Fishing the Target of Antitubercular Compounds: In Silico Target Deconvolution Model Development and Validation

An in silico, target prediction protocol for antitubercular (antiTB) compounds has been proposed in this work. This protocol is the extension of a recently published 'domain fishing model' (DFM), validating its predicted targets on a set of 42 common antitubercular drugs. For the 23 antiTB compounds of the set which are directly linked to targets (see text for definition), the DFM exhibited a very good target prediction accuracy of 95%. For 19 compounds indirectly linked to targets also, a reasonable pathway/embedded pathway prediction accuracy of 84% was achieved. Since mostly eukaryotic ligand binding data was used for the DFM generation, the high target prediction accuracy for prokaryotes (which is an extrapolation from the training data) was unexpected and provides an additional proof of concept of the DFM. To estimate the general applicability of the model, ligand-target coverage analysis was performed. Here, it was found that, although the DFM only modestly covers the entire TB proteome (32% of all proteins), it captures 70% of the proteome subset targeted by 42 common antiTB compounds, which is in agreement with the good predictive ability of the DFM for the targets of the compounds chosen here. In a prospective validation, the model successfully predicted the targets of new antiTB compounds, CBR-2092 and Amiclenomycin. Together, these findings suggest that in silico, target prediction tools may be a useful supplement to existing, experimental target deconvolution strategies.