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.

Automated summary

In this study, potential drug targets for Burkholderia cepacia complex (BCC) were identified through the analysis of proteomic data. Two potential BCC targets were found using computational modeling and library screening techniques. These findings are of great significance for the development of novel therapies for BCC infections.