Abstract
SummaryThe identification and validation of a small molecule’s targets is a major bottleneck in the discovery process for tuberculosis antibiotics. Activity-based protein profiling (ABPP) is an efficient tool for determining a small molecule’s targets within complex proteomes. However, how target inhibition relates to biological activity is often left unexplored. Here, we study the effects of 1,2,3-triazole ureas on Mycobacterium tuberculosis (Mtb). After screening ∼200 compounds, we focus on 4 compounds that form a structure-activity series. The compound with negligible activity reveals targets, the inhibition of which is functionally less relevant for Mtb growth and viability, an aspect not addressed in other ABPP studies. Biochemistry, computational docking, and morphological analysis confirms that active compounds preferentially inhibit serine hydrolases with cell wall and lipid metabolism functions and that disruption of the cell wall underlies biological activity. Our findings show that ABPP identifies the targets most likely relevant to a compound's antibacterial activity.
Highlights
The rising incidence of antibiotic resistance in the causative bacterium Mycobacterium tuberculosis (Mtb) makes the need to develop novel tuberculosis therapies ever more urgent
Triazole urea compounds inhibit the growth of Mtb on glycerol and on cholesterol To assess the activity of 1,2,3-triazole ureas against Mtb, we screened a library of 192 compounds (Adibekian et al, 2011)
Similar to Ortega et al (2016) we found that the detected serine hydrolase (SH) proteome is enriched relative to the Mtb genome in the functional categories of lipid metabolism (16% versus 6%) and intermediate metabolism and respiration (46% versus 22%)
Summary
The rising incidence of antibiotic resistance in the causative bacterium Mycobacterium tuberculosis (Mtb) makes the need to develop novel tuberculosis therapies ever more urgent. Target validation has depended largely on high-throughput genetic methods, such as generating spontaneous mutations (Andries et al, 2005; Christophe et al, 2009; Grzegorzewicz et al, 2012; Makarov et al, 2009; Pethe et al, 2013; Remuina ́ n et al, 2013; Stanley et al, 2013; Stover et al, 2000) and over- or underexpressing putative targets (Abrahams et al, 2012; Evans and Mizrahi, 2015; Johnson et al, 2019; Krieger et al, 2012; Wei et al, 2011) This method is less revealing when multiple targets underlie biological activity. There is a need for a method that detects all potential targets simultaneously and thereby provides a comprehensive and accurate assessment of an inhibitor’s mode of action
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
