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Open Access

Discovery of novel TLR modulators by Molecular Modeling and Virtual Screening

  • Manuela S Murgueitio1Email author,
  • Sandra Santos-Sierra2 and
  • Gerhard Wolber1
Journal of Cheminformatics20124(Suppl 1):P58

Published: 1 May 2012


Virtual ScreeningInflammatory Chronic DiseaseTLR1 SignalingTLR2 AgonistInteraction Field

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.

Authors’ Affiliations

Pharmaceutical Chemistry, Institute of Pharmacy, Freie Universität Berlin, Berlin, Berlin, Germany
Institute of Biochemical Pharmacology, Medizinische Universität Innsbruck, Austria


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© Murgueitio et al; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.