Biology subjects

Sharif Siam, M. K.

Publications and source records attributed to Sharif Siam, M. K..

3 recordsLinked to original sources

bioRxiv2020

Investigation of the anti-TB potential of selected alkaloid constituents using molecular docking approach

Mycobacterium tuberculosis, the leading bacterial killer disease worldwide, causes Human tuberculosis (TB). Due to the growing problem of drug resistant Mycobacterium tuberculosis strains, new anti-TB drugs are urgently needed. Natural sources such as plant extracts have long played an important role in tuberculosis management and can be used as a template to design new drugs. A wide screening of natural sources is time consuming but the process can be significantly sped up using molecular docking. In this study, we used a molecular docking approach to investigate the interactions between selected natural constituents and three proteins MtPanK, MtDprE1 and MtKasA involved in the physiological functions of Mycobacterium tuberculosis which are necessary for the bacteria to survive and cause disease. The molecular docking score, a score that accounts for the binding affinity between a ligand and a target protein, for each protein was calculated against 150 chemical constituents of different classes to estimate the binding free energy. The docking scores represent the binding free energy. The best docking scores indicates the highest ligand protein binding which is indicated by the lowest energy value. Among the natural constituents, Shermilamine B showed a docking score of - 8.5kcal/mol, Brachystamide B showed a docking score of -8.6 kcal/mol with MtPanK, Monoamphilectine A showed a score of -9.8kcal/mol with MtDprE1.These three compounds showed docking scores which were superior to the control inhibitors and represent the opportunity of in vitro biological evaluation and anti-TB drug design. Consequently, all these compounds belonged to the alkaloid class. Specific interactions were studied to further understand the nature of intermolecular bonds between the most active ligands and the protein binding site residues which proved that among the constituents monoamphilectine A and Shermilamine B show more promise as Anti-TB drugs. Furthermore, the ADMET properties of these compounds or ligands showed that they have no corrosive or carcinogenic parameters. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/067090v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1542087org.highwire.dtl.DTLVardef@23ade8org.highwire.dtl.DTLVardef@6e93dcorg.highwire.dtl.DTLVardef@1ad6c6f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

A Computational Approach to Design Potential siRNA Molecules as a Prospective Tool for Silencing Nucleocapsid Phosphoprotein and Surface Glycoprotein Gene of SARS-CoV-2

An outbreak, caused by a RNA virus, SARS-CoV-2 named COVID-19 has become pandemic with a magnitude which is daunting to all public health institutions in the absence of specific antiviral treatment. Surface glycoprotein and nucleocapsid phosphoprotein are two important proteins of this virus facilitating its entry into host cell and genome replication. Small interfering RNA (siRNA) is a prospective tool of the RNA interference (RNAi) pathway for the control of human viral infections by suppressing viral gene expression through hybridization and neutralization of target complementary mRNA. So, in this study, the power of RNA interference technology was harnessed to develop siRNA molecules against specific target genes namely, nucleocapsid phosphoprotein gene and surface glycoprotein gene. Conserved sequence from 139 SARS-CoV-2 strains from around the globe was collected to construct 78 siRNA that can inactivate nucleocapsid phosphoprotein and surface glycoprotein genes. Finally, based on GC content, free energy of folding, free energy of binding, melting temperature and efficacy prediction process 8 siRNA molecules were selected which are proposed to exerts the best action. These predicted siRNAs should effectively silence the genes of SARS-CoV-2 during siRNA mediated treatment assisting in the response against SARS-CoV-2

bioinformatics↗

bioRxiv2020

Combinatorial analysis of gene regulatory network reveals the causal genetic basis of breast cancer and gene-specific personalized drug treatments

Cancer is the major burden of diseases around the world. The incidence and mortality rate of cancers is mounting up with the passage of days. Breast cancer is the most demoralizing cause of death, where both diseases interlocked with each other due to some genetic, biological and behavioral motives. The molecular mechanism of breast cancer through which they crop up and manifest together remains questionable. The genetic basis of protein-protein interactions and gene networks has elucidated a group of gene regulatory systems in Breast cancer. Thus, the extraction of all genomic and proteomic data has enabled unprecedented views of gene-protein co-expression, co-regulation, and interactions in the biological system. This study explored the biological system to develop a gene-disease interaction model by implementing the extracted genomic and proteomic data of Breast cancer. The disease-specific and correlated genes were pulled out and their cabling studied by PPI, disease pathway and drug-disease interaction data to articulate their role in disease development. By analyzing mined genes that are related to breast cancer, a network model is also proposed. Exploration of all the correlated genes, Hub and common genes have given some promising pieces of evidence surrounding the genetic networking models. The result of this prospective study disclosed breast cancer mediated crosslinking or possible metastatic relation on a genetic basis. Moreover, other diseases like prostate, colorectal and ovarian cancers are at the same risk and might count into consideration. The finding provides a narrative broad approach for understanding the genetic basis of these fatal diseases by the pathway analysis with gene regulatory network evaluation.

genetics↗