Inferring Therapeutic Targets in Candida albicans and Possible Inhibition through Natural Products: A Binding and Physiological Based Pharmacokinetics Snapshot.

Despite being responsible for invasive infections, fungal pathogens have been underrepresented in computer aided therapeutic target mining and drug design. Excess of Candida albicans causes candidiasis, causative of thrush and vaginal infection due to off-balance. In this study, we attempted to mine drug targets (n = 46) using a subtractive proteomic approach in this pathogenic yeast and screen natural products with inhibition potential against fructose-bisphosphate aldolase (FBA) of the C. albicans. The top compound selected on the basis of best docking score from traditional Indian medicine/Ayurvedic library was (4-Hydroxybenzyl)thiocarbamic acid, from the ZINC FBA inhibitor library was ZINC13507461 (IUPAC name: [(2R)-2-hydroxy-3-phosphonooxypropyl] (9E,12E)-octadeca-9,12-dienoate), and from traditional Tibetan medicine/Sowa rigpa was Chelerythrine (IUPAC name: 1,2-Dimethoxy-12-methyl-9H-[1,3]benzodioxolo[5,6-c]phenanthridin-12-ium), compared to the control (2E)-1-(4-nitrophenyl)-2-[(4-nitrophenyl)methylidene]hydrazine. No Ames toxicity was predicted for prioritized compounds while control depicted this toxicity. (4-Hydroxybenzyl)thiocarbamic acid showed hepatotoxicity, while Chelerythrine depicted hERG inhibition, which can lead to QT syndrome, so we recommend ZINC13507461 for further testing in lab. Pharmacological based pharmacokinetic modeling revealed that it has low bioavailability and hence, absorption in healthy state. In cirrhosis and renal impairment, absorption and plasma accumulation increased so we recommend further investigation into this occurrence and recommend high dosage in further tests to increase bioavailability.

An Overview of Algorithms and Associated Applications for Single Cell RNA-Seq Data Imputation.

Single cell RNA-Seq technology enables the assessment of RNA expression in individual cells. This makes it popular in experimental biology for gleaning specifications of novel cell types as well as inferring heterogeneity. Experimental data conventionally contains zero counts or dropout events for many single cell transcripts. Such missing data hampers the accurate analysis using standard workflows, designed for massive RNA-Seq datasets. Imputation for single cell datasets is done to infer the missing values. This was traditionally done with ad-hoc code but later customized pipelines, workflows and specialized software appeared for this purpose. This made it easy to benchmark and cluster things in an organized manner. In this review, we have assembled a catalog of available RNA-Seq single cell imputation algorithms/workflows and associated softwares for the scientific community performing single-cell RNA-Seq data analysis. Continued development of imputation methods, especially using deep learning approaches, would be necessary for eradicating associated pitfalls and addressing challenges associated with future large scale and heterogeneous datasets.