This project will begin with a ligand-based approach by clustering the known ligands to identify groups of compounds with potentially similar interaction profiles. This will be done by generating Morgan fingerprints and calculating their Tanimoto distances, and by clustering the compounds in LigandScout by 3D pharmacophore similarity. The clusters will then be analyzed using  LigandScout to generate ligand-based 3D pharmacophores. Furthermore, we will generate shape-based queries with OpenEye's ROCS (rapid overlay of chemical structures).  We will evaluate our pharmacophore and ROCS models by performing a virtual screen against the clustered ligands and a set of decoys generated with DUD-E. The models that perform best according to the area under the receiver operator characteristics (ROC) curve will be utilized in ou workflow. 

As no structure for MHCR1 is currently available, an inactive AlphaFold model will be generated following the alphafold-multistate protocol. The model will be biased using inactive conformations retrieved from the gpcrdb. The AlphaFold model will be optimized and evaluated in MOE. Using molecular dynamics (MD) simulations set up with the in-house open-source software OpenMMDL[https://github.com/wolberlab/OpenMMDL], the final 3D receptor model will be chosen based on stability evaluated on the backbone root mean square deviation (RMSD). Furthermore, structure-based 3D pharmacophores based on tracing interactions between water molecules and the receptor in molecular dynamics simulations will be generated using the in-house developed software PyRod[1, 2]. This will aid in the identification of novel interaction patterns for ligand design.

Subsequently, LigandScout and ROCS will be used to screen a virtual library based on the Enamine Real database against the 3D pharmacophore models and shape queries[2-7].  The hit compounds from the screenings will be docked into the extracellular binding cavity of the AlphaFold model with GOLD. Molecular binding hypotheses will be evaluated based on the number and type of interaction features as calculated by Ligandscout. The compounds will then be filtered to remove pan assay interference compounds (PAINS) with rdkit. The final step will comprise of visual inspection to leverage medicinal chemistry expertise and select the best fitting compounds[8].

Our computationally efficient approach combining modern 3D-pharmacophore modeling with well-established protocols has proven effective in identifying potent hit compounds. Our combination of experience in chemical and structure biological fields ensures a synergistic use of ligand and structure-based methods to achieve a well-rounded result. With this expertise, we believe we can achieve results that surpass the performance of the coarse machine learning models that will be applied to this challenge based on the given data.

InteLigand - LigandScout

OpenEye - ROCS

CCG - MOE

CCDC - GOLD

OpenMMDL

OpenMM

PyRod

AlphaFold / alphafold-multistate

RDkit

1.        Schaller, D., S. Pach, and G. Wolber, PyRod: Tracing Water Molecules in Molecular Dynamics Simulations. J Chem Inf Model, 2019. 59(6): p. 2818-2829.

2.        Pach, S., et al., Catching a Moving Target: Comparative Modeling of Flaviviral NS2B-NS3 Reveals Small Molecule Zika Protease Inhibitors. ACS Med Chem Lett, 2020. 11(4): p. 514-520.

3.        Wolber, G. and T. Langer, LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model, 2005. 45(1): p. 160-9.

4.        Wolber, G., A.A. Dornhofer, and T. Langer, Efficient overlay of small organic molecules using 3D pharmacophores. Journal of Computer-Aided Molecular Design, 2006. 20(12): p. 773-788.

5.        Šribar, D., et al., Identification and characterization of a novel chemotype for human TLR8 inhibitors. Eur J Med Chem, 2019. 179: p. 744-752.

6.        Schaller, D., et al., Next generation 3D pharmacophore modeling. WIREs Computational Molecular Science, 2020. 10(4): p. e1468.

7.        Machalz, D., et al., Discovery of a novel potent cytochrome P450 CYP4Z1 inhibitor. Eur J Med Chem, 2021. 215: p. 113255.

8.        Noonan, T., et al., Mind the Gap - Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence. Pharmaceuticals, 2022. 15(11): p. 1304.