Molecular docking is a key tool to predict the best position, orientation, and conformation (flexible shape) of a ligand in a protein. This approach allows us to characterize the behaviour of small molecules in the binding site of target proteins as well as to elucidate molecular interactions. The docking process involves two steps: (1) prediction of the ligand conformation as well as its position and orientation within these sites and (2) assessment of the binding affinity.1 Docking also can be used to perform virtual screening on large libraries of compounds, rank the results, and propose structural hypotheses of how the ligands bind the target, which is of upmost importance in lead optimization.2 These calculations can help increase the probability of finding new inhibitors of key proteins from pathogens, by predicting how tightly a compound can bind to the target, where it might bind, and what types of interactions it could form at the binding site.3,4 It can also increase the efficiency of finding promising candidates for early laboratory testing. To perform such computational experiments for ZIKV, we are using a widely used molecular docking program called AutoDock VINA,5 developed by the Olson laboratory at The Scripps Research Institute, which has also partnered with World Community Grid on two other projects: FightAIDS@Home and GO Fight Against Malaria. Several other World Community Grid projects have also used VINA, including Outsmart Ebola Together, Drug Search for Leishmaniasis and Say No to Schistosoma. We have already prepared the proteins and the compounds and chemical libraries to initiate the Autodock Vina calculations. Due to the absence of any relevant treatment for this virus, we will screen a collection of millions of compounds against Zika protein homology models, from the ZINC database.
1. Meng, X.; Zhang, H,; Mezei, M.; Cui, M. Molecular Docking: A powerful approach for structure-based drug discovery, Curr Comput Aided Drug Des. 2011; 7(2): 146–157. PMCID:PMC3151162
2. Morris, G. M.; Lim-Wilby, M. Molecular Docking, Methods Mol Biol. 2008; 443: 365-382, Springer. 10.1007/978-1-59745-177-2_19
3. Cosconati, S., Forli, S., Perryman, A.L., Harris, R., Goodsell, D.S., and Olson, A.J. Virtual Screening with AutoDock: Theory and Practice. Expert Opin Drug Discov. (2010). 5(6): 597-607. 10.1517/17460441.2010.484460
4. Perryman, A.L., Yu, W., Wang, X., Ekins, S., Forli, S., Li, S.G., Freundlich, J.S., Tonge, P.J., and Olson, A.J. A Virtual Screen Discovers Novel, Fragment-sized Inhibitors of Mycobacterium Tuberculosis InhA. J Chem Inf Model (2015). 55(3): 645-659. 10.1021/ci500672v
5. Trott, O., and Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010 Jan 30;31(2):455-61. 10.1002/jcc.21334