Characterization and profiling of multidrug resistant tuberculosis A bioinformatics study

Abstract

Multidrug resistant TB MDR TB has evolved into a global health crisis This dissertation is a compilation of in silico studies aimed at characterizing MDR TB strains and identifying novel drug targets and drug molecules for MDR TB We developed data table of whole genome sequences of MDR TB strains available in public domain Phylogenetic analysis was performed to root the evolutionary link between the genomes Using the M tuberculosis strain CAS NITR204 from south India as template we analyzed the Hypothetical proteins that could possibly have a role in bacterial virulence Around 33 proteins identified as potential vaccine candidates as they contained highly immunogenic epitopes The 3D structure of 4 proteins was modeled using I TASSER Ab Initio method WhiB6 a transcriptional regulator protein was identified as highly potential drug target and was docked with a library of 173 Phytochemicals with potential anti tuberculosis activity to identify novel lead molecules for drug development UDP galactopyranose and GDPL galactose were identified as potent lead molecules Further a multilayer perceptron neural network model built to differentiate pulmonary tuberculosis from Sarcoidosis a condition resembles pulmonary TB and poses a diagnostic challenge This model was extended to distinguish between MDR TB and drug sensitive TB using signal intensity data from blood transcriptional microarrays The neural network model was found to be capable of distinguishing between PTB and Sarcoidosis with 95 point 8 percent accuracy and between MDR TB and non MDR TB with 90 point zero percent accuracy The in silico results may be further validated using laboratory experimentsand subsequently recommended for novel drug discovery vaccine development and future applications newline newline newline newline