Computational Discovery and Experimental Confirmation of TLR9 Receptor Antagonist Leads

Maria Zatsepin, Angela Mattes, Steffen Rupp, Doris Finkelmeier, Arijit Basu, Anke Burger-Kentischer, Amiram Goldblum*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Toll-like receptors (TLR) are receptors of innate immunity that recognize pathogen associated molecular patterns. They play a critical role in many pathological states, in acute and chronic inflammatory processes. TLR9 is a promising target for drug discovery, since it has been implicated in several pathologies, including defense against viral infections and psoriasis. Immune-modulators are promising molecules for therapeutic intervention in these indications. TLR9 is located in the endosome and activated by dsDNA with CpG motives encountered in microbial DNA. Here we report on a combined approach to discover new TLR9 antagonists by computational chemistry and cell based assays. We used our in-house iterative stochastic elimination (ISE) algorithm to create models that distinguish between TLR9 antagonists ("actives") and other molecules ("inactives"), based on molecular physicochemical properties. Subsequent screening and scoring of a data set of 1.8 million commercially available molecules led to the purchasing of top scored molecules, which were tested in a new cell based system based on human pattern recognition receptors (PRRs) stably expressed in NIH3T3 fibroblasts. As described previously, this cell line shows a very low endogenous PRR-activity and contains a reporter gene which is selectively activated by the integrated human PRR enabling rapid screening of potential ligands. IC50 values of each of these top scored molecules were determined. Out of 60 molecules tested, 56 showed antagonistic activity. We discovered 21 new highly potential antagonists with IC50 values lower than 10 μM, with 5 of them having IC50 values under 1 μM.

Original languageAmerican English
Pages (from-to)1835-1846
Number of pages12
JournalJournal of Chemical Information and Modeling
Issue number9
StatePublished - 26 Sep 2016

Bibliographical note

Publisher Copyright:
© 2016 American Chemical Society.


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