetry (FLS). The statistical quality of such models is quite good: the determination coefficient values for active training set, passive training set, calibration set, and validation set are 0.711, 0.715, 0.859, and 0.884, respectively. The reliability of the approach was assessed for three random distributions of experimental data in the training and validation sets. Machine learning technique was used for a structured training sample distributed in so-called active and passive learning, combined with a calibration set. The optimal descriptors for developed the models were calculated by the Monte Carlo technique.

Pesticides are crucial in modern agriculture, significantly enhancing crop productivity by managing pests. It is important to evaluate their toxicity to minimize health risks to bird species and preserve ecosystem balance. Traditional parameters including lethal concentration (LC₅₀) or median lethal dose (LD₅₀) often underestimate hazards due to limited data and uncertainty about the most sensitive species tested. This limitation can be addressed using extrapolation factors like HD₅ accounting for 50% mortality of the most sensitive 5% of bird species. In this research, a QSTR model was developed utilizing a diverse set of 480 pesticides using partial least squares (PLS) regression with 2D descriptors. Additionally, a PLS-based quantitative read-across structure-toxicity relationship (q-RASTR) and classification based models were constructed. The q-RASTR model outperformed traditional QSTR approaches, achieving robust statistical performance with internal validation metrics r² = 0.623, Q² = 0.569 and external validation metrics Q²_F1 = 0.541, Q²_F2 = 0.540. Key factors influencing avian toxicity were identified. The q-RASTR model was used to screen the Pesticide Properties Database (PPDB) to recognize the most and least toxic pesticides for avian species, aligning well with real-world data. This work provides a more economical and ethical alternative to conventional in vivo testing methods, aiding regulatory bodies and industries in developing safer, environmentally friendly pesticides.

Protein arginylation mediated by arginyltransferase 1 is a crucial regulator of cellular processes in eukaryotes by affecting protein stability, function, and interaction with other macromolecules. This enzyme and its targets are of immense interest for modulating cellular processes in diseased states like obesity and cancer. Despite being an important target molecule, no highly potent drug against this enzyme exists. Therefore, this study focuses on discovering potential inhibitors of human arginyltransferase 1 by computational approaches where screening of over 300,000 compounds from natural and synthetic databases was done using a pharmacophore model based on common features among known inhibitors. The drug-like properties and potential toxicity of the compounds were also assessed in the study to ensure safety and effectiveness. Advanced methods, including molecular simulations and binding free energy calculations, were performed to evaluate the stability and binding efficacy of the most promising candidates. Ultimately, three compounds were identified as potent inhibitors, offering new avenues for developing therapies targeting arginyltransferase 1.

Diabetes mellitus (DM) affects over 77 million adults in India, with cases expected to reach 134 million by 2045. Current treatments, including sulfonylureas and thiazolidinediones, are inadequate, underscoring the need for novel therapeutic strategies. This study investigates marine natural products (MNPs) as alternative therapeutic agents targeting SIK2, a key enzyme involved in DM. The structural stability of the predicted SIK2 model was validated using computational methods and subsequently employed for structure-based virtual screening (SBVS) of over 38,000 MNPs. This approach identified five promising candidates: CMNPD21753 and CMNPD13370 from the Comprehensive Marine Natural Product Database, MNPD10685 from the Marine Natural Products Database, and SWMDRR053 and SWMDRR052 from the Seaweed Metabolite Database. The identified compounds demonstrated docking scores ranging from -7.64 to -11.95 kcal/mol and MMGBSA binding scores between -33.29 and -68.29 kcal/mol, with favourable predicted pharmacokinetic and toxicity profiles. Molecular dynamics simulations (MDS) revealed stronger predicted binding affinity for these compounds compared to ARN-3236, a known SIK2 inhibitor. Principal component (PC)-based free energy landscape (FEL) analysis further supported the stable binding of these compounds to SIK2. These computational findings highlight the potential of these leads as novel SIK2 inhibitors, warranting future in vitro and in vivo validation.

Ras is identified as a human oncogene which is frequently mutated in human cancers. Among its three isoforms (K, N, and H), KRas is the most frequently mutated. Mutant Ras exhibits reduced GTPase activity, leading to the prolonged activation of its conformation. This extended activation promotes Ras-dependent signalling, contributing to cancer cell survival and growth. In this study, we conducted structure-based virtual screening of 11698 phytochemicals in the IMPPAT 2.0 database to identify inhibitors of KRas. We identified two phytochemicals with fair binding affinity, and their binding patterns with KRas were analysed in detail. Additionally, we performed 200 ns molecular dynamics (MD) simulations of each complex to understand the interaction mechanism of KRas with the newly identified compounds, such as Samaderine E and Bismurrayaquinone A. These phytochemicals bind to the binding site residues ARG41 and ASP54, causing conformational changes in KRas. The RMSD, RMSF, Rg, SASA, hydrogen bond, and secondary structure analysis studies suggested the potential of the selected phytochemicals. The identification of Samaderine E and Bismurrayaquinone A as phytochemicals binding to a functional pocket on KRas, supported by PCA and FEL analysis, highlights their potential as effective therapeutic inhibitors of the KRas oncoprotein.