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Discovery of Selective Inhibitors Against EBNA1 via High Throughput In Silico Virtual Screening

Overview of attention for article published in PLOS ONE, April 2010
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Title
Discovery of Selective Inhibitors Against EBNA1 via High Throughput In Silico Virtual Screening
Published in
PLOS ONE, April 2010
DOI 10.1371/journal.pone.0010126
Pubmed ID
Authors

Ning Li, Scott Thompson, David C. Schultz, Weiliang Zhu, Hualiang Jiang, Cheng Luo, Paul M. Lieberman

Abstract

Epstein-Barr Virus (EBV) latent infection is associated with several human malignancies and is a causal agent of lymphoproliferative diseases during immunosuppression. While inhibitors of herpesvirus DNA polymerases, like gancyclovir, reduce EBV lytic cycle infection, these treatments have limited efficacy for treating latent infection. EBNA1 is an EBV-encoded DNA-binding protein required for viral genome maintenance during latent infection. Here, we report the identification of a new class of small molecules that inhibit EBNA1 DNA binding activity. These compounds were identified by virtual screening of 90,000 low molecular mass compounds using computational docking programs with the solved crystal structure of EBNA1. Four structurally related compounds were found to inhibit EBNA1-DNA binding in biochemical assays with purified EBNA1 protein. Compounds had a range of 20-100 microM inhibition of EBNA1 in fluorescence polarization assays and were further validated for inhibition using electrophoresis mobility shift assays. These compounds exhibited no significant inhibition of an unrelated DNA binding protein. Three of these compounds inhibited EBNA1 transcription activation function in cell-based assays and reduced EBV genome copy number when incubated with a Burkitt lymphoma cell line. These experiments provide a proof-of-principle that virtual screening can be used to identify specific inhibitors of EBNA1 that may have potential for treatment of EBV latent infection.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
United Kingdom 1 1%
Norway 1 1%
Russia 1 1%
China 1 1%
Unknown 61 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 28%
Student > Master 10 15%
Student > Ph. D. Student 8 12%
Professor > Associate Professor 4 6%
Other 3 4%
Other 8 12%
Unknown 16 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 24%
Biochemistry, Genetics and Molecular Biology 10 15%
Medicine and Dentistry 6 9%
Chemistry 5 7%
Pharmacology, Toxicology and Pharmaceutical Science 4 6%
Other 8 12%
Unknown 19 28%