Biology subjects

Mondal, D.

Publications and source records attributed to Mondal, D..

3 recordsLinked to original sources

bioRxiv2020

An extensive computational approach to inhibit MSP-1 of P.vivax elucidates further horizon in the establishment next generation therapeutics against malaria

Malaria represents a life-threatening disease caused by the obligate intra-erythrocytic protozoa of the Plasmodium genus, exerting a sinister global health burden and accounting for approximately 660,000 deaths annually. Additionally, 219 million new cases are reported each year, most of which result from the growing issue of artemisinin resistance shown by the Plasmodium parasite. Much of the research done for the purpose of development of therapeutics against malaria has traditionally been focused on Plasmodium falciparum, which is responsible for majority of the cases of mortality due to malaria, Plasmodium vivax is also known to contribute greatly towards the malaria relate morbidities particularly in vivax endemic areas. In this study, we have used two different computational approaches aimed at establishing newer concepts towards the development of advanced therapeutics against vivax malaria by targeting the surface antigen, merozoite surface protein-1 (MSP-1). In-silico approach involving computational siRNA designing against MSP-1 resulted in a total of four candidate siRNAs being rationally validated following corroboration with a plethora of algorithms. Additionally, molecular docking analysis unraveled a total of three anti-parasitic peptides. These peptides namely: AP02283, AP02285 and AP00101 were found to exhibit considerable binding affinity with MSP-1 of P.vivax, thus providing an apparent indication of their anti-malarial property and affirming their potency to be used as novel molecules for development of next generation anti-malarials. However, irrespective of the prospective magnitude of these in-silico findings, the results require extensive validation by further rigorous laboratory experiments involving both in-vitro and in-vivo approaches.

bioinformatics↗

bioRxiv2020

The covariance shift (C-SHIFT) algorithm for normalizing biological data

Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. In this paper, we introduce a novel normalization technique, called the covariance shift (C-SHIFT) method. This normalization algorithm uses optimization techniques together with the blessing of dimensionality philosophy and energy minimization hypothesis for covariance matrix recovery under additive noise (in biology, known as the bias). Thus, it is perfectly suited for the analysis of logarithmic gene expression data. Numerical experiments on synthetic data demonstrate the methods advantage over the classical normalization techniques. Namely, the comparison is made with rank, quantile, cyclic LOESS (locally estimated scatterplot smoothing), and MAD (median absolute deviation) normalization methods.

bioinformatics↗

bioRxiv2020

Improved Supra-Organization Is A Conformational And Functional Adaptation Of Respiratory Complexes

Multiple surveillance mechanisms accelerate proteasome mediated degradation of misfolded proteins to prevent protein aggregation inside and outside mitochondria. But how cells safeguard mitochondrial function despite increased protein aggregation during proteasome inactivation? Here, using two-dimensional complexome profiling, we extensively characterize the dynamic states of respiratory complexes (RCs) in proteasome-inhibited cells. We report that RC-subunits are increasingly integrated into supra-organizations to optimize catalytic activity simultaneous to their aggregation inside mitochondria. Complex-II (CII) and CV are incorporated into oligomers. CI, CIII, and CIV subcomplexes are associated into holocomplexes followed by integration into supercomplexes. Time-course experiments reveal that the core (CI+CIII2) stoichiometry of supercomplex (I+III2+IV) is preserved during early-stress while CIV composition varies. Simultaneously, increased CI-activity suggests conformational optimization of supercomplexes for better function. Re-establishment of steady-state stoichiometry and relative increase in supercomplex-quantity consolidates functional adaptation during prolonged proteasome-inhibition. Together, we name this pre-emptive adaptive mechanism as improved Supra-organization of Respiratory Complexes (iSRC). We find that iSRC is active in multiple protein-unfolding stresses, in multiple cell-types that differ in proteostatic and metabolic demands, and reversible upon stress-withdrawal.

biochemistry↗