SAR and QSAR in Environmental Research最新文献

Human aldehyde dehydrogenases (ALDHs) are a group of 19 isoforms often overexpressed in cancer stem cells (CSCs). These enzymes play critical roles in CSC protection, maintenance, cancer progression, therapeutic resistance, and poor prognosis. Thus, targeting ALDH isoforms offers potential for innovative cancer treatments. Flavonoids, known for their ability to affect multiple cancer-related pathways, have shown anticancer activity by downregulating specific ALDH isoforms. This study aimed to evaluate 830 flavonoids from the PubChem database against five ALDH isoforms (ALDH1A1, ALDH1A2, ALDH1A3, ALDH2, ALDH3A1) using computational methods to identify potent inhibitors. Extra precision (XP) Glide docking and MM-GBSA free binding energy calculations identified several flavonoids with high binding affinities. MD simulation highlighted flavonoids 1, 2, 18, 27, and 42 as potential specific inhibitors for each isoform, respectively. Flavonoid 10 showed high binding affinities for ALDH1A2, ALDH1A3, and ALDH3A1, emerging as a potential multi-ALDH inhibitor. ADMET property evaluation indicated that the promising hits have acceptable drug-like profiles, but further optimization is needed to enhance their therapeutic efficacy and reduce toxicity, making them more effective ALDH inhibitors for future cancer treatment.

BACE1 has been regarded as an essential drug design target for treating Alzheimer's disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (MM-GBSA) method are integrated to elucidate the molecular mechanism underlying the effect of D93 and D289 protonation on binding of inhibitors OV6 and 4B2 to BACE1. The GaMD trajectory-based DL successfully identifies significant function domains. Dynamic analysis shows that the protonation of D93 and D289 strongly affects the structural flexibility and dynamic behaviour of BACE1. Free energy landscapes indicate that inhibitor-bound BACE1s have more conformational states in the protonated states than the wild-type (WT) BACE1, and show more binding poses of inhibitors. Binding affinities calculated using the MM-GBSA method indicate that the protonation of D93 and D289 highly disturbs the binding ability of inhibitors to BACE1. In addition, the protonation of two residues significantly affects the hydrogen bonding interactions (HBIs) of OV6 and 4B2 with BACE1, altering their binding activity to BACE1. The binding hot spots of BACE1 recognized by residue-based free energy estimations provide rational targeting sites for drug design towards BACE1. This study is anticipated to provide theoretical aids for drug development towards treatment of AD.

Human polo-like kinase 1 (PLK1) has been recognized as an attractive therapeutic target against nasopharyngeal carcinoma (NPC). The kinase contains a conserved polo-box domain (PBD) that exhibits a wide specificity across various substrates. Previously, we explored natural amino acid preference in PLK1 PBD-binding phosphopeptides. However, limited to the short sequence only natural amino acids cannot guarantee the sufficient exploitation of chemical and structural diversity of the phosphopeptides. Here, we described a genetic optimization (GO) strategy to systematically optimize a 10⁴-sized 6-mer phosphopeptide array towards increasing affinity to PLK1 PBD domain by using 20 natural plus 34 unnatural amino acids as basic building blocks. A QSAR predictor was created to guide the GO optimization and then evaluated rigorously at molecular and cellular levels. Three unnatural phosphopeptides uPP8, uPP15 and uPP20 were designed as potent binders with K_d = 0.18, 0.42 and 0.08 μM, respectively, in which the uPP20 also possessed a good anti-tumor activity against human NPC cells when fused with cell permeation sequence. In addition, we defined a relaxed 6-mer motif for the preferential PLK1 PBD-binding phosphosites, namely [Φ/П]-3-[ζ]-2-[ζ]-1-[pT/pS]0-[Φ/П]+1-[Φ]+2, where the symbols Φ, ζ and П represent hydrophobic, polar and aromatic amino acid types, respectively.  .

Dithiocarbamate fungicides have been widely used in agricultural practices due to their effective control of fungal diseases, thereby contributing to global food security and agricultural productivity. In this study, the inhibitory potency of eight compounds on human and rat aromatase (CYP19A1) activity was evaluated. The results revealed that zineb exhibited the highest inhibitory potency on human CYP19A1 (IC₅₀, 2.79 μM). Maneb (IC₅₀, 3.09 μM), thiram (IC₅₀, 4.76 μM), and ferbam (IC₅₀, 6.04 μM) also demonstrated potent inhibition on human CYP19A1. For the rat CYP19A1, disulfiram (IC₅₀, 1.90 μM) displayed the strongest inhibition followed by maneb (2.16 μM), zineb (2.54 μM), and thiram (6.99 μM). These dithiocarbamates acted as mixed/non-competitive inhibitors of human and rat CYP19A1. Dithiothreitol (DTT), a reducing agent, partially rescued thiram-mediated inhibition when incubated at the same. Moreover, positive correlations were observed between log P, topological polar surface area, molecular weight, and heavy atoms and IC₅₀ values. 3D-QSAR analysis revealed the hydrogen bond acceptor and donor play critical roles in the binding of dithiocarbamates to human CYP19A1. In silico analysis showed that dithiocarbamates bind to the haem binding site, containing Cys437 residues. In conclusion, some dithiocarbamates potently inhibit human and rat CYP19A1 via interacting with haem-binding Cys437 residues.

