SAR and QSAR in Environmental Research最新文献

Human epidermal growth factor receptor (EGFR) has been approved as a well-established druggable target of lung carcinoma. The binding peptide segments of both substrate and inhibitory proteins contain a phosphorylatable tandem YY motif but interact with EGFR kinase domain in different manners. Here, the two tandem substrate Y₀Y₊₁ and inhibitor Y₊₁Y₊₂ motifs were aligned to define a new pseudo triad tyrosine Y₀Y₊₁Y₊₂ (PtriY) motif, in which the Y₀, Y₊₁ and Y₊₂ residues bind to catalytic, priming and priming pockets on EGFR kinase domain surface, respectively. Here, we examined the effects of different PtriY phosphorylation codes on EGFR binding and created a systematic single-point substitution-binding energy change profile of its N- and C-terminal extensions, which was then used to develop and validate quantitative structure-activity relationship (QSAR) models. The best model was utilized to guide rational peptidic inhibitor design, from which more than 40 promising hits were selected to perform affinity and/or kinase assays. The QSAR-designed PH2[Y₀pY₊₁pY₊₂] peptide (ENGHY₀pY₊₁pY₊₂AL) was identified to have the strongest binding potency (K_d = 0.26 ± 0.07 μM) and the highest inhibitory activity (IC₅₀ = 5.8 ± 0.9 nM), which consists of an amphiphilic N-terminal extension, a double-phosphorylated PtriYmotif and hydrophobic C-terminal extension.

This article presents the results of computational screening of approved drug compounds to find new inhibitors of acetylcholinesterase (AChE), an enzyme that plays a key role in the regulation of neurotransmission and cognitive functions. Using molecular docking and quantum chemical postprocessing methods, the authors conducted a virtual screening of a library of 2909 drug compounds approved for clinical use from two ZINC database libraries. The screening process employed the SOL docking program with MMFF94 force field and genetic algorithms for global optimization, targeting the human AChE structure (PDB ID: 6O4W). As a result of the docking, 211 of the most promising ligands were selected for calculating their enthalpy of binding to AChE using quantum chemical calculations. Based on the analysis of the free energy of binding estimated by docking score and the enthalpy of binding calculated using the quantum-chemical PM7 method with the COSMO solvent model, 16 of the most promising candidates for the role of AChE inhibitors were identified. Notable candidates include Pixantrone, Guanfacine and Hydroxystilbamidine. These compounds, although not previously known as AChE inhibitors, represent diverse chemical classes including substituted thiophenes, pyridines, and fused nitrogen-containing heterocycles, showing high potential for treating neurodegenerative diseases such as Alzheimer's disease.

Antioxidant agents that efficiently scavenge reactive oxygen species (ROS) are of great interest in medicinal chemistry for their potential to mitigate oxidative stress-related pathologies. In this work, we developed an interpretable Multiple Linear Regression (MLR) QSAR model using seven molecular descriptors (D/Dr05, MATS2v, MATS8p, Mor24m, L2s, HATS3u, H8m) to predict the free radical scavenging activity of coumarin-based compounds as measured by the IC₅₀ in the DPPH assay. The MLR-QSAR model showed strong goodness-of-fit and robust internal and external validation parameters (r² = 81.04, Q²_LOO = 77.93, Q²_boot = 76.78, r²_ext = 75.38, y_scrambler² = 0.25), supporting its predictive reliability. We applied the model to predict the antiradical potential of a novel set of Warfarin derivatives, a class of molecules historically known for anticoagulant properties but with unexplored antioxidant potential. Experimental in vitro DPPH assays on the seven Warfarin derivatives (WD) revealed a positive correlation (r = 0.63) with the predictions, validating the MLR-QSAR as a screening tool. Furthermore, all WD exhibited significant DPPH radical scavenging activity, demonstrating the chemical antioxidant potential of an anticoagulant-derived scaffold. This dual in silico-in vitro strategy highlights the value of interpretable QSAR models for guiding compound prioritization and structural optimization towards new coumarin-based antioxidants.

