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PLoS One. 2018 Jul 26;13(7):e0201321. doi: 10.1371/journal.pone.0201321. eCollection 2018.

Identification of lead anti-human cytomegalovirus compounds targeting MAP4K4 via machine learning analysis of kinase inhibitor screening data.

Author information

1
Institute for Infection & Immunity, St George's, University of London, London, United Kingdom.
2
Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
3
Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
4
Miami Project to Cure Paralysis, University of Miami, Miami, Florida, United States of America.
5
Department of Neurological Surgery, University of Miami, Miami, Florida, United States of America.
6
Sylvester Comprehensive Cancer Center, University of Miami, Miami, Florida, United States of America.
7
Katz Drug Discovery Center, University of Miami, Miami, Florida, United States of America.
8
Department of Medicine, University of Miami, Miami, Florida, United States of America.

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.

PMID:
30048526
PMCID:
PMC6062112
DOI:
10.1371/journal.pone.0201321
[Indexed for MEDLINE]
Free PMC Article

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