Abstract
Protein tunnels and channels are attractive targets for drug design. Drug molecules that block the access of substrates or release of products can be efficient modulators of biological activity. Here, we demonstrate the applicability of a newly developed software tool CaverDock for screening databases of drugs against pharmacologically relevant targets. First, we evaluated the effect of rigid and flexible side chains on sets of substrates and inhibitors of seven different proteins. In order to assess the accuracy of our software, we compared the results obtained from CaverDock calculation with experimental data previously collected with heat shock protein 90α. Finally, we tested the virtual screening capabilities of CaverDock with a set of oncological and anti-inflammatory FDA-approved drugs with two molecular targets—cytochrome P450 17A1 and leukotriene A4 hydrolase/aminopeptidase. Calculation of rigid trajectories using four processors took on average 53 min per molecule with 90% successfully calculated cases. The screening identified functional tunnels based on the profile of potential energies of binding and unbinding trajectories. We concluded that CaverDock is a sufficiently fast, robust, and accurate tool for screening binding/unbinding processes of pharmacologically important targets with buried functional sites. The standalone version of CaverDock is available freely at https://loschmidt.chemi.muni.cz/caverdock/ and the web version at https://loschmidt.chemi.muni.cz/caverweb/.
Highlights
Until the beginning of the new millennium, drug design mostly relied on experimental highthroughput screening (Kansy et al, 1998; Zhang et al, 1999; Bleicher et al, 2003)
Cytochrome P450 17A1 and leukotriene A4 hydrolase/aminopeptidase were selected for flexibility testing as well as the model systems to validate the applicability of CaverDock for the virtual screening of ligand libraries
We tested the intrinsic flexibility of AutoDock Vina implemented in CaverDock with the substrate and inhibitor datasets
Summary
Until the beginning of the new millennium, drug design mostly relied on experimental highthroughput screening (Kansy et al, 1998; Zhang et al, 1999; Bleicher et al, 2003). These techniques evolved rapidly up to the beginning of the nineties. More cost-effective methods emerged with the introduction of docking algorithms and thorough analysis of protein-ligand interactions This boom in docking approaches led to the development of over 60 software tools for docking (Sousa et al, 2010; Pagadala et al, 2017). At the beginning of the new millennium, a new technique for drug design called “virtual screening” started to gain recognition (Clark, 2008; Ripphausen et al, 2010)
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