Klebsiella aerogenes is a multidrug-resistant Gram-negative bacterium that causes nosocomial infections. The organism showed resistance to most of the conventional antibiotics available. Because of the ...
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Klebsiella aerogenes is a multidrug-resistant Gram-negative bacterium that causes nosocomial infections. The organism showed resistance to most of the conventional antibiotics available. Because of the high resistance of the species, the treatment of K. aerogenes is difficult. These species are resistant to third-generation cephalosporins due to the production of chromosomal beta-lactams with cephalosporin activity. The lack of better treatment and the development of therapeutic resistance in hospitals hinders better/new broad-spectrum-based treatment against this pathogen. This study identifies potential drug targets/vaccine candidates through a computational subtractive proteome-driven approach. This method is used to predict proteins that are not homologous to humans and human symbiotic intestinal flora. The resultant proteome of K. aerogenes was further searched for proteins, which are essential, virulent, and determinants of antibiotic/drug resistance. Subsequently, their druggability properties were also studied. The data set was reduced based on its presence in the pathogen-specific metabolic pathways. The subtractive proteome analysis predicted 13 proteins as potential drug targets for K. aerogenes. Furthermore, these target proteins were annotated based on their spectrum of activity, cellular localization, and antigenicity properties, which ensured that they are potent candidates for broad-spectrum antibiotic and vaccine design. The results open up new opportunities for designing and manufacturing powerful antigenic vaccines against K. aerogenes and the detection and release of new and active drugs against K. aerogenes without altering the gut microbiome.
Non-steroidal anti-inflammatory drugs (NSAIDs) often cause adverse effects, leading to increased interest in the identification of natural compounds with fewer side effects. This study reports the purification and cha...
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Non-steroidal anti-inflammatory drugs (NSAIDs) often cause adverse effects, leading to increased interest in the identification of natural compounds with fewer side effects. This study reports the purification and characterization of an alkylated flavonoid, Brevi-inflammin, a novel marine actinobacteria-derived compound that contains anti-inflammatory activity from Brevibacterium casei VRK 1. Using the one-pot synthesis method, the alkylated flavonoid compound Brevi-inflammin was synthesized and purified using semi-preparative reverse-phase HPLC. Further, it is characterized through multiple spectroscopic techniques, including UV–visible spectroscopy, FTIR, HPLC NMR, and UPLC-MS/MS. To assess the anti-inflammatory potential of Brevi-inflammin, the cytotoxic assessment and nitrous oxide inhibitory activity were performed. Further, molecular docking and molecular dynamics simulations were used to evaluate the binding affinity and stability of Brevi-inflammin with a COX-2. Spectroscopic results revealed Brevi-inflammin as an alkylated flavonoid compound, evidenced by its maximum absorption at 230 nm, characteristic aromatic ring structures (CH2 and CH3 stretching vibrations), and alkylation signature at 1652 cm−1. Additionally, HPLC results highlighted Brevi-inflammin as a flavonoid compound. Structural verification through NMR studies confirmed Brevi-inflammin as an alkylated flavonoid. UPLC-MS/MS results confirmed its molecular mass as 279.05 m/z. Cytotoxicity studies proved the compound's safety up to a concentration of 250 μg/mL. The inhibition of nitrous oxide production was observed at a concentration of 1000 µg/mL. Molecular dynamic simulations revealed strong binding interactions between Brevi-inflammin and COX-2, mediated through hydrogen bonds, hydrophobic interactions, and van der Waals forces. Combining experimental and computational approaches, this comprehensive characterization establishes that Brevi-inflammin could be a
Salmonella typhi are Gram-negative pathogen causes severe systemic infections like typhoid fever and gastrointestinal disease in humans. PgtE belongs to Omptin family, plays a central role at the host-pathogen interfa...
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Salmonella typhi is a Gram-negative pathogen that causes severe systemic infections like typhoid fever and gastrointestinal disease in humans. PgtE belongs to the Omptin family, and plays a central role at the host-pa...
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Salmonella typhi is a Gram-negative pathogen that causes severe systemic infections like typhoid fever and gastrointestinal disease in humans. PgtE belongs to the Omptin family, and plays a central role at the host-pathogen interface. In the present study, the S. typhi PgtE sequence was retrieved and the cleavage sites were predicted. Owing to the non-existence of a crystalline structure of S. typhi PgtE, theoretical 3D modeled structures were predicted using the I-TASSER server, which helps to understand the protease inhibitory mechanism. Molecular interaction studies with known protease inhibitors revealed that aspartic protease inhibitor Indinavir has the best interaction with S. typhi PgtE. From the metal ion docking studies, Mg 2+ has better interaction compared to the Zn 2+ and Cu 2+ ions. The multiple pathogen sequence alignment of the Omptin proteases family shows that interacting residues were conserved among the Omptins. These results will provide new knowledge for the development of novel therapeutic strategies against S. typhi PgtE and Omptin family proteases.
Current microarray datamining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In re...
Current microarray datamining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In recent years, there has been a growing interest in methods that attempt to discover patterns based on multiple but related data sources. Gene expression data and the corresponding literature data are one such example. This paper suggests a new approach to microarray datamining as a combination of textmining (TM) and information extraction (IE). TM is concerned with identifying patterns in natural language text and IE is concerned with locating specific entities, relations, and facts in text. The present paper surveys the state of the art of datamining methods for microarray data analysis. We show the limitations of current microarray datamining methods and outline how textmining could address these limitations.
MicroRNA expression profiles can improve classification, diagnosis, and prognostic information of malignancies, including lung cancer. In this paper, we undertook to develop a miRNA-mRNA network and uncover unique gro...
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HomoKinase database is a comprehensive collection of curated human protein kinases and their relevant biological information. The entries in the database are curated by three criteria: HGNC approval, gene ontology-bas...
HomoKinase database is a comprehensive collection of curated human protein kinases and their relevant biological information. The entries in the database are curated by three criteria: HGNC approval, gene ontology-based biological process (protein phosphorylation), and molecular function (ATP binding and kinase activity). For a given query protein kinase name, the database provides its official symbol, full name, other known aliases, amino acid sequences, functional domain, gene ontology, pathways assignments, and drug compounds. In addition, as a search tool, it enables the retrieval of similar protein kinases with specific family, subfamily, group, and domain combinations and tabulates the information. The present version contains 498 curated human protein kinases and links to other popular databases.
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