The structural characterization and bioactivity assessment of nonspecific lipid transfer protein 1 (nsLTP1) from caraway (Carum carvi) seeds.

BACKGROUND: Carum carvi (caraway) of the Apiaceae family has been used in many cultures as a cooking spice and part of the folk medicine. Previous reports primarily focus on the medicinal properties of caraway seed essential oil and the whole seeds extract. However, no effort has been made to study caraway proteins and their potential pharmacological properties, including nonspecific lipid transfer protein (nsLTP), necessitating further research. The current study aimed to characterize nonspecific lipid transfer protein 1 (nsLTP1) from caraway seed, determine its three-dimensional structure, and analyze protein-ligand complex interactions through docking studies. We also evaluated nsLTP1 in vitro cytotoxic effect and antioxidant capacity. Additionally, nsLTP1 thermal- and pH- stability were investigated. METHODS: Caraway nsLTP1 was purified using two-dimensional chromatography. The complete amino acid sequence of nsLTP1 was achieved by intact protein sequence for the first 20 residues and the overlapping digested peptides. The three-dimensional structure was predicted using MODELLER. Autodock Vina software was employed for docking fatty acids against caraway nsLTP1. Assessment of nsLTP1 cytotoxic activity was achieved by MTS assay, and the Trolox equivalent antioxidant capacity (TAC) was determined. Thermal and pH stability of the nsLTP1 was examined by circular dichroism (CD) spectroscopy. RESULTS: Caraway nsLTP1 is composed of 91 residues and weighs 9652 Da. The three-dimensional structure of caraway nsLTP1 sequence was constructed based on searching known structures in the PDB. We chose nsLTP of Solanum melongena (PDB ID: 5TVI) as the modeling template with the highest identity among all other homologous proteins. Docking linolenic acid with caraway protein showed a maximum binding score of -3.6 kcal/mol. A preliminary screening of caraway nsLTP1 suppressed the proliferation of human breast cancer cell lines MDA-MB-231 and MCF-7 in a dose­dependent manner with an IC50 value of 52.93 and 44.76 µM, respectively. Also, nsLTP1 (41.4 µM) showed TAC up to 750.4 µM Trolox equivalent. Assessment of nsLTP1 demonstrated high thermal/pH stability. CONCLUSION: To the best of our knowledge, this is the first study carried out on nsLTP1 from caraway seeds. We hereby report the sequence of nsLTP1 from caraway seeds and its possible interaction with respective fatty acids using in silico approach. Our data indicated that the protein had anticancer and antioxidant activities and was thermally stable.

Evaluation of anti-EGFR potential of quinazoline derivatives using molecular docking: An in silico approach.

Overexpression of epidermal growth factor receptor (EGFR) is commonly reported in epithelial malignancies such as oral squamous cell carcinoma. Inhibition of EGFR is, therefore, considered a potential therapeutic strategy. Among various anti-EGFR drugs, quinazoline-based tyrosine kinase inhibitors (TKIs) have gained increasing attention. Present study focused to investigate anti-EGFR potential of quinazoline-based compounds using in silico approach. Two widely used docking programs GOLD and AutoDock Vina were used for the study. Four drugs were docked on the X-ray crystallographic EGFR structure (1XKK). GOLD and AutoDock Vina produced results in terms of fitness score and binding affinity, respectively. GOLD prioritized varlitinib and AutoDock Vina preferred imatinib over other drugs. To reach the consensus from both software, all four drugs coupled with EGFR were studied rigorously. GOLD demonstrated varlitinib to be the best inhibitor with highest fitness score of 109, whereas AutoDock Vina revealed imatinib as the potent ligand with least binding energy of -10.9 kcal/mol. Most stable hydrogen bonds observed by GOLD and maximum number of hydrophobic contacts along with strong ionic interaction exhibited by varlitinib through both software have led us to conclude varlitinib as the most potent EGFR inhibitor in the studied group.

Molecular docking studies of some selected gallic acid derivatives against five non-structural proteins of novel coronavirus.

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.

Structural characterization and in vitro lipid binding studies of non-specific lipid transfer protein 1 (nsLTP1) from fennel (Foeniculum vulgare) seeds.

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.

Genome Subtraction and Comparison for the Identification of Novel Drug Targets against Mycobacterium avium subsp. hominissuis.

Mycobacterium avium complex (MAC) is a major cause of non-tuberculous pulmonary and disseminated diseases worldwide, inducing bronchiectasis, and affects HIV and immunocompromised patients. In MAC, Mycobacterium avium subsp. hominissuis is a pathogen that infects humans and mammals, and that is why it is a focus of this study. It is crucial to find essential drug targets to eradicate the infections caused by these virulent microorganisms. The application of bioinformatics and proteomics has made a significant impact on discovering unique drug targets against the deadly pathogens. One successful bioinformatics methodology is the use of in silico subtractive genomics. In this study, the aim was to identify the unique, non-host and essential protein-based drug targets of Mycobacterium avium subsp. hominissuis via in silico a subtractive genomics approach. Therefore, an in silico subtractive genomics approach was applied in which complete proteome is subtracted systematically to shortlist potential drug targets. For this, the complete dataset of proteins of Mycobacterium avium subsp. hominissuis was retrieved. The applied subtractive genomics method, which involves the homology search between the host and the pathogen to subtract the non-druggable proteins, resulted in the identification of a few prioritized potential drug targets against the three strains of M. avium subsp. Hominissuis, i.e., MAH-TH135, OCU466 and A5. In conclusion, the current study resulted in the prioritization of vital drug targets, which opens future avenues to perform structural as well as biochemical studies on predicted drug targets against M. avium subsp. hominissuis.

Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale.

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.