Pesticides are widely used in agriculture to enhance crop yield and protect against pests. However, their persistence in soil can lead to long-term environmental contamination and pose health risks to humans and other organisms through indirect exposure via the food chain. In this study, we used in silico approaches like Quantitative Structure-Activity Relationship (QSAR) modelling, Intelligent Consensus Prediction (ICP), and chemical read-across to predict the soil degradation half-lives of various pesticides. Models were established using 2D molecular descriptors, thoroughly validated with the help of training and test sets validation parameters, and conformed to OECD guidelines. The predictive models were applied to the Pesticide Properties Database (PPDB) to demonstrate their utility in screening untested and/or newly synthesized pesticides, considering the domain of applicability. Key structural features associated with degradation were identified, providing valuable insights for the design of biodegradable and environmentally safer pesticides. This work contributes to data gap-filling, regulatory risk assessment, and the prioritization of new or untested pesticides for environmental evaluation.

Cancer remains a major global health challenge, with approximately 18 million new cases reported annually. Existing evidence highlights the PAK1 protein as a critical regulator of cancer progression, making it a promising therapeutic target. The PAK1 protein complexed with dibenzodiazepine was fetched from the PDB with the identifier 4ZLO. The structure was preprocessed through preparation and exposed to pharmacophore hypotheses on the Schrödinger suite programme, indicating key features of RRH. A multi-tiered docking-based screening workflow from the libraries of ZINC and Enamine databases identified five potential bioactive compounds: ZINC952869440, ZINC952869442, ENAMINE558, ENAMINE6304, and ENAMINE8429. The docking and MM/GBSA scores ranked from -5.02 to -8.34 kcal/mol and -46.10 to -50.41 kcal/mol. Remarkably, none of these candidates violated the rules of five, and the Qikprop parameters complied with pharmacokinetic suitability. The DFT analysis revealed energy gap scores ranged from -0.182 to -0.225 eV, indicating favourable electronic properties and stability of the ligands. Furthermore, molecular dynamics (MD) and essential dynamics (ED) studies validated the structural stability of the complexes. The secondary structure analysis indicated stable retention of α-helices and β-strands throughout the simulation. Moreover, the computational investigation identified potential PAK1 inhibitors that warrant further experimental testing and therapeutic development.

The structure of 4-Amino-N-methylphthalimide (4AMP) was investigated through spectroscopic techniques and quantum chemical calculations. Structural parameters were optimized using the DFT-B3LYP/6-311++G(d,p) method in both gas and DMSO phases. Experimental results from powder XRD and reported single crystal XRD showed excellent agreement. The experimental FT-IR and FT-Raman spectra correlated well with theoretical vibrational frequencies, and UV-Vis spectra comparisons further validated the computational findings. Molecular electrostatic potential (MEP), Mulliken and natural charges, and Fukui function analyses highlighted the reactive regions of 4AMP. Natural bond orbital (NBO) analysis revealed stabilization energies of bonding and antibonding orbitals. Hirshfeld surface and fingerprint analyses provided insights into intra and intermolecular interactions. Biological studies indicated that 4AMP exhibited the strongest binding affinity towards the PI3Kα (PIK3CA catalytic subunit) at -6.9 kcal/mol, suggesting significant therapeutic potential. Molecular dynamics simulations over 100 ns have been performed to assess the stability and dynamic behaviour of 4AMP. Cytotoxicity assays demonstrated potent activity against breast cancer cell lines, with IC₅₀ values of 16.89 μg/mL (MCF-7) and 19.53 μg/mL (MDA-MB-231). These findings suggest that 4AMP possesses promising anticancer activity, combining favourable structural, spectroscopic, and biological characteristics, making it a potential candidate for targeted breast cancer therapy.

Amyloid-like proteins (ALPs) are key components of bacterial biofilms, reinforcing the extracellular matrix and supporting cell adhesion. Biofilms contribute significantly to chronic infections, antimicrobial resistance (AMR), and persistent diseases, posing major clinical and environmental challenges. TasA, a primary amyloid protein in biofilms, is well-studied in Bacillus subtilis, but its role in Bacillus cereus remains poorly understood. This study aimed to explore TasA as a target for anti-biofilm intervention. Due to the lack of an experimentally resolved structure, the 3D conformation of B. cereus TasA was predicted using computational modelling and validated through biophysical analyses. A structure-based virtual screening of FDA-approved compounds was conducted to identify potential inhibitors of TasA. Top candidates were further evaluated through molecular docking and triplicate molecular dynamics simulations to assess binding interactions and structural effects. Deferoxamine mesylate emerged as a strong inhibitor, demonstrating high binding affinity and destabilizing the amyloid fibril structure of TasA. This ligand-induced disruption may impair biofilm integrity and reduce AMR. Overall, the study presents TasA as a novel target for biofilm disruption in B. cereus and supports drug repurposing strategies for managing biofilm-associated infections.

