Target Explorer (Prototype)
Target Explorer is intended to be an interactive web site that allows
researchers in the Tuberculosis research community to experiment with
different target selection criteria and explore alternative ways of
prioritizing gene targets for experimentation (crystallization
structure solution, high-throughput screening for inhibitor discovery,
The original source of the data comes from a PLoS-Computational
Biology paper by Hasan and colleagues (Hasan
et al., 2006), who collected various attributes on the 3927 genes in the
H37Rv genome of Mycobacterium tuberculosis, including data on
druggability, enzyme function, essentiality, and DNA micro-array
results (gene expression profiles from various models simulating
latency conditions, e.g. hypoxia, starvation, high pH...).
The Hasan paper combines the various values for each gene together
using a weighted-sum (linear combination) scoring function.
While the Hasan paper proposed a particular set of weights, we
recognize that other researchers have alternative goals in mind and want
to try different weighting schemes.
Individual Categories Used to Characterize Drug
Druggability: These experiments are used to choose domains that bind
small molecules following Lipinski's Rule of 5. (Proteins that have previously
been targeted by experimental drugs are chosen.) The protein domains chosen
need not be specific to M.tb, since if a protein domain has been successfully
inhibited previously to treat any other disease, it might provide some further
information regarding a new class of drugs. Additionally, domains in EC
families with homologues that are targeted by commercial drugs are also
Essentiality: TRaSH (Transposon site hybridization) experiments were
conducted by two different groups based on the same in vitro conditions. These
experiments were conducted to identify growth essential genes in M.tb under
nutrient-rich conditions. 78% of these predicted essential genes share a close
homolog in the M.Leprae genome. Other experiments based on clinical isolates,
identified some genes to be frequently deleted, rendering them undesirable as
- Hopkins AL, Groom CR (2002) The druggable genome. Nat Rev Drug Discov 1: 727.730.
- Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Bateman A, et al. (2005)
InterPro, progress and status in 2005. Nucleic Acids Res 33: D201.205.
- Robertson JG (2005) Mechanistic basis of enzyme-targeted drugs.
Biochemistry 44: 8918.
Metabolic Chokepoints: Enzymes involved in unique essential
chokepoint reactions make good metabolic drug targets, since their function
cannot be compensated for by another enzyme. The chokepoint reactions could
either be the consumption of a unique substrate or production of a unique
product. Targets with unique chokepoint recations and those with unique EC
numbers are all identified.
- Sassetti CM, Boyd DH, Rubin EJ (2003) Genes required for mycobacterial
growth defined by high density mutagenesis. Mol Microbiol 48: 77.84.
- Sassetti CM, Boyd DH, Rubin EJ (2001) Comprehensive identification of
conditionally essential genes in mycobacteria. Proc Natl Acad Sci USA
- Lamichhnae G, Zignol M, Blades NJ, Geiman DE, Dougherty A, Grosset J,
Broman KW , Bishai WR. (2003) A postgenomic method for predicting essential
genes at subsaturation levels of mutagenesis: Application to Mycobacterium
tuberculosis. Proc Natl Acad Sci USA 100: 7213.7218.
Structural Clues: It is very useful to consider targets with known
crystal structures, since this aids in docking and lead-optimization studies.
The SMID genome comparison tool is used to compare genomes to find small
molecule-protein domain interactions that are common across multiple genomes.
Homology with teh host and host-flora is also computed based on this structural
data. Other physical properties such as length and molecular mass of the
target are also used for characterization.
- Yeh I, Hanekamp T, Tsoka S, Karp PD, Altman RB (2004) Computational
analysis of Plasmodium falciparum metabolism: Organizing genomic information
to facilitate drug discovery. Genome Res 14: 917.924.
Microarray Data: Various microarray models of the latent state of
M.tb. in latent in vivo infection are available. If a target is expressed
in most of these models, it increases confidence that it is expressed in the
latent in vivo infection, which could mean that it is required for survival
- Terwilliger TC, Park MS, Waldo GS, Berendzen J, Hung LW, et al. (2003)
The TB structural genomics consortium: A resource for Mycobacterium
tuberculosis biology. Tuberculosis (Edinb) 83: 223.249.
- Alfarano C, Andrade CE, Anthony K, Bahroos N, Bajec M, et al. (2005)
The Biomolecular Interaction Network Database and related tools 2005
update. Nucleic Acids Res 33: D418.D424.
