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An in silico approach towards identification of novel drug targets in Klebsiella oxytoca
- Source :
- Informatics in Medicine Unlocked, Vol 31, Iss , Pp 100998- (2022)
- Publication Year :
- 2022
- Publisher :
- Elsevier, 2022.
-
Abstract
- The prolonged use, incorrect diagnosis, unnecessary prescription, improper dosing over the year has transformed klebsiella organisms into resistant to antibiotics. The emergence of resistance to many antibiotics and drugs makes treatment options limited. Now, the antibiotic pipeline has become severely dry. So, there is a pressing necessity for a new drug against Klebsiella oxytoca infections as well as the identification of drug targets. In this study, a systematic proteome subtractive method is used to screen out the most potential drug targets of Klebsiella oxytoca that might facilitate the discovery of putative drug in the near future. The comparative proteome analysis of host and pathogen was made to identify the non-homologous proteins which showed no similarity with human host proteins. KEGG pathway analysis was made to identify common and unique metabolic pathways. A computational analysis was carried out to list out the indispensable non-homologous proteins of the pathogen. Essential proteins were predicted by the analysis of protein-protein interactions networks to reveal the proteins which are exigent for the survival of the pathogen, and these proteins also have a lethal effect when removed from the pathogen. In this study, 43 essential proteins were identified. To predict subcellular localization, CELLO (version 2.5) and PSORTb (version 3.00) tools were used. The druggability of proteins was predicted using the DrugBank database. Besides, the physiochemical properties of proteins were analyzed using the Protparam tool of ExPASy. After physiochemical properties analysis and the based on 3D structure availability in the Protein Data Bank, the homology model was built for only one influential drug target using MODELLER. In the end, molecular docking study was carried out to investigate the protein interactions with five different drugs.
Details
- Language :
- English
- ISSN :
- 23529148
- Volume :
- 31
- Issue :
- 100998-
- Database :
- Directory of Open Access Journals
- Journal :
- Informatics in Medicine Unlocked
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.70dd10c522f44c7db071369c06a45772
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.imu.2022.100998