Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene-and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism.
Background The World Health Organization has recently declared a new coronavirus disease (COVID-19) a pandemic and a global health emergency. The pressure to produce drugs and vaccines against the ongoing pandemic has resulted in the use of some drugs such as azithromycin, chloroquine (sulfate and phosphate), hydroxychloroquine, dexamethasone, favipiravir, remdesivir, ribavirin, ivermectin, and lopinavir/ritonavir. However, reports from some of the clinical trials with these drugs have proved detrimental on some COVID-19 infected patients with side effects more of which cardiomyopathy, cardiotoxicity, nephrotoxicity, macular retinopathy, and hepatotoxicity have been recently reported. Realizing the need for potent and harmless therapeutic compounds to combat COVID-19, we attempted in this study to find promising therapeutic compounds against the imminent threat of this virus. In this current study, 16 derivatives of gallic acid were docked against five selected non-structural proteins of SARS-COV-2 known to be a good target for finding small molecule inhibitors against the virus, namely, nsp3, nsp5, nsp12, nsp13, and nsp14. All the protein crystal structures and 3D structures of the small molecules (16 gallic acid derivatives and 3 control drugs) were retrieved from the Protein database (PDB) and PubChem server respectively. The compounds with lower binding energy than the control drugs were selected and subjected to pharmacokinetics screening using AdmetSAR server. Results 4-O-(6-galloylglucoside) gave binding energy values of − 8.4, − 6.8, − 8.9, − 9.1, and − 7.5 kcal/mol against Mpro, nsp3, nsp12, nsp13, and nsp15 respectively. Based on the ADMET profile, 4-O-(6-galloylglucoside) was found to be metabolized by the liver and has a very high plasma protein binding. Conclusion The result of this study revealed that 4-O-(6-galloylglucoside) could be a promising inhibitor against these SAR-Cov-2 proteins. However, there is still a need for further molecular dynamic simulation, in vivo and in vitro studies to support these findings.
Non-specific lipid transfer proteins (nsLTPs) are cationic proteins involved in intracellular lipid shuttling in growth and reproduction, as well as in defense against pathogenic microbes. Even though the primary and spatial structures of some nsLTPs from different plants indicate their similar features, they exhibit distinct lipid-binding specificities signifying their various biological roles that dictate further structural study. The present study determined the complete amino acid sequence, in silico 3D structure modeling, and the antiproliferative activity of nsLTP1 from fennel (Foeniculum vulgare) seeds. Fennel is a member of the family Umbelliferae (Apiaceae) native to southern Europe and the Mediterranean region. It is used as a spice medicine and fresh vegetable. Fennel nsLTP1 was purified using the combination of gel filtration and reverse-phase high-performance liquid chromatography (RP-HPLC). Its homogeneity was determined by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry. The purified nsLTP1 was treated with 4-vinyl pyridine, and the modified protein was then digested with trypsin. The complete amino acid sequence of nsLTP1 established by intact protein sequence up to 28 residues, overlapping tryptic peptides, and cyanogen bromide (CNBr) peptides. Hence, it is confirmed that fennel nsLTP1 is a 9433 Da single polypeptide chain consisting of 91 amino acids with eight conserved cysteines. Moreover, the 3D structure is predicted to have four α-helices interlinked by three loops and a long C-terminal tail. The lipid-binding property of fennel nsLTP1 is examined in vitro using fluorescent 2-p-toluidinonaphthalene-6-sulfonate (TNS) and validated using a molecular docking study with AutoDock Vina. Both of the binding studies confirmed the order of binding efficiency among the four studied fatty acids linoleic acid > linolenic acid > Stearic acid > Palmitic acid. A preliminary screening of fennel nsLTP1 suppressed the growth of MCF-7 human breast cancer cells in a dose-dependent manner with an IC50 value of 6.98 µM after 48 h treatment.
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