A bioinformatics approach to the identification of novel deleterious mutations of human TPMT through validated screening and molecular dynamics.

Polymorphisms of Thiopurine S-methyltransferase (TPMT) are known to be associated with leukemia, inflammatory bowel diseases, and more. The objective of the present study was to identify novel deleterious missense SNPs of TPMT through a comprehensive in silico protocol. The initial SNP screening protocol used to identify deleterious SNPs from the pool of all TPMT SNPs in the dbSNP database yielded an accuracy of 83.33% in identifying extremely dangerous variants. Five novel deleterious missense SNPs (W33G, W78R, V89E, W150G, and L182P) of TPMT were identified through the aforementioned screening protocol. These 5 SNPs were then subjected to conservation analysis, interaction analysis, oncogenic and phenotypic analysis, structural analysis, PTM analysis, and molecular dynamics simulations (MDS) analysis to further assess and analyze their deleterious nature. Oncogenic analysis revealed that all five SNPs are oncogenic. MDS analysis revealed that all SNPs are deleterious due to the alterations they cause in the binding energy of the wild-type protein. Plasticity-induced instability caused by most of the mutations as indicated by the MDS results has been hypothesized to be the reason for this alteration. While in vivo or in vitro protocols are more conclusive, they are often more challenging and expensive. Hence, future research endeavors targeted at TPMT polymorphisms and/or their consequences in relevant disease progressions or treatments, through in vitro or in vivo means can give a higher priority to these SNPs rather than considering the massive pool of all SNPs of TPMT.

Articulating target-mining techniques to disinter Alzheimer's specific targets for drug repurposing.

BACKGROUND AND OBJECTIVES: Alzheimer's Disease (AD), an extremely progressive neurodegenerative disorder is an amalgamation of numerous intricate pathological networks. This century old disease is still an unmet medical condition owing to the modest efficacy of existing therapeutic agents in antagonizing the multi-targeted pathological pathways underlying AD. Given the paucity in AD specific drugs, fabricating comprehensive research strategies to envision disease specific targets to channelize and expedite drug discovery are mandated. However, the dwindling approval rates and stringent regulatory constraints concerning the approval of a new chemical entity is daunting the pharmaceutical industries from effectuating de novo research. To bridge the existing gaps in AD drug research, a promising contemporary way out could be drug repurposing. This drug repurposing investigation is intended to envisage AD specific targets and create drug libraries pertinent to the shortlisted targets via a series of avant-garde bioinformatics and computational strategies. METHODS: Transcriptomic analysis of three AD specific datasets viz., GSE122063, GSE15222 and GSE5281 revealed significant Differentially Expressed Genes (DEGs) and subsequent Protein-Protein Interactions (PPI) network analysis captured crucial AD targets. Later, homology model was constructed through I-TASSER for a shortlisted target protein which lacked X-ray crystallographic structure and the built protein model was validated by molecular dynamic simulations. Further, drug library was created for the shortlisted target based on structural and side effect similarity with respective standard drugs. Finally, molecular docking, binding energy calculations and molecular dynamics studies were carried out to unravel the interactions exhibited by drugs from the created library with amino acids in active binding pocket of RGS4. RESULTS: SST and RGS4 were shortlisted as potentially significant AD specific targets, however, the less explored target RGS4 was considered for further sequential analysis. Homology model constructed for RGS4 displayed best quality when validated through Ramachandran plot and ERRAT plot. Subsequent docking and molecular dynamics studies showcased substantial affinity demonstrated by three drugs viz., Ziprasidone, Melfoquine and Metaxalone from the created drug libraries, towards RGS4. CONCLUSION: This virtual analysis forecasted the repurposable potential of Ziprasidone, Melfoquine and Metaxalone against AD based on their affinity towards RGS4, a key AD-specific target.

Application of comprehensive bioinformatics approaches to reconnoiter crucial genes and pathways underpinning hepatocellular carcinoma: a drug repurposing endeavor.

Hepatocellular carcinoma (HCC) is the fifth most common neoplasm in the world. Chronic inflammation of liver and associated wound healing processes collectively contribute to the development of cirrhosis which further progresses to dysplastic nodule and then to HCC. Etiological mediators and ongoing manipulations at cellular level in HCC are well established; however, key protein interactions and genetic alterations involved in stepwise hepatocarcinogenic pathways are seldom explored. This study aims to unravel novel targets of HCC and repurpose the FDA-approved drugs against the same. Genetic data pertinent to different stages of HCC were retrieved from GSE6764 dataset and analyzed via GEO2R. Subsequently, protein-protein interaction network analysis of differentially expressed genes was performed to identify the hub genes with significant interaction. Hub genes displaying higher interactions were considered as potential HCC targets and were validated thorough UALCAN and GEPIA databases. These targets were screened against FDA-approved drugs through molecular docking and dynamics simulation studies to capture the drugs with potential activity against HCC. Finally, cytotoxicity of the shortlisted drug was confirmed in vitro by MTT assay. CDC20 was identified as potential druggable target. Docking, binding energy calculations, and dynamic studies revealed significant interaction exhibited by Labetalol with CDC20. Further, in MTT assay, Labetalol demonstrated an IC50 of 200.29 µg/ml in inhibiting the cell growth of HepG2 cell line. In conclusion, this study discloses a series of key genetic underpinnings of HCC and recommends the pertinence of labetalol as a potential repurposable drug against HCC.

In silico approach towards the identification of potential inhibitors from Curcuma amada Roxb against H. pylori: ADMET screening and molecular docking studies.

Introduction: The present study attempts to identify potential targets of H. pylori for novel inhibitors from therapeutic herb, mango ginger (Curcuma amada Roxb.). Methods: Crystal structure of all the selected drug targets obtained from Protein Data Bank (PDB) were subjected to molecular docking against a total of 130 compounds (found to have biological activity against H. pylori ) were retrieved from public databases. Compounds with good binding affinity were selected for Prime MM-GBSA rescoring and molecular dynamics (MD) simulation. Final list of compounds were taken for ADMET predictions. Results: Based on binding affinity denoted by glide score and ligand efficiency, mango ginger compounds were found selective to shikimate kinase and type II dehydroquinase through hydrogen bonding and salt bridge interactions. Stability of the interactions and free energy calculations by Prime MM-GBSA results confirmed the affinity of mango ginger compounds towards both shikimate kinase and type II dehydroquinase. From the above results, 15 compounds were calculated for ADMET parameters, Lipinski's rule of five, and the results were found promising without any limitations. MD simulations identified gentisic acid as hit compound for shikimate kinase of H. pylori. Conclusion: Current study could identify the in silico potential of mango ginger compounds against shikimate kinase and type II dehydroquinase targets for H. pylori infections and are suitable for in vitro and in vivo evaluation.