Accounting of Receptor Flexibility in Ultra-Large Virtual Screens with VirtualFlow Using a Grey Wolf Optimization Method

Christoph Gorgulla; Konstantin Fackeldey; Gerhard Wagner; Haribabu Arthanari

doi:10.14529/jsfi200301

Authors

Christoph Gorgulla Department of Physics, Harvard University, Cambridge, USA Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, USA Department of Cancer Biology, Dana Farber Cancer Institute, Boston, USA
Konstantin Fackeldey Institute of Mathematics, Technical University Berlin, Berlin, Germany Zuse Institute Berlin, Berlin, Germany
Gerhard Wagner Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, USA
Haribabu Arthanari Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, USA Department of Cancer Biology, Dana Farber Cancer Institute, Boston, USA

DOI:

https://doi.org/10.14529/jsfi200301

Abstract

Structure-based virtual screening approaches have the ability to dramatically reduce the time and costs associated to the discovery of new drug candidates. Studies have shown that the true hit rate of virtual screenings improves with the scale of the screened ligand libraries. Therefore, we have recently developed an open source drug discovery platform (VirtualFlow), which is able to routinely carry out ultra-large virtual screenings. One of the primary challenges of molecular docking is the circumstance when the protein is highly dynamic or when the structure of the protein cannot be captured by a static pose. To accommodate protein dynamics, we report the extension of VirtualFlow to allow the docking of ligands using a grey wolf optimization algorithm using the docking program GWOVina, which substantially improves the quality and efficiency of flexible receptor docking compared to AutoDock Vina. We demonstrate the linear scaling behavior of VirtualFlow utilizing GWOVina up to 128 000 CPUs. The newly supported docking method will be valuable for drug discovery projects in which protein dynamics and flexibility play a significant role.

References

Bohacek, R.S., McMartin, C., Guida, W.C.: The art and practice of structure-based drug design: A molecular modeling perspective. Medicinal Research Reviews 16(1), 3–50 (1996), DOI: 10.1002/(SICI)1098-1128(199601)16:1¡3::AID-MED1¿3.0.CO;2-6

ChemAxon: JChem Suite 18.20.0. https://chemaxon.com/products/jchem-engines, accessed: 2020-09-06

Elastifile Cloud File System. https://console.cloud.google.com/marketplace/details/elastifile/elastifile-cloud-file-system, accessed: 2020-09-06

Elfiky, A.A.: Zika viral polymerase inhibition using anti-HCV drugs both in market and under clinical trials. Journal of Medical Virology 88(12), 2044–2051 (2016), DOI: 10.1002/jmv.24678

Elfiky, A.A., Elshemey, W.M.: IDX-184 is a superior HCV direct-acting antiviral drug: a QSAR study. Medicinal Chemistry Research 25(5), 1005–1008 (2016), DOI: 10.1007/s00044-016-1533-y

Ganesan, A., Barakat, K.: Applications of computer-aided approaches in the development of hepatitis C antiviral agents. Expert Opinion on Drug Discovery 12(4), 407–425 (2017), DOI: 10.1080/17460441.2017.1291628

Google Cloud. https://cloud.google.com, accessed: 2020-09-06

Gorgulla, C.: Free Energy Methods Involving Quantum Physics, Path Integrals, and Virtual Screenings. Ph.D. thesis, Freie Universität Berlin (2018), DOI: 10.17169/refubium-11597

Gorgulla, C., Boeszoermenyi, A.,Wang, Z.F., et al.: An open-source drug discovery platform enables ultra-large virtual screens. Nature 580(7805), 663–668 (2020), DOI: 10.1038/s41586-020-2117-z

Koebel, M.R., Schmadeke, G., Posner, R.G., et al.: AutoDock VinaXB: implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina. Journal of Cheminformatics 8(1), 27 (2016), DOI: 10.1186/s13321-016-0139-1

Koes, D.R., Baumgartner, M.P., Camacho, C.J.: Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. Journal of Chemical Information and Modeling 53(8), 1893–1904 (2013), DOI: 10.1021/ci300604z

Lal, D.K., Barisal, A., Tripathy, M.: Grey Wolf Optimizer Algorithm Based Fuzzy PID Controller for AGC of Multi-area Power System with TCPS. Procedia Computer Science 92, 99–105 (2016), DOI: 10.1016/j.procs.2016.07.329

Lyu, J., Wang, S., Balius, T.E., et al.: Ultra-large library docking for discovering new chemotypes. Nature 566, 224–229 (2019), DOI: 10.1038/s41586-019-0917-9

Mirjalili, S., Mirjalili, S., Lewis, A.: Grey wolf optimizer. Advances in Engineering Software 69, 46–61 (2014), DOI: 10.1016/j.advengsoft.2013.12.007

Morris, G.M., Huey, R., Lindstrom, W., et al.: AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry 30(16), 2785–2791 (2009), DOI: 10.1002/jcc.21256

Nivedha, A.K., Thieker, D.F., Makeneni, S., et al.: Vina-Carb: Improving Glycosidic Angles during Carbohydrate Docking. Journal of Chemical Theory and Computation 12(2), 892–901 (2016), DOI: 10.1021/acs.jctc.5b00834

O’Boyle, N.M., Banck, M., James, C.A., et al.: Open Babel: An open chemical toolbox. Journal of Cheminformatics 3(1), 1–14 (2011), DOI: 10.1186/1758-2946-3-33

Papoian, G.A., Wolynes, P.G.: The physics and bioinformatics of binding and folding – energy landscape perspective. Biopolymers 68(3), 333–349 (2003), DOI: 10.1002/bip.10286

Schrödinger LLC, New York: Maestro Release 2020-3. https://www.schrodinger.com/maestro, accessed: 2020-09-06

Shaw, D.E., Deneroff, M.M., Dror, R.O., et al.: Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51(7), 91–97 (2008), DOI: 10.1145/1364782.1364802

Sterling, T., Irwin, J.J.: ZINC 15 – ligand discovery for everyone. Journal of Chemical Information and Modeling 55(11), 2324–2337 (2015), DOI: 10.1021/acs.jcim.5b00559

Trott, O., Olson, A.J.: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry 31(2), 455–461 (2010), DOI: 10.1002/jcc.21334

VirtualFlow. https://virtual-flow.org/ (2020), accessed: 2020-09-06

Wong, K.M., Tai, H.K., Siu, S.W.I.: GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking. Chemical Biology & Drug Design (2020), DOI: 10.1111/cbdd.13764

Yin, W., Mao, C., Luan, X., et al.: Structural basis for inhibition of the RNA-dependent RNA polymerase from SARS-CoV-2 by remdesivir. Science 368(6498), 1499–1504 (2020), DOI: 10.1126/science.abc1560