Biology subjects

Hazra, D.

Publications and source records attributed to Hazra, D..

3 recordsLinked to original sources

bioRxiv2024

Single-Cell Peripheral Immunoprofiling of Lewy Body Disease in a Multi-site Cohort

Studies implicated peripheral organs involvement in the development of Lewy body disease (LBD), a spectrum of neurodegenerative diagnoses that include Parkinsons Disease (PD) without or with dementia (PDD) and dementia with Lewy bodies (DLB). This study characterized peripheral immune responses unique to LBD at single-cell resolution. Peripheral mononuclear cell (PBMC) samples were collected from sites across the U.S. The diagnosis groups comprise healthy controls (HC, n=164), LBD (n=132), Alzheimers disease dementia (ADD, n=98), other neurodegenerative disease controls (NDC, n=21), and immune disease controls (IDC, n=14). PBMCs were activated with three stimulants, stained by surface and intracellular signal markers, and analyzed by flow cytometry, generating 1,184 immune features. Our model classified LBD from HC with an AUROC of 0.90{+/-}0.06. The same model distinguished LBD from ADD, NDC, IDC, or other common conditions associated with LBD. Model predictions were driven by pPLC{gamma}2, p38, and pSTAT5 signals from specific cell populations and activations.

neuroscience↗

bioRxiv2023

De novo drug designing coupled with brute force screening and structure guided lead optimization gives highly specific inhibitor of METTL3: a potential cure for Acute Myeloid Leukaemia

Expression of METTL3, a SAM dependent methyltransferase, which deposits m6A on mRNA is linked to poor prognosis in Acute Myeloid Leukaemia and other type of cancers. Down regulation of this epitranscriptomic regulator has been found to inhibit cancer progression. Silencing the methyltransferase activity of METTL3 is a lucrative strategy to design anticancer drugs. In this study 3600 commercially available molecules were screened against METTL3 using brute force screening approach. However, none of these compounds take advantage of the unique Y-shaped binding cavity of the protein, raising the need for de novo drug designing strategies. As such, 125 branched, Y-shaped molecules were designed by "stitching" together the chemical fragments of the best inhibitors that interact strongly with the METTL3 binding pocket. This results in molecules that have the three-dimensional structure and functional groups which enable it to fit in the METTL3 cavity like fingers in a glove, having unprecedented selectivity and binding affinities. The designed compounds were further refined based on Lipinskis rule, docking score and synthetic accessibility. The molecules faring well in these criteria were simulated for 100ns to check the stability of the protein inhibitor complex followed by binding free energy calculation.

bioinformatics↗

bioRxiv2022

Identification of potential natural compound inhibitors and drug like molecules against human METTL3 by docking and molecular dynamics simulation

Nucleotide level chemical modification in transcriptome is critical in regulating different cellular processes, including cancer. The most investigated epitranscriptomic modification is methylation at the N6-position of adenosine (m6A). This dynamic modification process is carried out by: writer, reader and eraser proteins. Writers are methyltransferases, METTL3 is the major writer that works in association with METTL14, an accessory protein. Extensive study revealed that cancer progression for acute myeloid leukaemia, gastric cancer, colorectal cancer, hepatocellular carcinoma, and lung cancer is directly contributed by irregular expression of METTL3. Targeting METTL3, has opened a new window in the development of new inhibitors/drugs. In this study, 80 commercially available compounds were found from an unbiased screening by molecular docking, showing better score when compared with the existing substrate/substrate-analogue and the inhibitor bound crystal structures in terms of docking score and binding energy calculation. Among this pool of compounds, the best seven small molecules, AMF, RAD, JNJ, MEH, ECP, MHN, SGI, have been selected and further validated by different computational tools like binding energy calculation, molecular dynamics simulation etc. The novel hits found in this study can function as lead compounds which can be developed into inhibitors as well as drugs, specific against METTL3.

bioinformatics↗