Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Discovery of a novel HCV helicase inhibitor by a de novo drug design approach

Brancale, Andrea, Vlachakiis, Dimitros, Kandil, Sahar, Biondaro, Sonia, Berry, Colin and Neyts, Johan 2008. Discovery of a novel HCV helicase inhibitor by a de novo drug design approach. Antiviral Research 78 (2) , A22. 10.1016/j.antiviral.2008.01.030

Full text not available from this repository.

Abstract

Structure-based drug design methods utilize knowledge of a three-dimensional structure of an enzyme/receptor to develop small molecules able to bind to the desired target, generating a specific biological response. These computer-based methodologies are now becoming an integral part of the drug discovery process and, although the principles of molecular recognition are far from being completely understood, some marketed compounds (i.e. influenza neuraminidase inhibitors and HIV protease inhibitors) have been developed with a successful application of structure-based design techniques. In this presentation we are reporting a successful application of a computer-aided design approach to identify and synthetize a series of novel HCV helicase inhibitors. Initially a putative binding site was identified on the enzyme surface, then a de novo drug design software package was used to generate an initial set of structures that could potentially bind to it. A further structure refinement was carried out by docking a series of virtual libraries derived from the de novo procedure. The best structure identified in silico (AB100) was then prepared and it exhibits a submicromolar inhibition of the HCV helicase. The results of the replicon assay as well as the enzymatic assay for AB100 and a series of related analogues will be also presented.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Pharmacy
Biosciences
Publisher: Elsevier
ISSN: 0166-3542
Last Modified: 04 Jun 2017 01:57
URI: http://orca.cf.ac.uk/id/eprint/6451

Citation Data

Cited 31 times in Google Scholar. View in Google Scholar

Actions (repository staff only)

Edit Item Edit Item