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Brucella Miletensis: Identification and Characterization of Potential Drug Targets presents a systematic approach to identifying and characterizing drug targets using bioinformatics. The book shows the potential of bioinformatic tools in the identification of virulence targets in pathogenic bacteria and viruses, in general, and in B. militensis 16M in particular. Chapters identify putative genes as potential drug targets, employ a subtractive genomic approach, consider the virulent genes of this bacteria that negatively affects humans, list twelve potential virulence genes as drug targets, and consider the screening of potential drugs against the bacteria's virulence genes through molecular modeling, computational screening, drug discovery and molecular docking studies.
In addition, the book demonstrates in silico approaches that offer insights into the identification of drug targets in B.melitensis 16M. The title employs a step-by-step approach to understanding drug targets by identifying and characterizing vaccine targets for Brucella melitensis, in silico screening, and the identification of novel drug targets from the total Brucella melitensis proteome. Other sections cover computational modeling and evaluation of the best potential drug targets through comparative modeling, molecular docking, and dynamics simulations of novel drug targets and in silico validation and ADMET analysis for best lead molecules.
In addition, the book demonstrates in silico approaches that offer insights into the identification of drug targets in B.melitensis 16M. The title employs a step-by-step approach to understanding drug targets by identifying and characterizing vaccine targets for Brucella melitensis, in silico screening, and the identification of novel drug targets from the total Brucella melitensis proteome. Other sections cover computational modeling and evaluation of the best potential drug targets through comparative modeling, molecular docking, and dynamics simulations of novel drug targets and in silico validation and ADMET analysis for best lead molecules.