Proteome scale comparative modeling for conserved drug and vaccine targets identification in Corynebacterium pseudotuberculosis

Abstract

Corynebacterium pseudotuberculosis (Cp) is a pathogenic bacterium that causes caseous lymphadenitis (CLA), ulcerative lymphangitis, mastitis, and edematous to a broad spectrum of hosts, including ruminants, thereby threatening economic and dairy industries worldwide. Currently there is no effective drug or vaccine available against Cp. To identify new targets, we adopted a novel integrative strategy, which began with the prediction of the modelome (tridimensional protein structures for the proteome of an organism, generated through comparative modeling) for 15 previously sequenced C. pseudotuberculosis strains. This pan-modelomics approach identified a set of 331 conserved proteins having 95-100% intra-species sequence similarity. Next, we combined subtractive proteomics and modelomics to reveal a set of 10 Cp proteins, which may be essential for the bacteria. Of these, 4 proteins (tcsR, mtrA, nrdI, and ispH) were essential and non-host homologs (considering man, horse, cow and sheep as hosts) and satisfied all criteria of being putative targets. Additionally, we subjected these 4 proteins to virtual screening of a drug-like compound library. In all cases, molecules predicted to form favorable interactions and which showed high complementarity to the target were found among the top ranking compounds. The remaining 6 essential proteins (adk, gapA, glyA, fumC, gnd, and aspA) have homologs in the host proteomes. Their active site cavities were compared to the respective cavities in host proteins. We propose that some of these proteins can be selectively targeted using structure-based drug design approaches (SBDD). Our results facilitate the selection of C. pseudotuberculosis putative proteins for developing broad-spectrum novel drugs and vaccines. A few of the targets identified here have been validated in other microorganisms, suggesting that our modelome strategy is effective and can also be applicable to other pathogens.

Figure 1

High-throughputness (efficiency) of the MHOLline biological workflow for genome-scale modelome (3D models) prediction. Predicted proteomes from the genomes of 15 C. pseudotuberculosis strains were fed to the MHOLline workflow in FASTA format. The blue line represents the number of input data, according to the left-hand side y-axis. The bars show the number in the form of MHOLline output data (according to the right-hand side y-axis) of: not aligned sequences (G0, green bars); sequences for which there is a template structure available at RCSB PDB (yellow bars); sequences with acceptable template structures that where modeled in the MHOLline workflow (G2, red bars); sequences with predicted transmembrane regions (HMMTOP, purple bars) and the number of sequences that were predicted as enzymes in each genome and were assigned an EC number (ECNGet, gray bars). The x-axis represents the C. pseudotuberculosis genomes used in this study.

Figure 2

Overview of different computational steps employed in the identification of putative essential targets (non-host homologous and host homologous) for drugs and vaccines from the core-proteome of 15 C. pseudotuberculosis strains. Figure 2b. Intra-species subtractive modelomics workflow for conserved targets identification in C. pseudo tuberculosis species. The table (from left to right) represents the total number of protein sequences as an input data in fasta format fed to the MHOLline workflow (upper forward arrow). The remaining columns show the output data of group G2 (upper backward arrow), first by BATS and then by Filter tools of the MHOLline workflow respectively. Columns 4^th-7^th constitute the number of protein sequences of different qualities of all 15 Cp strains, where the sequences of 14 Cp strains were compared using BLASTp, to the sequences of Cp1002 strain as reference, for the identification of conserved protein targets (core-modelome). The funnel shows how this workflow processes and filters a large quantity of genomic data for putative drug and vaccine targets identification of a pathogen.