- Schnappinger D, Ehrt S, Voskuil MI, Liu Y, Mangan JA, et al. (2003)
Transcriptional Adaptation of Mycobacterium tuberculosis within macrophages:
Insights into the phagosomal environment. J Exp Med 198: 693.704.
- Sherman DR, Voskuil M, Schnappinger D, Liao R, Harrell MI, et al. (2001)
Regulation of the Mycobacterium tuberculosis hypoxic response gene encoding
alpha-crystallin. Proc Natl Acad Sci U S A 98: 7534.7539.
- Hampshire T, Soneji S, Bacon J, James BW, Hinds J, et al. (2004)
Stationary phase gene expression of Mycobacterium tuberculosis following a
progressive nutrient depletion: A model for persistent organisms?
Tuberculosis (Edinb) 84: 228.238.
- Betts JC, Lukey PT, Robb LC, McAdam RA, Duncan K (2002) Evaluation of a
nutrient starvation model of Mycobacterium tuberculosis persistence by gene
and protein expression profiling. Mol Microbiol 43: 717.731.
- Muttucumaru DG, Roberts G, Hinds J, Stabler RA, Parish T (2004) Gene
expression profile of Mycobacterium tuberculosis in a non-replicating state.
Tuberculosis (Edinb) 84: 239.246.
- Voskuil MI, Visconti KC, Schoolnik GK (2004) Mycobacterium tuberculosis
gene expression during adaptation to stationary phase and low-oxygen dormancy.
Tuberculosis (Edinb) 84: 218.227.
- Boshoff HI, Myers TG, Copp BR, McNeil MR, Wilson MA, et al. (2004) The
transcriptional responses of Mycobacterium tuberculosis to inhibitors of
metabolism: Novel insights into drug mechanisms of action. J Biol Chem
How to Use the Interface
Access and Security
There are two versions, a public and a private version of this interface. A secure
(login based) interface allows access to private data. Each login name is associated
with a list fo data files that a particular user has access to. This access can be
easily changed over time to allow greater interaction between researchers. The public
version provides equal access to all users to all publicly available data.
Different researchers use different criterion to evaluate possible drug targets. In order
to provide them with this flexibility, the first page of the tool allows the user to view
all the different categories of information available regarding each of the targets. The
user can then select the criterion that they want to explore further.
If the user wishes to reselect criteria at any stage they can use the Reselect Columns
button on the second page.
Weights and Score
Each of the selected criteria can be assigned a user defined weight. (Default weights
based on the Hasan paper are shown and can be automatically chosen.) Users can use this feature
to examine the effects of varying the influence of criteria over the target prioritization.
Each target is assigned a final score which is computed as the weighted sum over all the
selected criteria. The drug targets are sorted based on the score. (Targets with the highest
score are at the top of the list.)
As mentioned earlier, the targets are sorted in descending order based on the overall score.
Additionally, it is possible to sort the targets based on any of the selection criteria either
in ascending or descending order. The data can be sorted based on any one column at a time.
In order to resort based on a selection either click on the Rescore button on the
upper left hand corner of the page or click 'Enter' anywhere inside the table.
The user has two options to normalize the data in all the columns: unit normalize and standard
normalization. Unit normalize option normalizes the data to the range [0, 1]. Standard normalize
normalize option normalizes the data to a distribution with mean of 0 and variance of 1.
The user can choose to only view those targets that have values greater than a threshold. They
can specify the selection criteria for each column (for eg.) as > 0 . This will
result in a display of only those targets with a value greater than zero for that column.
The user can examine the relationships between various criteria by calculating the correlation
between these columns. At this point we cannot correlate discrete data with continuous data.
If the two data columns being correlated contain discrete data, then a table containing the
counts for each set fo discrete values is displayed. If the data columns contain continuous
data then a graph showing the distribution is shown.
The user can set a threshold for the values in each column. If the data value in a column is
greater than or equal to the threshold, a value of 1 is added to count, otherwise zero is added.
A final count for each target is computed. The assumption here is that if a target resonds to a
treatment, its level of expression is not as important as there being an expression. This count is
also accompanied by a color coding (green if data ≥ threshold). This allows greater visualization
of the results. Additionally, the targets are sorted based on the count. (The top 200 targets are shown.)
For each column, the min and max values are computed. The mean, standard deviation and the range of data
values are also computed.
Target Explorer is implemented in the Sacchettini lab
at Texas A&M University by Reetal Pai
and Tom Ioerger.
If you have any comments or suggestions regarding the data or implementation features please contact
Reetal Pai at reetalp[at]cs[dot]tamu[dot]edu