This article is part of the supplement: 7th German Conference on Chemoinformatics: 25 CIC-Workshop

Open Access Poster presentation

Discovery of novel TLR modulators by Molecular Modeling and Virtual Screening

Manuela S Murgueitio1*, Sandra Santos-Sierra2 and Gerhard Wolber1

Author Affiliations

1 Pharmaceutical Chemistry, Institute of Pharmacy, Freie Universität Berlin, Berlin, Berlin,14195, Germany

2 Institute of Biochemical Pharmacology, Medizinische Universität Innsbruck, A-6020, Austria

For all author emails, please log on.

Journal of Cheminformatics 2012, 4(Suppl 1):P58  doi:10.1186/1758-2946-4-S1-P58


The electronic version of this article is the complete one and can be found online at: http://www.jcheminf.com/content/4/S1/P58


Published:1 May 2012

© 2012 Murgueitio et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

Toll-like receptors (TLRs) play a crucial role in the onset of innate immunity by distinguishing between endogenous and pathogen-associated molecular patterns. TLR2, in cooperation with TLR1 and TLR6, recognizes several microbial components such as lipoteichoic acids and lipoproteins [1]. Toll-like receptors have been broadly reported to contribute to several inflammatory chronic diseases and autoimmune diseases [2]. In this study we aim to discover new TLR2 modulating agents through computer-aided drug design.

Based on recently identified synthetic TLR2 agonists [3] and antagonists [4], a shape and chemical-feature based similarity search was performed against a library of 260.071 compounds provided by the National Cancer Institute (NCI) [5]. This led to several virtual hits, which were tested in vitro in a cell-based assay. Several compounds with biological activity on TLR2 signaling in general and TLR1 signaling specifically were identified.

To further optimize these biologically validated virtual hits, molecular interaction fields (MIFs) for the dimerization of TLR2 and TLR1 were developed. Feature-based MIFs allowed for the manual creation of virtual compounds that fulfill an optimized interaction pattern, which led to a 3D pharmacophore that was used for a second virtual screening to select compounds for biological testing.

References

  1. Akira S, Takeda K: Toll-like Receptor Signalling.

    Nat Rev Immunol 2004, 4(7):499-511. PubMed Abstract | Publisher Full Text OpenURL

  2. Kawai T, Akira S: The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors.

    Nat Rev Immunol 2010, 11(5):373-384. Publisher Full Text OpenURL

  3. Guan Y, et al.: Identification of Novel Synthetic Toll-like Receptor 2 Agonists by High Throughput Screening.

    J Biol Chem 2010, 258(31):23755-23762. OpenURL

  4. Zhou S, et al.: Discovery of a novel TLR2 signaling inhibitor with anti-viral activity.

    Antiviral Res 2010, 87(3):295-306. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  5. [http://cactus.nci.nih.gov/download/nci/] webcite