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PLoS One. 2017 Oct 19;12(10):e0186401. doi: 10.1371/journal.pone.0186401. eCollection 2017.

An integrative in-silico approach for therapeutic target identification in the human pathogen Corynebacterium diphtheriae.

Author information

1
PG program in Bioinformatics (LGCM), Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.
2
Department of Chemistry, Islamia College University Peshawar, KPK, Pakistan.
3
Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón II, Buenos Aires, Argentina.
4
Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology, Nonakuri, Purba Medinipur, West Bengal, India.
5
Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States of America.
6
Institute of Biologic Sciences, Federal University of Para, Belém, PA, Brazil.
7
Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
8
Department of General Biology (LGCM), Institute of Biologic Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.

Abstract

Corynebacterium diphtheriae (Cd) is a Gram-positive human pathogen responsible for diphtheria infection and once regarded for high mortalities worldwide. The fatality gradually decreased with improved living standards and further alleviated when many immunization programs were introduced. However, numerous drug-resistant strains emerged recently that consequently decreased the efficacy of current therapeutics and vaccines, thereby obliging the scientific community to start investigating new therapeutic targets in pathogenic microorganisms. In this study, our contributions include the prediction of modelome of 13 C. diphtheriae strains, using the MHOLline workflow. A set of 463 conserved proteins were identified by combining the results of pangenomics based core-genome and core-modelome analyses. Further, using subtractive proteomics and modelomics approaches for target identification, a set of 23 proteins was selected as essential for the bacteria. Considering human as a host, eight of these proteins (glpX, nusB, rpsH, hisE, smpB, bioB, DIP1084, and DIP0983) were considered as essential and non-host homologs, and have been subjected to virtual screening using four different compound libraries (extracted from the ZINC database, plant-derived natural compounds and Di-terpenoid Iso-steviol derivatives). The proposed ligand molecules showed favorable interactions, lowered energy values and high complementarity with the predicted targets. Our proposed approach expedites the selection of C. diphtheriae putative proteins for broad-spectrum development of novel drugs and vaccines, owing to the fact that some of these targets have already been identified and validated in other organisms.

PMID:
29049350
PMCID:
PMC5648181
DOI:
10.1371/journal.pone.0186401
[Indexed for MEDLINE]
Free PMC Article

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