In silico identification of common putative drug targets in Leptospira interrogans

Abstract

Infectious diseases are the leading causes of death worldwide. Hence, there is a need to develop new antimicrobial agents. Traditional method of drug discovery is time consuming and yields a few drug targets with little intracellular information for guiding target selection. Thus, focus in drug development has been shifted to computational comparative genomics for identifying novel drug targets. Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. Availability of L. interrogans serovars and human genome sequences facilitated to search for novel drug targets using bioinformatics tools. The genome sequence of L. interrogans serovar Copenhageni has 5,124 genes while that of serovar Lai has 4,727 genes. Through subtractive genomic approach 218 genes in serovar Copenhageni and 158 genes in serovar Lai have been identified as putative drug targets. Comparative genomic approach had revealed that 88 drug targets were common to both the serovars. Pathway analysis using the Kyoto Encyclopaedia of Genes and Genomes revealed that 66 targets are enzymes and 22 are non-enzymes. Sixty two common drug targets were predicted to be localized in cytoplasm and 16 were surface proteins. The identified potential drug targets form a platform for further investigation in discovery of novel therapeutic compounds against Leptospira.

Computational Identification and Screening of Natural Compounds as Drug Targets against the Fish Pathogen, Pseudomonas fluorescens

“…The average amino acid length in the identified essential proteins was 508 and the minimum similarity 45% or more with genes in DEG database. Similar results were observed in other studies viz., Sakharkar et al, (2004) reported 306 essential genes in Pseudomonas aeruginosa, Chong et al, (2006) identified 312 in Burkholderia pseudomallei, Amineni et al, (2010) identified 576 in Leptospira interrogans, Butt et al, (2012) identified 424 in Mycobacterium ulcerans, Murali et al, (2013) identified 238 in Flavobacterium columnare and 32 proteins in Fusobacterium nucleatum were identified by Habib et al, (2016). The number of essential proteins or gene products identified in the present study was also found similar to those determined through experimental techniques in Escherichia coli (150 essential genes) (Jordan et al, 2002) and Bacillus subtilis (203 essential genes) (Peters et al, 2016) etc.…”

In Silico Identification and Characterization of Potent Drug Targets in Fungal Pathogen causing Keratitis

“…2). The web server classifies 88 proteins as transmembrane proteins (27), lipid-binding proteins (13), zinc-binding proteins(13), ironbinding proteins (12), electrochemical potential driven transporter proteins (10), DNA-binding proteins (1), repressorproteins (1), metal-binding proteins(1), transferases (2), hydrolases (1),G protein coupled receptor (1), repressor (1), lyases (1), oxidoreductase (1), structural proteins(1) and two proteins classified with very low p value (58.6%) among which one protein involved in photosystem 1 other in calcium, magnesium-binding. Metabolic pathway analysis of these 136 non-human homologous essential proteins revealed that 91 proteins are involved in metabolic pathway, among these 4 pathways were unique to pathogen these unique pathways were Methane metabolism, Styrene metabolism, Carbon fixation and photosynthetic organisms and two component system.…”

“…These non-homologous essential genes ensure the survival of the pathogen and therefore can be targeted for drug development [7]. This subtractive genomics approach has been successfully used to identify novel drug targets in several pathogens such as Pseudomonas aeruginosa [8], Helicobacter pylori [9], Brugiamalayi [10], Campylobacter fetus [11], Leptospira interrogans [12] and Mycobacterium ulceran [13]. The current study on Aspergillus terreus is based on proteome subtraction approach.…”

Computational Methods to Identify Drug Target in Multidrug Resistant Human Pathogens Klebsiella pneumonia, Vibrio Cholerae and Yersinia Pestis

“…Trending application of bioinformatics expertise has immensely accelerated the standard procedure of drug design [9]. In silico drug target mining lays the groundwork in this regard and has been favoured in numerous researches concerning pathogenic bacteria [10]. CADD is a whole genome comparative approach in which microbial genome is extensively traversed through a series of bioinformatics tools in order to mine therapeutically important, unique, druggable targets [11].…”