Resistance related metabolic pathways for drug target identification in Mycobacterium tuberculosis

BMC Bioinformatics

Research Article

Wolf-Dieter Schubert¹  Ekow Oppon²  Ruben Cloete²  Alan Christoffels²  Edwin Murungi³ 

[1] Department of Biotechnology, University of the Western Cape, Bellville, South Africa;Current address: Department of Biochemistry, University of Pretoria, Pretoria, South Africa;South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa;South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa;Current address: Department of Biochemistry, Egerton University, Njoro, Kenya;

关键词: Root Mean Square Deviation;    Multiple Drug Resistant;    Potential Drug Target;    Drug Resistance Gene;    Polyphosphate Kinase;   

DOI  :  10.1186/s12859-016-0898-8

 received in 2015-07-20, accepted in 2016-01-20,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundIncreasing resistance to anti-tuberculosis drugs has driven the need for developing new drugs. Resources such as the tropical disease research (TDR) target database and AssessDrugTarget can help to prioritize putative drug targets. Hower, these resources do not necessarily map to metabolic pathways and the targets are not involved in dormancy. In this study, we specifically identify drug resistance pathways to allow known drug resistant mutations in one target to be offset by inhibiting another enzyme of the same metabolic pathway. One of the putative targets, Rv1712, was analysed by modelling its three dimensional structure and docking potential inhibitors.ResultsWe mapped 18 TB drug resistance gene products to 15 metabolic pathways critical for mycobacterial growth and latent TB by screening publicly available microarray data. Nine putative targets, Rv1712, Rv2984, Rv2194, Rv1311, Rv1305, Rv2195, Rv1622c, Rv1456c and Rv2421c, were found to be essential, to lack a close human homolog, and to share >67 % sequence identity and >87 % query coverage with mycobacterial orthologs. A structural model was generated for Rv1712, subjected to molecular dynamic simulation, and identified 10 compounds with affinities better than that for the ligand cytidine-5′-monophosphate (C5P). Each compound formed more interactions with the protein than C5P.ConclusionsWe focused on metabolic pathways associated with bacterial drug resistance and proteins unique to pathogenic bacteria to identify novel putative drug targets. The ten compounds identified in this study should be considered for experimental studies to validate their potential as inhibitors of Rv1712.

【 授权许可】

CC BY   
© Cloete et al. 2016