Jahirul Islam

The multidrug transporter P-glycoprotein is an ATP binding cassette (ABC) exporter responsible for resistance to tumor cells during chemotherapy. This study was designed with computational approaches aimed at identifying the best potent inhibitors of P-glycoprotein. Although many compounds have been suggested to inhibit P-glycoprotein, however, their information on bioavailability, selectivity, ADMET properties, and molecular interactions has not been revealed. Molecular docking, ADMET analysis, molecular dynamics, Principal component analysis (PCA), and binding free energy calculations were performed. Two compounds D1 and D2 showed the best docking score against P-glycoprotein and both compounds have 4-thiazolidinone derivatives containing indolin-3 one moiety are novel anti-tumor compounds. ADMET calculation analysis predicted D1 and D2 to have acceptable pharmacokinetic properties. The MD simulation discloses that D1-P-glycoprotein and D2-P-glycoprotein complexes are in stable conformation as apo-form. Hydrophobic amino acid such as phenylalanine plays significant on the interactions of inhibitors. Principal component analysis shows that both complexes are relatively similar variables as apo-form except planarity and Columbo energy profile. In addition, Quantitative Structural Activity Relationship (QSAR) of the ligand candidates were subjected to the principal component analysis (PCA) for pattern recognition. Partial-least-square (PLS) regression analysis was further utilized to model drug candidates’ QSAR for subsequent prediction of the binding energy of validated drug candidates. PCA revealed groupings of the drug candidates based on the similarity or differences in drug candidates QSAR. Moreover, the developed PLS regression accurately predicted the values of the binding energy of drug candidates, with low residual error of prediction. Communicated by Ramaswamy H. Sarma

Inhibition of tumor necrosis factor-alpha (TNF-α) has become a feasible target for alleviating inflammation-mediated diseases. Currently, techniques developed, such as anti-TNF antibody therapies, prove not to be nearly as beneficial enough to effectively treat inflammation-mediated syndromes because of the increased risk for severe infections and malignancies. Our study has undertaken the attempt of identifying small molecules to inhibit TNF-α. This study manually selected 37 plant-derived compounds based on IC50 value from various literature, which showed inhibitory activity against TNF-α. By employing an in silico pipeline, we have aimed to explore the binding modes, to discover the most possible mechanism of inhibition, as well as, for a deeper understanding of structural changes, which is necessary for rationalization of the targeted inhibition by our proposed bioactive compounds. Therefore, this study has identified two potential compounds through advanced induced fit docking and simulation study. The stability of protein-ligand complex and structural changes was studied by performing 100 ns molecular dynamics simulation with its binding energy estimated through MM-PBSA analysis.

Serine-threonine kinase11 (STK11) is a tumor suppressor gene which plays a key role in regulating cell growth and apoptosis. It is widely known as a multitasking kinase and engaged in cell polarity, cell cycle arrest, chromatin remodeling, energy metabolism, and Wnt signaling. The substitutions of single amino acids in highly conserved regions of the STK11 protein are associated with Peutz–Jeghers syndrome (PJS), which is an autosomal dominant inherited disorder. The abnormal function of the STK11 protein is still not well understood. In this study, we classified disease susceptible single nucleotide polymorphisms (SNPs) in STK11 by using different computational algorithms. We identified the deleterious nsSNPs, constructed mutant protein structures, and evaluated the impact of mutation by employing molecular docking and molecular dynamics analysis. Our results show that W239R and W308C variants are likely to be highly deleterious mutations found in the catalytic kinase domain, which may destabilize structure and disrupt the activation of the STK11 protein as well as reduce its catalytic efficiency. The W239R mutant is likely to have a greater impact on destabilizing the protein structure compared to the W308C mutant. In conclusion, these mutants can help to further realize the large pool of disease susceptibilities linked with catalytic kinase domain activation of STK11 and assist to develop an effective drug for associated diseases.

Zaire ebolavirus (EBOV) is one of the dangerous and a negative-stranded ssRNA virus. EBOV is a zoonotic pathogen that causes severe hemorrhagic fever in humans. Nowadays epidemic outbreak caused by EBOV is incurable with present technologies; thus figure out as a major health risk which needs enhanced surveillance. The study was conducted with seven proteins of Zaire ebola (EBOV) and gene sequences are available in NCBI database. The homology modeling was done by SWISS-MODEL, Phyre2 and HHpred. The obtained model was verified with structure validation programs such as PROCHECK, Verify3D and ERRAT. PROCHECK analysis of seven proteins showed that 85-96.6% of the residues are in the most favored region, the verify 3D value of 80-100% indicates that constructed model is good and ERRAT value of 87.442-100% indicates that overall good quality factor. In this study, we also reported phylogenic relationship, physico-chemical characteristics, secondary structure,3-D structure. Moreover, active sites were identified by CASTp suggests that these proteins can be utilized as a potential drug target. Furthermore, the initial findings were reinforced by the results from I-Mutant and mCSM as these tools predicted significant and functional instability of the mutated vp35 protein. Int J Appl Sci Biotechnol, Vol 4(1): 92-103