Subtractive sequence analysis aided druggable targets mining in Burkholderia cepacia complex and finding inhibitors through bioinformatics approach.

Burkholderia cepacia complex (BCC) is a group of gram-negative bacteria composed of at least 20 different species that cause diseases in plants, animals as well as humans (cystic fibrosis and airway infection). Here, we analyzed the proteomic data of 47 BCC strains by classifying them in three groups. Phylogenetic analyses were performed followed by individual core region identification for each group. Comparative analysis of the three individual core protein fractions resulted in 1766 ortholog/proteins. Non-human homologous proteins from the core region gave 1680 proteins. Essential protein analyses reduced the target list to 37 proteins, which were further compared to a closely related out-group, Burkholderia gladioli ATCC 10,248 strain, resulting in 21 proteins. 3D structure modeling, validation, and druggability step gave six targets that were subjected to further target prioritization parameters which ultimately resulted in two BCC targets. A library of 12,000 ZINC drug-like compounds was screened, where only the top hits were selected for docking orientations. These included ZINC01405842 (against Chorismate synthase aroC) and ZINC06055530 (against Bifunctional N-acetylglucosamine-1-phosphate uridyltransferase/Glucosamine-1-phosphate acetyltransferase glmU). Finally, dynamics simulation (200 ns) was performed for each ligand-receptor complex, followed by ADMET profiling. Of these targets, details of their applicability as drug targets have not yet been elucidated experimentally, hence making our predictions novel and it is suggested that further wet-lab experimentations should be conducted to test the identified BCC targets and ZINC scaffolds to inhibit them.

The Druggable Pocketome of Corynebacterium diphtheriae: A New Approach for in silico Putative Druggable Targets.

Diphtheria is an acute and highly infectious disease, previously regarded as endemic in nature but vaccine-preventable, is caused by Corynebacterium diphtheriae (Cd). In this work, we used an in silico approach along the 13 complete genome sequences of C. diphtheriae followed by a computational assessment of structural information of the binding sites to characterize the "pocketome druggability." To this end, we first computed the "modelome" (3D structures of a complete genome) of a randomly selected reference strain Cd NCTC13129; that had 13,763 open reading frames (ORFs) and resulted in 1,253 (∼9%) structure models. The amino acid sequences of these modeled structures were compared with the remaining 12 genomes and consequently, 438 conserved protein sequences were obtained. The RCSB-PDB database was consulted to check the template structures for these conserved proteins and as a result, 401 adequate 3D models were obtained. We subsequently predicted the protein pockets for the obtained set of models and kept only the conserved pockets that had highly druggable (HD) values (137 across all strains). Later, an off-target host homology analyses was performed considering the human proteome using NCBI database. Furthermore, the gene essentiality analysis was carried out that gave a final set of 10-conserved targets possessing highly druggable protein pockets. To check the target identification robustness of the pipeline used in this work, we crosschecked the final target list with another in-house target identification approach for C. diphtheriae thereby obtaining three common targets, these were; hisE-phosphoribosyl-ATP pyrophosphatase, glpX-fructose 1,6-bisphosphatase II, and rpsH-30S ribosomal protein S8. Our predicted results suggest that the in silico approach used could potentially aid in experimental polypharmacological target determination in C. diphtheriae and other pathogens, thereby, might complement the existing and new drug-discovery pipelines.