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

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.

Fig 1. Overview of different computational steps employed for the identification of putative essential targets (non-host homologous and host homologous) from the core-proteome of 13 C. diphtheriae strains.

Fig 2. Intra-species subtractive modelomics workflow for conserved target identification in C. diphtheriae species.

The table represents the total number of protein sequences as an input data fed to the MHOLline workflow (upper red arrow). The blue arrow represents the core genes of thirteen Cd strains. The rectangular boxes show how this workflow processes and filters a large quantity of genomic data for putative drug and vaccine target identification of a pathogen.

Fig 3. Efficiency of the MHOLline biological workflow for genome-scale modelome (3D models) prediction.

Predicted proteomes from the genomes of 13 C. diphtheriae strains were fed to the MHOLline workflow in FASTA format. The grey bars represent the number of input data. The remaining bars (MHOLline output data) show the number of not aligned sequences (G0, green bars), sequences for which there is a template structure available at RCSB PDB (blue bars), and sequences with acceptable template structures that were modeled in the MHOLline workflow (G2, red bars).

Fig 4. Superposition of co-crystallized and Docked ligand; Dark Khaki represents the co crystallized ligand and Dark Cyan the re-docked conformation of the ligand.

Fig 5

A-I: 3D cartoon representation of the docking analyses for the most druggable protein cavity of NP_939302.1 (glpX, Fructose 1,6-bisphosphatase II) with Jacarandic Acid (CID 73645). A-II: 3D surface representation of the docking analyses for the structures of Jacarandic Acid with glpX protein. Figs B-I, II, C-I, II & D-I, II represent same information for compounds 16-hydrazonisosteviol, ZINC13142972 and ZINC67912153 respectively, for the same protein cavity.

Fig 6

A-I: 3D cartoon representation of the docking analyses for the most druggable protein cavity of NP_939692.1 (nusB, Transcription antitermination protein NusB) with Jacarandic Acid (CID 73645). A-II: 3D surface representation of the docking analyses for the structures of Jacarandic Acid with nusB protein. Figs B-I, II, C-I, II and D-I, II represent same information for compounds 16-hydrazonisosteviol, ZINC00053531 and ZINC15043210 respectively, for the same protein cavity.

Fig 7

A-I 3D cartoon representation of the docking analyses for the most druggable protein cavity of NP_938900.1 (rpsH, 30S ribosomal protein S8) with Jacarandic Acid (CID 73645). A-II: 3D surface representation of the docking analyses for the structures of Jacarandic Acid with rpsH protein. Figs B-I, II, C-I, II and D-I, II represent same information for compounds 17-hydroxyisosteviol ZINC15221730 and ZINC35457686 respectively, for the same cavity.

Fig 8

A-I 3D cartoon representation of the docking analyses for the most druggable protein cavity of NP_938502.1 (bioB, Biotin synthase) with Rhein (CID 10168). A-II: 3D surface representation of the docking analyses for the structure of Rhein with bioB protein. Figs B-I, II, C-I, II & D-I, II represent same information for compounds 16-oxime, 17-hydroxyisosteviol, ZINC16952914 and ZINC77269615 respectively, for the same protein cavity.

Fig 9

A-1 3D cartoon representation of the docking analyses for the most druggable protein cavity of NP_939612.1 (hisE, Phosphoribosyl-ATP pyrophosphatase) with Jacarandic Acid (CID 73645). A-II: 3D surface representation of the docking analyses for the structure of Jacarandic Acid with hisE protein. Figs B-I, II, C-I, II & D-I, II represent same information for compounds 16–17 dihydroxyisosteviol, ZINC05809437 and ZINC67913372 respectively, for the same protein cavity.

Fig 10

A-I 3D cartoon representation of the docking analyses for the most druggable protein cavity of NP_939123.1 (smpB, SsrA-binding protein) with Rhein (CID 10168). A-II: 3D surface representation of the docking analyses for the structure of Rhein with smpB protein. Figs B-I, II, C-I, II & D-I, II represent same information for compounds 16-hydroxyisosteviol ZINC01414475 & ZINC31168211 respectively, for the same protein cavity.

Fig 11

A-I 3D cartoon representation of the docking analyses for the most druggable protein cavity of NP_939445.1 (DIP1084, Putative iron transport membrane protein, FecCD-family) with Jacarandic Acid (CID 73645). A-II: 3D surface representation of the docking analyses for the structure of Jacarandic Acid with DIP1084, Putative iron transport membrane protein. Figs B-I, II, C-I, II & D-1, II D represent same information for compounds 16-hydrazonisosteviol ZINC13142972 and ZINC70454922 respectively, for the same protein cavity.

Fig 12

A-1: 3D cartoon representation of the docking analyses for the most druggable protein cavity of NP_939345.1 (DIP0983, Hypothetical protein DIP0983) with Jacarandic Acid (CID 73645). A-II: 3D surface representation of the docking analyses for the structure of Jacarandic Acid with Hypothetical protein DIP0983. Figs B-I, II, C-I, II & D-I, II represent same information for compounds 17-hydroxyisosteviol, ZINC00211173 and ZINC67911471 respectively, for the same protein cavity.