Hit-to-lead optimization of a latency-associated nuclear antigen inhibitor against Kaposi's sarcoma-associated herpesvirus infections.
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Authors
Kirsch, PhilineStein, Saskia C
Berwanger, Aylin
Rinkes, Julia
Jakob, Valentin
Schulz, Thomas F
Empting, Martin
Issue Date
2020-06-28
Metadata
Show full item recordAbstract
The Latency-associated nuclear antigen (LANA) plays a central role for the latent persistence of the Kaposi's Sarcoma Herpesvirus (KSHV) in the human host and helps to establish lifelong infections. Herein, we report our efforts towards hit-to-lead generation starting from a previously discovered LANA-DNA inhibitor. By tethering the viral genome to the host nucleosomes, LANA ensures the segregation and persistence of the viral DNA during mitosis. LANA is also required for the replication of the latent viral episome during the S phase of the cell cycle. We aim to inhibit the interaction between LANA and the viral genome to prevent the latent persistence of KSHV in the host organism. Medicinal chemistry-driven optimization studies and structure-activity-relationship investigation led to the discovery of an improved LANA inhibitor. The functional activity of our compounds was evaluated using a fluorescence polarization (FP)-based interaction inhibition assay and electrophoretic mobility shift assay (EMSA). Even though a crystal structure of the ligand protein complex was not available, we successfully conducted hit optimization toward a low micromolar protein-nucleic acid-interaction inhibitor. Additionally, we applied STD-NMR studies to corroborate target binding and to gain insights into the binding orientation of our most potent inhibitor, providing opportunities for further rational design of more efficient LANA-targeting anti KSHV agents in future studies.Citation
Eur J Med Chem. 2020;202:112525. doi:10.1016/j.ejmech.2020.112525.Affiliation
HIPS, Helmholtz-Institut für Pharmazeutische Forschung Saarland, Universitätscampus E8.1 66123 Saarbrücken, Germany.Publisher
ElsevierPubMed ID
32634628Type
ArticleLanguage
enEISSN
1768-3254ae974a485f413a2113503eed53cd6c53
10.1016/j.ejmech.2020.112525
Scopus Count
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- Creative Commons
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