Neurodegenerative disorders are considered as one of the leading causes of disability and mortality. Monoamine oxidases play a crucial role in their management. Despite many monoamine oxidase inhibitors in the market problems like hepatotoxicity, drug interactions, irreversibility and selectivity etc. present lacunas in the current therapy. Therefore, development of more effective monoamine oxidase inhibitors is required. In the present research, Monte Carlo method-based and OECD principles-guided models on 192 monoamine oxidase-A (MAO-A) inhibitors are reported using index of ideality of correlation (IIC) and correlation intensity index (CII). These two criteria are responsible for enhanced statistical characteristics values. The predictive potential of the prepared random models has been estimated through the system of self-consistence which resulted in similar statistical predictive quality of the created models using different splits of the available data. Five new molecules (M1-M5) were designed by incorporation of extracted structural attributes. The designed molecules were analysed via molecular docking study, and compound M1 showed maximum endpoint value (pIC₅₀ = 5.5) and binding affinity (-9.5 kcal mol^-1). The entire designed molecule exhibited better binding affinity and MAO-A inhibitory activity than the parent molecule. The calculated pIC₅₀ is highly correlated with binding affinity showing correlation value of 0.9540.

Research on chemical adsorption onto microplastics is increasingly important in environmental studies. However, many existing models rely on basic structural properties, LSER descriptors, or 2D descriptor pools, often lacking robust external validation and applicability domain (AD) assessments. We revisited two literature studies to recompile a larger adsorption coefficient dataset (log K_d) of diverse organic chemicals onto polyethylene microplastics in pure water, applying rigorous data screening to ensure a higher-quality dataset and remove inconsistencies. log K_d values of 61 external chemicals were predicted from a dataset of 47 chemicals, including persistent, mobile and toxic (PMT) chemicals in both sets thereby extending predictions to PMT/vPvM compounds with limited experimental data. An extensive 3D descriptor set provided deeper mechanistic insights than 2D or physicochemical descriptors. Inclusion of 3D descriptors from dual-phase (gas and aqueous) geometry optimizations also improved mechanistic interpretation compared to gas-phase optimizations in literature. Our findings highlight the importance of strict external validation and a well-defined AD, using diverse validation metrics that improve upon the revisited studies. Adsorption correlated positively with lipophilicity and 3D-MoRSE descriptors, and negatively with -OH groups, ionization potential, and polarity. Model predictions aligned well with literature-reported log K_d values.

Neurodegenerative disorders, including Alzheimer's disease (AD) and Parkinson's disease (PD), represent major global health concerns due to multifactorial pathology and lack of effective therapeutics. A comprehensive multi-target (MT) computational strategy was applied to identify natural product-derived therapeutics against four critical targets of AD and PD: acetylcholinesterase (AChE), dopamine receptor D2, monoamine oxidase B (MAO-B) and cyclooxygenase-2 (COX-2). Structure-based pharmacophore models were constructed for each target, validated and employed to virtually screen 2,54,850 compounds from the COCONUT database, following rigorous ADME-based filtering. Compounds with an average pharmacophore fit score ≥0.6 were shortlisted, yielding 55 promising candidates. These were examined through molecular dynamics (MD) docking using the CDOCKER algorithm. Four hits (N1-N4) displayed superior binding affinities relative to reference drugs donepezil (DNP) and safinamide (SAF). Binding free energy calculations further substantiated their interaction stability. ADMET analysis predicted favourable pharmacokinetic properties, efficient blood-brain barrier penetration and non-toxic profiles, with compound N1 (CNP0388746.0) emerging as the most promising candidate. Additionally, density functional theory (DFT) studies provided insights into the electronic characteristics and reactivity of top hits. Overall, this integrated in silico approach emphasized the potential of natural scaffolds as sustainable multi-target-directed ligands (MTDLs) for AD and PD therapeutics.