Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein–ligand binding challenge
To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein–ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new “Rank Difference Ratio” metric. The first stage of SAMPL4 involved using virtual screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: (1) AutoDock Vina (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4’s very challenging compound library that displayed a significantly lower amount of structural diversity than most libraries that are conventionally employed in prospective virtual screens, these approaches produced hit rates of 24, 25, 34, and 27 %, respectively, on a set with 19 % declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly created, multi-institution, NIH-funded center called the “HIV Interaction and Viral Evolution Center”.
KeywordsVirtual screening Ligand docking Computational pipeline HIV integrase Computational drug design Docking challenge AutoDock Vina
We thank the I.T. staff at The Scripps Research Institute (especially Jean-Christophe Ducom, and Lisa Dong) for maintaining a great Linux cluster and for giving the Levy lab access to it for their BEDAM calculations. This research was funded by the HIVE center Grant (P50 GM103368) and by the AutoDock grant (R01 GM069832).
- 1.Mobley DL, Liu S, Lim NM, Wymer KL, Perryman AL, Forli S, Deng N, Su J, Branson K, Olson AJ (2014) J Comput Aided Mol Des. doi: 10.1007/s10822-014-9723-5
- 2.Tiefenbrunn T, Forli S, Happer M, Gonzalez A, Tsai Y, Soltis M, Elder JH, Olson AJ, Stout CD (2013) Chem Biol Drug Des 83(2):141Google Scholar
- 3.Tiefenbrunn T, Forli S, Baksh MM, Chang MW, Happer M, Lin YC, Perryman AL, Rhee JK, Torbett BE, Olson AJ, Elder JH, Finn MG, Stout CD (2013) ACS Chem Biol 8(6):1223Google Scholar
- 8.Gallicchio E, Deng N, He P, Perryman AL, Santiago DN, Forli S, Olson AJ, Levy R (2014) J Comput Aided Mol Des 28(1)Google Scholar
- 11.Dewdney TG, Wang Y, Kovari IA, Reiter SJ, Kovari LC (2013) J Struct Biol 184(2):245Google Scholar
- 23.Forli S. Raccoon (2010) Molecular Graphics Laboratory, The Scripps Research Institute, La Jolla. http://autodock.scripps.edu/resources/raccoon. Accessed 2013
- 24.Trott O, Olson AJ (2010) J Comput Chem 31(2):455Google Scholar
- 30.LigPrep. 2.6. (2013) Schrödinger LLC, New YorkGoogle Scholar
- 32.Güner OF (1999) Pharmacophore perception, development, and use in drug design. International University Line, La Jolla, CAGoogle Scholar
- 33.Langer T, Hoffman RD (2006) Pharmacophores and pharmacophore searches. Wiley-VCH, Weinheim, Germany Google Scholar
- 34.Zhu T, Cao S, Su P-C, Patel R, Shah D, Chokshi HB, Szukala R, Johnson ME, Hevener KE (2013) J Med Chem 56(17):6560Google Scholar
- 35.Sanner MF (1999) J Mol Graph Model 17(1):57Google Scholar