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J Comput Aided Mol Des. 2011 Mar;25(3):263-74. doi: 10.1007/s10822-011-9418-0. Epub 2011 Feb 23.

Structure-guided fragment-based in silico drug design of dengue protease inhibitors.

Author information

1
Institute of Biophysical Chemistry, Center for Biomolecular Magnetic Resonance and Frankfurt Institute for Advanced Studies, J.W. Goethe-University, Max-von-Laue-Str. 9, 60438 Frankfurt am Main, Germany.

Abstract

An in silico fragment-based drug design approach was devised and applied towards the identification of small molecule inhibitors of the dengue virus (DENV) NS2B-NS3 protease. Currently, no DENV protease co-crystal structure with bound inhibitor and fully formed substrate binding site is available. Therefore a homology model of DENV NS2B-NS3 protease was generated employing a multiple template spatial restraints method and used for structure-based design. A library of molecular fragments was derived from the ZINC screening database with help of the retrosynthetic combinatorial analysis procedure (RECAP). 150,000 molecular fragments were docked to the DENV protease homology model and the docking poses were rescored using a target-specific scoring function. High scoring fragments were assembled to small molecule candidates by an implicit linking cascade. The cascade included substructure searching and structural filters focusing on interactions with the S1 and S2 pockets of the protease. The chemical space adjacent to the promising candidates was further explored by neighborhood searching. A total of 23 compounds were tested experimentally and two compounds were discovered to inhibit dengue protease (IC(50) = 7.7 μM and 37.9 μM, respectively) and the related West Nile virus protease (IC(50) = 6.3 μM and 39.0 μM, respectively). This study demonstrates the successful application of a structure-guided fragment-based in silico drug design approach for dengue protease inhibitors providing straightforward hit generation using a combination of homology modeling, fragment docking, chemical similarity and structural filters.

PMID:
21344277
DOI:
10.1007/s10822-011-9418-0
[Indexed for MEDLINE]

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