Per- and polyfluoroalkylated organic compounds (PFAs) are versatile compounds extensively used in global industries. However, they are also persistent organic pollutants (POPs). This study aimed to develop new models for assessing oral and inhalation toxicity in rat and mice models. A set of 407 PFAs from the literature was divided into four groups based on the endpoints of interest. The models were constructed using only 2D structure descriptors derived from SMILES strings. The resulting models showed a strong statistical quality for all endpoints. They present an applicability domain (AD) that ensures good reliability, and provided meaningful interpretation, which are partially supported by existing literature. Consequently, these models are valuable for understanding how PFAs exert their toxic effect on mammals and for predicting the risk associated with these significant industrial chemical agents.

Fraction unbound in plasma (f_u,p) of drugs is an significant factor for drug delivery and other biological incidences related to the pharmacokinetic behaviours of drugs. Exploration of different molecular fragments for f_u,p of different small molecules/agents can facilitate in identification of suitable candidates in the preliminary stage of drug discovery. Different researchers have implemented strategies to build several prediction models for f_u,p of different drugs. However, these studies did not focus on the identification of responsible molecular fragments to determine the fraction unbound in plasma. In the current work, we tried to focus on the development of robust classification-based QSAR models and evaluated these models with multiple statistical metrics to identify essential molecular fragments/structural attributes for fractions unbound in plasma. The study unequivocally suggests various N-containing aromatic rings and aliphatic groups have positive influences and sulphur-containing thiadiazole rings have negative influences for the f_u,p values. The molecular fragments may help for the assessment of the f_u,p values of different small molecules/drugs in a speedy way in comparison to experiment-based in vivo and in vitro studies.

Ionic liquids (ILs) have attracted considerable interest due to their unique properties and prospective uses in various industries. However, their potential toxicity, particularly regarding enzyme inhibition, has become a growing concern. In this study, a QSAR model was proposed to predict the enzyme inhibition toxicity of ILs. A dataset of diverse ILs with corresponding toxicity data against three enzymes was compiled. Molecular descriptors that capture the physicochemical, structural, and topological properties of the ILs were calculated. To optimize the selection of descriptors and develop a robust QSAR model, the chaotic spotted hyena optimization algorithm, a novel nature-inspired metaheuristic, was employed. The proposed algorithm efficiently searches for an optimal subset of descriptors and model parameters, enhancing the predictive performance and interpretability of the QSAR model. The developed model exhibits excellent predictive capability, with high classification accuracy and low computation time. Sensitivity analysis and molecular interpretation of the selected descriptors provide insights into the critical structural features influencing the toxicity of ILs. This study showcases the successful application of the chaotic spotted hyena optimization algorithm in QSAR modelling and contributes to a better understanding of the toxicity mechanisms of ILs, aiding in the design of safer alternatives for industrial applications.

Cathepsin K (CatK), a lysosomal cysteine protease, contributes to skeletal abnormalities, heart diseases, lung inflammation, and central nervous system and immune disorders. Currently, CatK inhibitors are associated with severe adverse effects, therefore limiting their clinical utility. This study focuses on exploring quantitative structure-activity relationships (QSAR) on a dataset of CatK inhibitors (1804) compiled from the ChEMBL database to predict the inhibitory activities. After data cleaning and pre-processing, a total of 1568 structures were selected for exploratory data analysis which revealed physicochemical properties, distributions and statistical significance between the two groups of inhibitors. PubChem fingerprinting with 11 different machine-learning classification models was computed. The comparative analysis showed the ET model performed well with accuracy values for the training set (0.999), cross-validation (0.970) and test set (0.977) in line with OECD guidelines. Moreover, to gain structural insights on the origin of CatK inhibition, 15 diverse molecules were selected for molecular docking. The CatK inhibitors (1 and 2) exhibited strong binding energies of -8.3 and -7.2 kcal/mol, respectively. MD simulation (300 ns) showed strong structural stability, flexibility and interactions in selected complexes. This synergy between QSAR, docking, MD simulation and machine learning models strengthen our evidence for developing novel and resilient CatK inhibitors.

Anaplastic Lymphoma Kinase (ALK) is a receptor tyrosine kinase within the insulin receptor superfamily. Alterations in ALK, such as rearrangements, mutations, or amplifications, have been detected in various tumours, including lymphoma, neuroblastoma, and non-small cell lung cancer. In this study, we outline a computational workflow designed to uncover new inhibitors of ALK. This process starts with a ligand-based exploration of the pharmacophoric space using 13 diverse sets of ALK inhibitors. Subsequently, quantitative structure-activity relationship (QSAR) modelling is employed in combination with a genetic function algorithm to identify the optimal combination of pharmacophores and molecular descriptors capable of elucidating variations in anti-ALK bioactivities within a compiled list of inhibitors. The successful QSAR model revealed three pharmacophores, two of which share three similar features, prompting their merger into a single pharmacophore model. The merged pharmacophore was used as a 3D search query to mine the National Cancer Institute (NCI) database for novel anti-ALK leads. Subsequent in vitro bioassay of the top 40 hits identified two compounds with low micromolar IC₅₀ values. Remarkably, one of the identified leads possesses a novel chemotype compared to known ALK inhibitors.