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PREDator - a new structure-based approach for cross-reactivity predictions

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Ligandcentric virtual screening techniques employ in most cases three-dimensional Gaussians in order to define a molecule's entire pharmacophoric properties (ideally of a co-crystallised ligand) [1] and have been applied successfully in many prospective and retrospective drug discovery campaign [2].

Here, the development of a new pharmacophoric binding site descriptor in the spirit of Cavbase [3] is presented: Instead of focussing on ligand features, crucial amino-acid residues within the binding site are identified and represented as a pharmacophore model. Our method aims to combine the advantages of Cavbase with the smooth nature of a Gaussian pharmacophore representation, thus enabling binding site comparisons independently of sequence homology. Gaussian models are fast to compute and show the advantage that only very few parameters have to be defined. In contrast to a recently published approach where the entire binding site is defined by a Gaussian model for structure-based cross-reactivity predictions [4], PREDator employs only a few characteristic cavity-flanking amino acids [5] which are finally encoded in order to accelerate computations.

It is shown that these models, as a conceptual representation of the binding site, can be used successfully for cross-reactivity predictions. Compared to a ligandcentric approach [5] with regard to this purpose, a structure-based approach is advantageous in terms of being less dependent on the ligand-scaffold.

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    Desaphy J, Kellenberger E, Rognan D: Comparison and Druggability Prediction of Protein-Ligand Binding Sites from Pharmacophore-Annotated Cavity Shapes. J Chem Inf Model. 2012, 52 (8):

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    Diwan MM, et al: Exploring Polypharmacology Using a ROCS-Based Target Fishing Approach. J Chem Inf Model. 2012, 52: 492-505. 10.1021/ci2003544.

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Correspondence to Florian Koelling.

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

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Koelling, F. PREDator - a new structure-based approach for cross-reactivity predictions. J Cheminform 5, P6 (2013) doi:10.1186/1758-2946-5-S1-P6

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Keywords

  • Drug Discovery
  • Virtual Screening
  • Gaussian Model
  • Pharmacophore Model
  • Conceptual Representation