An established paradigm in current drug development is (i) to identify a single protein target whose inhibition is likely to result in the successful treatment of a disease of interest; (ii) to assay experimentally large libraries of small-molecule compounds in vitro and in vivo to identify promising inhibitors in model systems; and (iii) to determine whether the findings are extensible to humans. This complex process, which is largely based on trial and error, is risk-, time- and cost-intensive. Computational (virtual) screening of drug-like compounds simultaneously against the atomic structures of multiple protein targets, taking into account protein-inhibitor dynamics, might help to identify lead inhibitors more efficiently, particularly for complex drug-resistant diseases. Here we discuss the potential benefits of this approach, using HIV-1 and Plasmodium falciparum infections as examples. We propose a virtual drug discovery 'pipeline' that will not only identify lead inhibitors efficiently, but also help minimize side-effects and toxicity, thereby increasing the likelihood of successful therapies.
Viral fusogenic envelope proteins are important targets for the development of inhibitors of viral entry. We report an approach for the computational design of peptide inhibitors of the dengue 2 virus (DENV-2) envelope (E) protein using high-resolution structural data from a pre-entry dimeric form of the protein. By using predictive strategies together with computational optimization of binding “pseudoenergies”, we were able to design multiple peptide sequences that showed low micromolar viral entry inhibitory activity. The two most active peptides, DN57opt and 1OAN1, were designed to displace regions in the domain II hinge, and the first domain I/domain II beta sheet connection, respectively, and show fifty percent inhibitory concentrations of 8 and 7 µM respectively in a focus forming unit assay. The antiviral peptides were shown to interfere with virus:cell binding, interact directly with the E proteins and also cause changes to the viral surface using biolayer interferometry and cryo-electron microscopy, respectively. These peptides may be useful for characterization of intermediate states in the membrane fusion process, investigation of DENV receptor molecules, and as lead compounds for drug discovery.
Abstract-The Severe Acute Respiratory Syndrome (SARS) is a serious respiratory illness that has recently been reported in parts of Asia and Canada. In this study, we use molecular dynamics (MD) simulations and docking techniques to screen 29 approved and experimental drugs against the theoretical model of the SARS CoV proteinase as well as the experimental structure of the transmissible gastroenteritis virus (TGEV) proteinase. Our predictions indicate that existing HIV-1 protease inhibitors, l-700,417 for instance, have high binding affinities and may provide good starting points for designing SARS CoV proteinase inhibitors. # 2003 Elsevier Ltd. All rights reserved.A novel coronavirus (CoV) has been isolated and identified as the cause of the severe acute respiratory syndrome (SARS), 1 for which there is currently no effective treatment. The SARS CoV genome sequence has been recently published. 2 The structure of the main proteinase, essential for SARS CoV replication, can be deduced from its similarity (43% sequence identity) to the X-ray crystallography structure of the proteinase from the porcine transmissible gastroenteritis virus (TGEV), also a coronavirus. 3 Anand et al., who solved the structure of the TGEV proteinase (PDB code: 1lvo), 4 have produced a model for the SARS CoV proteinase (PDB code: 1p9t and 1pa5) using the former as a template. In addition, they propose a drug discovered to treat the common cold, AG7088, as a good starting point for SARS CoV inhibition. AG7088 by itself has failed to inhibit the SARS CoV in vitro. 5 Here, using simulations, we show that HIV-1 protease inhibitors may provide better starting points than AG7088 for inhibition of the SARS CoV proteinase.Our computational simulation approaches use molecular dynamics (MD) and docking techniques, and have been applied to calculate the binding affinities of HIV-1 protease mutants and their inhibitors. 6 The affinities have then been used to predict resistance/susceptibility for 1800 HIV-1 protease mutants/inhibitor combinations with greater than 90% accuracy. In a similar fashion, we have calculated the binding affinities of a variety of known HIV-1 protease inhibitors to the TGEV main proteinase. Docking calculations were carried out using AutoDock version 3.0.5, with the Larmarckian genetic algorithm (LGA). 7 Since the structures of the TGEV and SARS CoV proteinases-inhibitor complexes have not been obtained, we therefore first performed preliminary docking experiments to identify the potential binding sites of the inhibitors by generating a grid box that is big enough to cover the entire surface of the protein. Docking runs were set to 100 to allow AutoDock to exhaustively find the potential binding sites of each inhibitor. The first ranked docking solution showed that all inhibitors bound to the substrate binding site of the TGEV and SARS proteinases.The protein-inhibitor complexes derived from the preliminary docking step were then immersed in TIP3-water shell and all atoms were allowed to relax using MD simulation. MD simula...
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