Computational Prediction of Compound-Protein Interactions for Orphan Targets Using CGBVS

A variety of Artificial Intelligence (AI)-based (Machine Learning) techniques have been developed with regard to in silico prediction of Compound–Protein interactions (CPI)—one of which is a technique we refer to as chemical genomics-based virtual screening (CGBVS). Prediction calculations done via pairwise kernel-based support vector machine (SVM) is the main feature of CGBVS which gives high prediction accuracy, with simple implementation and easy handling. We studied whether the CGBVS technique can identify ligands for targets without ligand information (orphan targets) using data from G protein-coupled receptor (GPCR) families. As the validation method, we tested whether the ligand prediction was correct for a virtual orphan GPCR in which all ligand information for one selected target was omitted from the training data. We have specifically expressed the results of this study as applicability index and developed a method to determine whether CGBVS can be used to predict GPCR ligands. Validation results showed that the prediction accuracy of each GPCR differed greatly, but models using Multiple Sequence Alignment (MSA) as the protein descriptor performed well in terms of overall prediction accuracy. We also discovered that the effect of the type compound descriptors on the prediction accuracy was less significant than that of the type of protein descriptors used. Furthermore, we found that the accuracy of the ligand prediction depends on the amount of ligand information with regard to GPCRs related to the target. Additionally, the prediction accuracy tends to be high if a large amount of ligand information for related proteins is used in the training.

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

Spinocerebellar ataxia (SCA-2) type-2 is a rare neurological disorder among the nine polyglutamine disorders, mainly caused by polyQ (CAG) trinucleotide repeats expansion within gene coding ataxin-2 protein. The expanded trinucleotide repeats within the ataxin-2 protein sequesters transcriptional cofactors i.e., CREB-binding protein (CBP), Ataxin-2 binding protein 1 (A2BP1) leading to a state of hypo-acetylation and transcriptional repression. Histone de-acetylases inhibitors (HDACi) have been reported to restore transcriptional balance through inhibition of class IIa HDAC's, that leads to an increased acetylation and transcription as demonstrated through in-vivo studies on mouse models of Huntington's. In this study, 61 di-aryl cyclo-propanehydroxamic acid derivatives were used for developing three dimensional (3D) QSAR and pharmacophore models. These models were then employed for screening and selection of anti-ataxia compounds. The chosen QSAR model was observed to be statistically robust with correlation coefficient (r²) value of 0.6774, cross-validated correlation coefficient (q²) of 0.6157 and co-relation coefficient for external test set (pred_r²) of 0.7570. A high F-test value of 77.7093 signified the robustness of the model. Two potential drug leads ZINC 00608101 (SEI) and ZINC 00329110 (ACI) were selected after a coalesce procedure of pharmacophore based screening using the pharmacophore model ADDRR.20 and structural analysis using molecular docking and dynamics simulations. The pharmacophore and the 3D-QSAR model generated were further validated for their screening and prediction ability using the enrichment factor (EF), goodness of hit (GH), and receiver operating characteristics (ROC) curve analysis. The compounds SEI and ACI exhibited a docking score of −10.097 and −9.182 kcal/mol, respectively. An evaluation of binding conformation of ligand-bound protein complexes was performed with MD simulations for a time period of 30 ns along with free energy binding calculations using the g_mmpbsa technique. Prediction of inhibitory activities of the two lead compounds SEI (7.53) and ACI (6.84) using the 3D-QSAR model reaffirmed their inhibitory characteristics as potential anti-ataxia compounds.

Trace Elements Speciation of Submicron Particulate Matter (PM1) Collected in the Surroundings of Power Plants

This study reports the concentrations of PM1 trace elements (As, Cd, Co, Cr, Hg, Mn, Ni, Pb, Sb and Se) content in highly mobile (F1), mobile (F2), less mobile (F3) and not mobile (F4) fractions in samples that were collected in the surroundings of power plants in southern Poland. It also reports source identification by enrichment factors (EF) and a principal component analysis (PCA). There is limited availability of scientific data concerning the chemical composition of dust, including fractionation analyses of trace elements, in the surroundings of power plants. The present study offers important results in order to fill this data gap. The data collected in this study can be utilized to validate air quality models in this rapidly developing area. They are also crucial for comparisons with datasets from similar areas all over the world. Moreover, the identification of the bioavailability of selected carcinogenic and toxic elements in the future might be used as output data for potential biological and population research on risk assessment. This is important in the context of air pollution being hazardous to human health.

PM2.5 in Urban and Rural Nursery Schools in Upper Silesia, Poland: Trace Elements Analysis

Indoor air quality (IAQ) in nursery schools is an emerging public health challenge. Particular attention should be paid to younger children, because they are more vulnerable to air pollution than older children. Among air pollutants, fine particulate matter (PM_2.5) is of the greatest interest mainly due to its strong association with acute and chronic effects on children’s health. In this paper, we present concentrations of PM_2.5 and the composition of its trace elements at naturally ventilated nursery schools located in the area of Gliwice, Poland. The nursery schools were selected to characterize areas with different degrees of urbanization and traffic densities during the winter and spring seasons. The results indicate there is a problem with elevated concentrations of PM_2.5 inside the examined classrooms. The children’s exposure to trace elements was different based on localization and season. PM_2.5 concentration and its trace element composition have been studied using correlation coefficients between the different trace elements, the enrichment factor (EF) and principal component analysis (PCA). PCA allowed the identification of the three components: anthropogenic and geogenic sources (37.2%), soil dust contaminated by sewage sludge dumping (18.6%) and vehicular emissions (19.5%).