Abstract

Chemogenomic approaches involving highly annotated compound sets and cell based high throughput screening are emerging as a means to identify novel drug targets. We have previously screened a collection of highly characterized kinase inhibitors (Khan et al., Journal of General Virology, 2016) to identify compounds that increase or decrease expression of a human cytomegalovirus (HCMV) protein in infected cells. To identify potential novel anti-HCMV drug targets we used a machine learning approach to relate our phenotypic data from the aforementioned screen to kinase inhibition profiling of compounds used in this screen. Several of the potential targets had no previously reported role in HCMV replication. We focused on one potential anti-HCMV target, MAPK4K, and identified lead compounds inhibiting MAP4K4 that have anti-HCMV activity with little cellular cytotoxicity. We found that treatment of HCMV infected cells with inhibitors of MAP4K4, or an siRNA that inhibited MAP4K4 production, reduced HCMV replication and impaired detection of IE2-60, a viral protein necessary for efficient HCMV replication. Our findings demonstrate the potential of this machine learning approach to identify novel anti-viral drug targets, which can inform the discovery of novel anti-viral lead compounds.