Abstract Details


Poster 53: Target Identification of Anti-prion Compounds

Valencia, J.M.1, Chen, B.2, Gillet, V.J.1
1Information School, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield S1 4DP
2Department of Chemistry, University of Sheffield, Sheffield S3 7HF
The Spongiform Encephalopathies are a group of progressive and fatal neurological diseases occurring in a variety of animals and humans. They are caused by the proteinaceous infectious particle prion PrPSc which is able to transform the normal prion isoform PrPC to the infective conformation by a mechanism which remains unknown[1]. Although spread of the encaphalopathic diseases is under control, the prion protein remains important due to its implication in Alzheimer's disease[2].
Recently, Chen[3] and co-workers have yielded a handful of novel potent anti-prion compounds with EC50 in the range 1-10 nM in a cellular model of the disease. However, these compounds do not show significant interactions with the recombinant prion protein in in vitro binding studies. The aim of this work is to apply a range of in silico target deconvolution approaches in order to identify the possible target and mechanisms of action of these compounds.
We have implemented an inverse docking protocol in GOLD (www.ccdc.cam.ac.uk) on a High Performance Computer Cluster in order to identify possible targets of two anti-prion compounds belonging to the indole family. Two inverse-docking experiments were carried out. The first involved generating a database using the list of 333 genes differentially expressed during a prion infection identified by Hood4 and co-workers. The genes were converted to protein IDs and the database compiled by extracting compounds from the Protein Data Bank (25% of the structures) and homology databases (such as the Swiss-Model Repository, Database of Annotated Comparative Protein Structure Models and the Database of Protein Disorder among others). However, due to the lack of x-ray structures and the quality of the models, a second inverse docking experiment was carried out using the PDBBind database[5]. In both cases, the protein structures were prepared for docking in GOLD by making hydrogens explicit, deleting water molecules, ligands, ions, and, in the case of PDBBind, structures with a similarity of 90% or higher were removed. Protonated energy-minimized 3D structures of the anti-prion compounds were generated in CORINA from SMILES strings.
A benchmark inverse docking experiment showed that ChemScore was the top performing GOLD scoring function, followed closely by ChemPLP. Therefore, a script was implemented to carry out the inverse docking using GOLD 5.1 and ChemScore. Each anti-prion compound was screened against each 3D structural database, the proteins were ranked for each ligand and the top 1% of targets was selected. Possible correlations with the prion disease and protein were searched in SciFinder and in the list of 333 Differentially Expressed Genes[4].
In parallel, microarrays and mass spectrometry experiments using perpetually infected Scrapie Mouse Brain cells of mesodermal origin and the anti-prion compounds were conducted. 18 proteins and approximately 4400 genes were identified to be differentially expressed. However, due to the complexity of the experiment it was not possible to include controls with healthy cells in order to discard basal effects of the compounds.
Using inverse docking, several targets were found to occur in the top ranking positions for both compounds, some of these targets have high similarity with targets already reported in the literature to be relevant for the prion disease. Some of the targets identified with this method also were found in the list of genes identified by microarrays, however, none was found in the list from proteomics. This method has reduced the list of possible targets to a reasonable number to be test one by one in wet lab experiments in order to validate the true biological target.

1. Solforosi, L.; Milani, M.; Mancini, N.; Clementi, M.; Burioni, R., A closer look at prion strains Characterization and important implications. Prion 2013, 7 (2), 99-108.
2. Manuelidis, L., Infectious particles, stress, and induced prion amyloids: A unifying perspective. Virulence 2013, 4 (5).
3. Thompson, M. J.; Borsenberger, V.; Louth, J. C.; Judd, K. E.; Chen, B., Design, synthesis, and structure-activity relationship of indole-3-glyoxylamide libraries possessing highly potent activity in a cell line model of prion disease. Journal of medicinal chemistry 2009, 52, 7503-11.
4. Hwang, D.; Lee, I. Y.; Yoo, H.; Gehlenborg, N.; Cho, J. H.; Petritis, B.; Baxter, D.; Pitstick, R.; Young, R.; Spicer, D.; Price, N. D.; Hohmann, J. G.; Dearmond, S. J.; Carlson, G. A.; Hood, L. E., A systems approach to prion disease. Mol Syst Biol 2009, 5, 252.
5. Wang, R.; Fang, X.; Lu, Y.; Yang, C. Y.; Wang, S., The PDBbind database: methodologies and updates. J Med Chem 2005, 48 (12), 4111-9.



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