Biology subjects

B, V.

Publications and source records attributed to B, V..

2 recordsLinked to original sources

bioRxiv2024

Chitin synthase targeting antifungal agents for Mucormycosis (Black fungus disease) caused by Rhizopus delemar - An in-silico study

Mucormycosis, a severe fungal infection caused by Mucorales fungi, particularly Rhizopus delemar, has prompted the development of new and more potent antifungal drugs due to the emergence of drug-resistant strains. Currently used drugs are known to be toxic to human cells also, which is a major drawback in their administration. Antifungal drugs commercially under use and their structurally modified analogues along with 30 plants reported with antifungal activity from IMPPAT database, totalling 229 compounds, were screened against the best modelled structure of Chitin Synthase, a key target enzyme of Rhizopus delemar by in-silico molecular docking studies. Computerized methods to assess absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles was employed to further filter key lead drug candidates from among the docked ligands that could act as potential antifungal medications. Modified structures of Posaconazole, Nikkomycin and Isavuconazole were found to give better docking results than compared to the original drugs, though they failed to comply with ADMET parameters. Among the phytochemicals, 1-nonacosanol displayed binding affinity (highest LibDock score) greater than the synthetic drugs and their derivatives, though it too failed to comply with ADMET parameters. The phytochemical Dihydrocapsaicin was found to be the best compound that satisfied all the parameters such as ADMET, TOPKAT, Lipinskis and Vebers rule along with good binding affinity. All in all, this novel study paves a way to investigate the interaction of Chitin Synthase inhibitors at molecular level to achieve an alternative solution with less toxic effects to treat Mucormycosis.

bioinformatics↗

bioRxiv2018

Inferring causal pathways among three or more variables from steady-state correlations in a homeostatic system

Cross-sectional correlations between two variables have limited implications for causality. We show here that in a homeostatic system with three or more inter-correlated variables, it is possible to make causal inferences from steady-state data. Every putative pathway between three variables makes a set of differential predictions that can be tested with steady state data. For example, among 3 variables, A, B and C, the coefficient of determination, [Formula] is predicted by the product of [Formula] and [Formula] for some pathways, but not for others. Residuals from a regression line are independent of residuals from another regression for some pathways, but positively or negatively correlated for certain other pathways. Different pathways therefore have different prediction signatures, which can be used to accept or reject plausible pathways. We apply these principles to test the classical pathway leading to a hyperinsulinemic normoglycemic insulin-resistant, or pre-diabetic state using four different sets of epidemiological data. Currently, a set of indices called HOMA-IR and HOMA-{beta} are used to represent insulin resistance and glucose-stimulated insulin response by {beta} cells respectively. Our analysis shows that if we assume the HOMA indices to be faithful indicators, the classical pathway must in turn, be rejected. Among the populations sampled, the classical pathway and faithfulness of the HOMA indices cannot be simultaneously true. The principles and tools described here can find wide application in inferring plausible regulatory mechanisms in homeostatic systems based on epidemiological data.

physiology↗