Applications of in silico methods to analyze the toxicity and estrogen receptor-mediated properties of plant-derived phytochemicals.

A myriad of phytochemicals may have potential to lead toxicity and endocrine disruption effects by interfering with nuclear hormone receptors. In this examination, the toxicity and estrogen receptor-binding abilities of a set of 2826 phytochemicals were evaluated. The endpoints mutagenicity, carcinogenicity (both CAESAR and ISS models), developmental toxicity, skin sensitization and estrogen receptor relative binding affinity (ER_RBA) were studied using the VEGA QSAR modeling package. Alongside the predictions, models were providing possible information for applicability domains and most similar compounds as similarity sets from their training sets. This information was subjected to perform the clustering and classification of chemicals using Self-Organizing Maps. The identified clusters and their respective indicators were considered as potential hotspot structures for the specified data set analysis. Molecular screening interpretations of models were exhibited accurate predictions. Moreover, the indication sets were defined significant clusters and cluster indicators with probable prediction labels (precision). Accordingly, developed QSAR models showed good predictive abilities and robustness, which observed from applicability domains, representation spaces, clustering and classification schemes. Furthermore, the designed new path could be useful as a valuable approach to determine toxicity levels of phytochemicals and other environmental pollutants and protect the human health.

Elucidation of endocrine-disrupting polychlorinated biphenyls binding potency with steroidogenic genes: Integration of in silico methods and ensemble docking approaches.

A myriad of polychlorinated biphenyls (PCBs) may have potential to reproductive axis and endocrine disruptions. PCBs mostly breach the cholesterol biotransformation in mitochondria through interfering with steroidogenic genes and lead to adverse consequences in steroidogenesis; however, studies are scanty. In this examination, the combinations of quantitative structure-activity relationship (QSAR) modeling and ensemble docking approaches was performed to envisage structural properties of PCBs that influence the developmental toxicity, estrogen receptor-mediated impacts, and to provide a better comprehension of binding levels between PCBs, steroidogenic acute regulatory protein (StAR) and cholesterol side-chain cleavage enzyme (CYP11A1). Prognostic QSAR models were illustrated with good robustness and predictive ability. Models provide extensive data on applicability domain and similarities between PCBs and training set compounds was used to implement for clustering and classification of toxic PCBs by employing Self-Organizing Maps. Docking and interaction profiles interpretations provided various insights into the structural features of PCBs that influence the cholesterol binding to StAR and CYP11A1 domains. The non-polar, atomic π - stacking and halogen bonds of PCBs with novel hotspots residues of StAR and CYP11A1 was indicated subtle conformational changes that barrier to cholesterol binding and/or locks to cholesterol ejection from Ω1-loop of StAR, and inhibits cholesterol to pregnenolone biosynthesis by CYP11A1; thus, these are probably revealed as block-cluster mechanisms. These outcomes are potential to be useful to predict developmental toxicity, endocrine disruption potencies and anti-steroidogenic activities of other environmental pollutants and provided sorted pinpoints for further evaluation of interaction mechanisms of PCBs with other sterodogenic genes.

Integration of in silico approaches to determination of endocrine-disrupting perfluorinated chemicals binding potency with steroidogenic acute regulatory protein.

A myriad of perfluorinated compounds (PFCs) have the ability to interfere with steroidogenic acute regulatory (StAR) protein. Consequently, PFCs breaches cholesterol biotransformation in mitochondria and cause fatal consequences in steroidogenesis, however, these were poorly characterized. In the present study, we have evaluated toxic potencies, nuclear mediated probabilities and interaction profiles with StAR of PFCs using computational system biology tools. Toxicity endpoints revealed that PFCs contain high carcinogenicity, developmental toxicity, skin sensitization effects with low mutagenic activity. Consensus qualitative nuclear receptor agonist models show higher probability rates towards ER and PPAR-γ receptor than AR and AhR models were observed. To poise the subtle fluctuations of actual predictions, balanced accuracy and MCC were computed, and they signify perfect correlation ranges in all models. Screening studies resulting protein-ligand interaction profiles showed that residues Asn148, Asn150, Glu169, Ala171, Arg182, Phe184, Arg188, Trp241, Thr263 and Phe267 were identified as novel hotspots, participated in halogen bonds, H-bonds, atomic π-stacking, π-cation interactions and salt-bridges formation. Thus, the additional bonds contribute conformer stability that holds the protein structure at flexible state, so that PFCs acts as a barrier to cholesterol binding. From docking outcomes, representation space was created, that specifies high and medium StAR binders were occupied in toxic endpoints space with active concern. PFCs restrain molecular features and mitochondrial membrane disruption functions were revealed by efficient toxicogenomics studies. These data indicate toxicity and StAR protein binding levels of PFCs, sorted pinpoints could be useful in a promising way to know the other environmental pollutants and health risks.