Title

CaverDock: Software tool for fast screening of un/binding of ligands in protein engineering

Conference Dates

September 15-19, 2019

Abstract

Protein tunnels, channels and gates are important for enzymatic catalysis and also represent attractive targets for rational protein design and drug design [1]. Drug molecules blocking the access of natural substrate or release of products are very efficient modulators of biological activity. Here we demonstrate the application of newly in-house developed software tool CaverDock [2,3] for the analysis of the transport of ligands through tunnels in biomolecular targets. Caverdock is a new addition to the Caver Suite [4-6]. We performed virtual screening of large databases of drugs against two pharmacologically relevant targets. We have used FDA-approved drugs for both targets. Oncological drugs (133 molecules), taken from the NIH website, and anti-inflammatory (56 molecules), taken from the Drugbank website, as the libraries of ligands for the two molecular targets: (i) cytochrome P450 17A1 and (ii) leukotriene A4 hydrolase/aminopeptidase. Moreover, we will also show the unbinding of the 2,3-dichloropropan-1-ol product from a buried active site of an haloalkane dehalogenase and its variant. With this study we identified hot-spots that may be used for directed evolution or site-directed mutagenesis to create new variants for faster 2,3-dichloropropan-1-ol release [7]. Finally, we will show the difference on ligand transportation when a protein is in an open and closed conformations [8]. We will show how CaverDock tackles the problem of protein flexibility.

1. Marques, S.M., et al., 2017: Enzyme Tunnels and Gates as Relevant Targets in Drug Design. Medicinal Research Reviews 37: 1095-1139.

2. Vavra, O., et al., 2019: CaverDock 1.0: A New Tool for Analysis of Ligand Binding and Unbinding Based on Molecular Docking. Bioinformatics (under review).

3. Filipovic, J., et al, 2019: A Novel Method for Analysis of Ligand Binding and Unbinding Based on Molecular Docking. Transactions on Computational Biology and Bioinformatics (under review)

4. Chovancova, E., et al., 2012: CAVER 3.0: A Tool for Analysis of Transport Pathways in Dynamic Protein Structures. PLOS Computational Biology 8: e1002708.

5. Jurcik, A., et al., 2018: CAVER Analyst 2.0: Analysis and Visualization of Channels and Tunnels in Protein Structures and Molecular Dynamics Trajectories. Bioinformatics 34: 3586-3588.

6. Stourac, J., et al., 2019: Caver Web 1.0: Identification of Tunnels and Channels in Proteins and Analysis of Ligand Transport. Nucleic Acids Research (under review).

7. Marques, S.M., et al., 2019: Computational Study of Protein-Ligand Unbinding for Enzyme Engineering. Frontiers in Chemistry 6: 650.

8. Kokkonen, P., et al., 2018: Molecular Gating of an Engineered Enzyme Captured in Real Time. Journal of the American Chemical Society 140: 17999–18008.

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