Physics-based (molecular dynamics simulations), de novo design (3D pharmacophore-based virtual screening) and high-throughput docking

The project will begin with a structure-based analysis of the RNA binding cavity of SARS-CoV-2 Nsp3, based on the crystal structure and the fragments, molecular dynamics simulations, and the in-house program PyRod [1,2] to sample interaction points in the binding pocket. Briefly, PyRod traces water molecules in protein binding cavities and generates dynamic maps describing the interaction patterns of the water molecules with respect to the protein. This identifies water molecules whose displacement with appropriate ligand moieties should result in a more favorable free energy upon ligand binding and thus increase the chance to identify high affinity ligands. This identifies water molecules whose displacement with appropriate ligand moieties should result in a more favorable free energy upon
 ligand binding and thus increase the chance to identify high affinity ligands. The results from these analyses will be used to generate 3D pharmacophore features for 3D pharmacophore-based virtual screening of the Enamine Real database [3]. As a second approach, we will derive structure-based 3D pharmacophores of the co-crystallized ligands crystal structures. Those will be aligned to the 3D pharmacophores generated by PyRod to optimize our screening pharmacophore.

The collection from the first two approaches will firstly be filtered via molecular docking into the binding cavity and subsequent scoring of the docking poses against the initial screening pharmacophore [4-11]. The compounds will then further be filtered by the ACS substructure filter for removal of pan-assay interference compounds (PAINS), and by chemical diversity via Tanimoto coefficients based on Morgan fingerprints of the hits. The final filtering step will comprise visual inspection to ensure shape complementarity of hits to the binding cavity.

The combination of our two structure-based approaches, being the application of the in-house program Pyrod[1,2] for the creation of 3D pharmacophores based
upon molecular dynamics simulations of water molecules in the protein binding cavities in addition to the use of 3D pharmacophores of the co-crystallized ligands
would allow us to generate 3D pharmacophores with certain features, that would increase the chance of obtaining high-affinity ligands.

Dynamic 3D Pharmacophores

InteLigand - LigandScout

CCG - MOE

Schrodinger - Desmond 

CCDC - GOLD

OpenEye - Szybki

PyRod

1. Schaller D, Pach S, Wolber G. PyRod: Tracing Water Molecules in Molecular Dynamics Simulations. J Chem Inf Model. 2019 Jun 24;59(6):2818-29. (10.1021/acs.jcim.9b00281)

2. Pach S, Sarter TM, Yousef R, Schaller D, Bergemann S, Arkona C, et al. Catching a Moving Target: Comparative Modeling of Flaviviral NS2B-NS3 Reveals Small Molecule Zika Protease Inhibitors. ACS Med Chem Lett. 2020 Apr 9;11(4):514-20. (10.1021/acsmedchemlett.9b00629)

3. Schaller D, Šribar D, Noonan T, Deng L, Nguyen TN, Pach S, et al. Next generation 3D pharmacophore modeling. WIREs Comput Mol Sci. 2020 Jul;10(4) (10.1002/wcms.1468)

4. Machalz D, Li H, Du W, Sharma S, Liu S, Bureik M, et al. Discovery of a novel potent cytochrome P450 CYP4Z1 inhibitor. European Journal of Medicinal Chemistry. 2021 Apr;215:113255. (10.1016/j.ejmech.2021.113255) 

5. Pach S, Sarter TM, Yousef R, Schaller D, Bergemann S, Arkona C, et al. Catching a Moving Target: Comparative Modeling of Flaviviral NS2B-NS3 Reveals Small Molecule Zika Protease Inhibitors. ACS Med Chem Lett. 2020 Apr 9;11(4):514-20. (10.1021/acsmedchemlett.9b00629)

6. Šribar D, Grabowski M, Murgueitio MS, Bermudez M, Weindl G, Wolber G. Identification and characterization of a novel chemotype for human TLR8 inhibitors. European Journal of Medicinal Chemistry. 2019 Oct;179:744-52. (10.1016/j.ejmech.2019.06.084)

7. Schulz R, Atef A, Becker D, Gottschalk F, Tauber C, Wagner S, et al. Phenylthiomethyl Ketone-Based Fragments Show Selective and Irreversible Inhibition of Enteroviral 3C Proteases. J Med Chem. 2018 Feb 8;61(3):1218-30. (10.1021/acs.jmedchem.7b01440)

8. Murgueitio M, Ebner S, Hörtnagl P, Rakers C, Bruckner R, Henneke P, et al. Enhanced immunostimulatory activity of in silico discovered agonists of Toll-like receptor 2 (TLR2). Biochimica et Biophysica Acta (BBA) - General Subjects. 2017 Nov;1861(11):2680-9. (10.1016/j.bbagen.2017.07.011)

9. Rakers C, Schumacher F, Meinl W, Glatt H, Kleuser B, Wolber G. In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations. Journal of Biological Chemistry. 2016 Jan;291(1):58-71. (10.1074/jbc.M115.685610)

10. Wolber G, Dornhofer AA, Langer T. Efficient overlay of small organic molecules using 3D pharmacophores. J Comput Aided Mol Des. 2007 Feb 7;20(12):773-88. (10.1007/s10822-006-9078-7) 

11.  Wolber G, Langer T. LigandScout:  3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters. J Chem Inf Model. 2005 Jan 1;45(1):160-9. (10.1021/ci049885e)