Prioritizing genomic drug targets in pathogens: application to Mycobacterium tuberculosis - PubMed (original) (raw)

Prioritizing genomic drug targets in pathogens: application to Mycobacterium tuberculosis

Samiul Hasan et al. PLoS Comput Biol. 2006.

Abstract

We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this software to produce three prioritized drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately needed. Each list is based on an individual criterion. The first list prioritizes metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome ("metabolic chokepoints") and their similarity to known "druggable" protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant state referred to as "persistence." The final list aims to prioritize potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current, candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritizing studied targets. It can also be shown that targets can be prioritized by using evolutionary programming to optimize the weights of each desired property. We demonstrate this approach in prioritizing persistence targets.

PubMed Disclaimer

Conflict of interest statement

Competing interests. The authors have declared that no competing interests exist.

Figures

Figure 1

Figure 1. Overlap of M. tuberculosis Growth-Essential Genes

Figure 2

Figure 2. Box and Whisker Plots of GA-Optimized Weights from 100 Evolved Solutions

Each possible solution was able to rank eight of ten target genes within the top 25%. M0, macrophage; n, number of experiments; nrp, nonreplicating persistence.

Similar articles

Cited by

References

    1. Humer F. Innovation in the Pharmaceutical Industry—Future Prospects. 2005. Available: http://www.roche.com/fbh_zvg05_e.pdf. Accessed 12 May 2006.
    1. Terstappen GC, Reggiani A. In silico research in drug discovery. Trends Pharmacol Sci. 2001;22:23–26. - PubMed
    1. Freiberg C. Novel computational methods in anti-microbial target identification. Drug Discovery Today. 2001;6:S72–S80.
    1. Wang S, Sim TB, Kim YS, Chang YT. Tools for target identification and validation. Curr Opin Chem Biol. 2004;8:371–377. - PubMed
    1. Sanseau P. Impact of human genome sequencing for in silico target discovery. Drug Discov Today. 2001;6:316–323. - PubMed

Publication types

MeSH terms

Substances

LinkOut - more resources