In our workflow we will employ 3D-pharmacophore screening to synergize the information coming from co-crystalized fragment crystal structures with the information from molecular dynamics simulations.
Firstly, we will capture the possible protein-ligand interactions in the ADPr binding site of SARS-CoV-2 Mac1 through apo-site molecular dynamics (MD) simulations and PyRod [1]. PyRod is an open-source software that tracks water molecules in MD simulations. Through that process, it identifies hydrogen bonds, as well as hydrophobic, charged, and aromatic areas in the binding site. This information is contained in dynamic molecular interaction fields (dMIFs), that can be visualized or applied to generate pharmacophore features. We will visualize the dMIFs as grids in LigandScout to evaluate their binding contribution and steer our selection of the pharmacophore features in the binding site (PyRod’s “super pharmacophore”) [2].
Secondly, we aim to imitate protein-ligand interactions associated to high ligand efficacy, as shown from a massive crystallographic fragment screen. To apply these pharmacophore features in their induced-fit arrangement, we will first align the crystal structures of fragments occupying the adenine subsite with MOE and use LigandScout to derive structure-based pharmacophores from them [3]. Integrated 3D pharmacophores will then be generated by merging the pharmacophore features originating from both crystal structures (LigandScout) and molecular dynamics simulations (PyRod).
To limit the extensive chemical space of the Enamine REAL collection and rationalize the use of our computational resources, a substructure search will be performed with RDKit to prepare a library of compounds capable of forming interactions derived from the structure-based pharmacophores. These compounds will be ionized using MOE.
The integrated pharmacophore models will then be employed for a virtual screen and subsequent pharmacophore filtering of the docked poses generated with GOLD. Docked poses of the molecules will be energy-minimized in the binding site and visually inspected to ensure shape complementarity to the binding site and their diversity (eliminating compounds through pairs with the highest Tanimoto index).
In the hit optimization phase we will follow the same workflow, but instead of considering the co-crystalized fragments, we will consider the docked pose of our experimentally validated hit compounds from the hit identification phase. Since PyRod is implicated in discovering and evaluating novel interactions in the apo binding site, we will replace it with the use of our open-source software OpenMMDL. This will allow us to evaluate the stability of the docked poses and to analyze the possible binding mechanisms dynamically.
