Elucidating isoniazid resistance using molecular
Because elucidating the effects of mutations experimentally is expensive and time consuming, many efforts have been made to develop computational methods that can predict the effects of mutations.
Extended treatment is required with multiple drugs that have a higher rate of side effects but limited rate of treatment success (Gygli et al.).Therefore, studies are needed to understand the epidemiology and generate a comprehensive list of resistance-conferring mutations. tuberculosis that are associated with drug resistance result in the alteration of the phenotype due to changes in drug-bacterial interactions, including protein stability and/or structural changes that interfere with the mechanism of drug action.Since then the effects of mutations have been reported for isolates representing the global diversity arising from four lineages of M. Detailed insights into mechanisms of drug-resistance mutations can help in the design of new and improved existing drugs, the selection of improved drug targets and even identification of new drug targets.Previous studies have reported numerous mutations that confer resistance to anti-TB drugs, but there has been little systematic analysis to understand their genetic background and the potential impacts on the drug target stability and/or interactions.
Here, we report the analysis of whole-genome sequence data for 98 clinical M.SDM uses a statistical approach to predict the effects of mutations on protein stability and is based on the analysis of naturally occurring amino-acid substitutions expressed in the form of environment-specific substitution tables (ESSTs).