In silico drug repositioning of FDA-approved drugs to predict new inhibitors for alpha-synuclein aggregation

One of the hallmarks of Parkinson's disease (PD), a long-term neurodegenerative syndrome, is the accumulation of alpha-synuclein (α-syn) fibrils. Despite numerous studies and efforts, inhibition of α-syn protein aggregation is still a challenge. To overcome this issue, we propose an in silico pharmacophore-based repositioning strategy, to find a pharmaceutical drug that, in addition to their defined role, can be used to prevent aggregation of the α-syn protein. Ligand-based pharmacophore modeling was developed and the best model was selected with validation parameters including 72 % sensitivity, 98 % specificity and goodness score about 0.7. The optimal model has three groups of hydrogen bond donor (HBD), three groups of hydrogen bond acceptor (HBA), and two aromatic rings (AR). The FDA-Approved reports in the ZINC15 database were screened with the pharmacophore model taken from inhibitor compounds. The model identified 22 hits, as promising candidate drugs for Parkinson's therapy. It is noteworthy that among these, 10 drugs have been reported to inhibition of α-syn aggregation or treat/reduce Parkinson's pathogenesis. This model was used to virtual screen ZINC, NCI databases, and natural products from the pomegranate. The results of this screen were filtered for their inability to cross the blood-brain barrier, poor oral bioavailability, etc. Finally, the selected compounds of two ZINC and NCI databases were combined and structurally clustered. Remained compounds were clustered in 28 different clusters, and the 17 compounds were introduced as final candidates

A Unified Model of the GABA(A) Receptor Comprising Agonist and Benzodiazepine Binding Sites

We present a full-length α(1)β(2)γ(2) GABA receptor model optimized for agonists and benzodiazepine (BZD) allosteric modulators. We propose binding hypotheses for the agonists GABA, muscimol and THIP and for the allosteric modulator diazepam (DZP). The receptor model is primarily based on the glutamate-gated chloride channel (GluCl) from C. elegans and includes additional structural information from the prokaryotic ligand-gated ion channel ELIC in a few regions. Available mutational data of the binding sites are well explained by the model and the proposed ligand binding poses. We suggest a GABA binding mode similar to the binding mode of glutamate in the GluCl X-ray structure. Key interactions are predicted with residues α(1)R66, β(2)T202, α(1)T129, β(2)E155, β(2)Y205 and the backbone of β(2)S156. Muscimol is predicted to bind similarly, however, with minor differences rationalized with quantum mechanical energy calculations. Muscimol key interactions are predicted to be α(1)R66, β(2)T202, α(1)T129, β(2)E155, β(2)Y205 and β(2)F200. Furthermore, we argue that a water molecule could mediate further interactions between muscimol and the backbone of β(2)S156 and β(2)Y157. DZP is predicted to bind with interactions comparable to those of the agonists in the orthosteric site. The carbonyl group of DZP is predicted to interact with two threonines α(1)T206 and γ(2)T142, similar to the acidic moiety of GABA. The chlorine atom of DZP is placed near the important α(1)H101 and the N-methyl group near α(1)Y159, α(1)T206, and α(1)Y209. We present a binding mode of DZP in which the pending phenyl moiety of DZP is buried in the binding pocket and thus shielded from solvent exposure. Our full length GABA(A) receptor is made available as Model S1

Bis(trans-cinnamaldehyde)-1,3-propanediimine) mercury(II)chloride, [Hg(BPPPB)Cl2] as Carrier for Construction of Iodide Selective Electrode

Highly selective poly(vinyl chloride) (PVC) membrane of iodide ion selective electrode based on the application of bis(trans-cinnamaldehyde)-1,3-propanediimine)mercury(II)chloride [Hg(BPPPB)Cl2] as new carrier by coating the membrane ingredient on the surface of graphite electrodes has been reported. The effect of various parameters including membrane composition, pH and possible interfering anions on the response properties of the electrode were examined. At optimum conditions, the proposed sensor exhibited Nernstian responses toward iodide ion in a wide concentration range of 1×10-6 to 0.1 M with slopes of 58.0±0.8 mV per decade of iodide concentration over a wide pH range of 3-11 with detection limit of detection of ~8×10-7 M. The sensors have stable responses times of ≤ 5 s and give stable response after conditioning in 0.05 M KI for 24 h with its response is stable at least 2 months without any considerable divergence in its potential response characteristics. The electrodes were successfully applied for the direct determination of iodide ion in water sample and as indicator electrodes in precipitation titrations

Scoring multiple features to predict drug disease associations using information fusion and aggregation

<p>Prediction of drug–disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug–disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug–drug level and the drug–disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug–disease interactions. The method was validated against a well-established drug–disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.</p

Revealing key structural features for developing new agonists targeting δ opioid receptor: Combined machine learning and molecular modeling perspective

Despite being the most widely prescribed and misused type of medication, opioids continue to function as robust pain relief agents; however, overdosing is a significant cause of fatalities among opioid users. The δ-opioid receptor (DOR) has immense promise in treating long-term pain by producing anxiolytic and antidepressant-like outcomes. Although DOR agonists play a crucial role, their clinical implementation is restricted because of the probable manifestation of severe, life-threatening complications. A Python-based machine learning approach was employed to develop a quantitative structure–activity relationship (QSAR) model in this study. To address this, 4217 compounds and their associated biological inhibition activities were retrieved from the gpcrdb database. The K-best features selection method revealed three key structural features such as SLOGPVSA2, Chi6ch, and S17 contributed significantly to the best model performance. Statistical analysis, K-fold cross-validation, applicability domain analysis, and external validation using 38 unseen FDA-approved drug data confirmed the robustness of the predictive model. A molecular docking study in along with Ligand–Receptor Contact Fingerprints (LRCFs) using the essential chemical interactions described for analog ligands releaved the key contact interactions of Asp 128, Tyr 129, Met 132, Trp 274, Ile 277, and Tyr 308 residues in the total binding affinities upon complexation. Our combinatorial study using regression QSAR and ligand–receptor Contact, analysis could serve in the design of more rational compounds for drug discovery targeting DOR

Clinical and microbial epidemiology of otomycosis in the city of Yasuj, southwest Iran, revealing aspergillus tubingensis as the dominant causative agent

Purpose. Otomycosis is a mycotic infection of the external auditory canal and can be caused by a wide range of fungal species. In this study, we aimed to identify fungal isolates from patients suspected of otomycosis. Methodology. External ear canal samples were taken from patients referred to the outpatient department of Shahid-Mofatteh Clinic in the city of Yasuj, Iran, and examined by direct microscopy and culture. DNA of the isolated fungi was tested by internal transcribed spacer PCR restriction fragment length polymorphism analysis for identification of yeasts and β-tubulin sequencing for identification of Aspergillus species. Results. Among 275 patients suspected of otomycosis, 144 cases (83 female and 61 male) were confirmed with otomycosis. For 89% (n=128) of positive cultures, microscopy was also positive, while there were no cases with a microscopy-positive and culture-negative result. The predominant predisposing factor was self-cleaning of the external ear using unhygienic tools, and the main risk occupation was ‘housewife’. The most common isolated fungi were typically Aspergillus (n=120), including 73 isolates of Aspergillus section Nigri, 43 of section Flavi, 3 of section Terrei and 1 of section Fumigati. After sequencing, 44 out of 73 strains primarily identified as Aspergillus niger turned out to be Aspergillus tubingensis. Thirty-five isolates were identified as Candida, including Candida parapsilosis (n=22), Candida albicans (n=12) and Candida tropicalis (n=1). Conclusion. Aspergillus tubingensis was the most common species involved in otomycosis. This work corroborates the difficulty of precise identification of species within the black Aspergilli by morphological characteristics

Mucormycosis in Iran: A six-year retrospective experience

Mucormycosis is a devastating infection caused by Mucoralean fungi (Mucormycotina, Mucorales). Data concerning the global epidemiology of mucormycosis are scarce and little is known about the characteristics of mucormycosis in Iran. In this study, we aimed to understand the distribution of this infection in Iran retrospectively and to ascertain whether the patterns of infection are associated with specific host factors or not. A total of 208 cases were included in this study occurring during 2008–2014 and were validated according to (EORTC/MSG) criteria. A rising trend as significant increase from 9.7% in 2008 to 23.7% in 2014 was observed. The majority of patients were female (51.4%) with median age of 50 and the infections were seen mostly in autumn season (39.4%). Diabetes mellitus (75.4%) was the most common underlying condition and sinus involvement (86%) was the mostly affected site of infection. Amphotericin B (AmB) was the drug of choice for the majority of cases. Sixty four isolates did not show any growth in the lab and only 21 cases were evaluated by ITS sequencing, among them; Rhizopus arrhizus var. arrhizus was the dominant species. Considering the high mortality rate of mucormycosis, early and accurate diagnosis, with the aid of molecular methods may provide accurate treatments and improve the survival rate. Therefore, increased monitoring and awareness of this life-threatening disease is critical. © 2018 Elsevier Masson SA

Mucormycosis in Iran: A six-year retrospective experience

Mucormycosis is a devastating infection caused by Mucoralean fungi (Mucormycotina, Mucorales). Data concerning the global epidemiology of mucormycosis are scarce and little is known about the characteristics of mucormycosis in Iran. In this study, we aimed to understand the distribution of this infection in Iran retrospectively and to ascertain whether the patterns of infection are associated with specific host factors or not. A total of 208 cases were included in this study occurring during 2008–2014 and were validated according to (EORTC/MSG) criteria. A rising trend as significant increase from 9.7% in 2008 to 23.7% in 2014 was observed. The majority of patients were female (51.4%) with median age of 50 and the infections were seen mostly in autumn season (39.4%). Diabetes mellitus (75.4%) was the most common underlying condition and sinus involvement (86%) was the mostly affected site of infection. Amphotericin B (AmB) was the drug of choice for the majority of cases. Sixty four isolates did not show any growth in the lab and only 21 cases were evaluated by ITS sequencing, among them; Rhizopus arrhizus var. arrhizus was the dominant species. Considering the high mortality rate of mucormycosis, early and accurate diagnosis, with the aid of molecular methods may provide accurate treatments and improve the survival rate. Therefore, increased monitoring and awareness of this life-threatening disease is critica