PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes

In this work, we present PharmaCore: a new, completely
automatic
workflow aimed at generating three-dimensional (3D) structure-based
pharmacophore models toward any target of interest. The proposed approach
relies on using cocrystallized ligands to create the input files for
generating the pharmacophore hypotheses, integrating not only the
three-dimensional structural information on the ligand but also data
concerning the binding mode of these molecules put in the protein
cavity. We developed a Python library that, starting from the specific
UniProt ID of the protein under investigation as the only element
that requires user intervention, subsequently collects and aligns
the corresponding structures bearing a known ligand in a fully automated
fashion, bringing them all into the same coordinate system. The protocol
includes a final phase in which the aligned small molecules are used
to produce the pharmacophore hypotheses directly onto the protein
structure using a specific software, e.g., Phase (Schrödinger
LLC). To validate the entire procedure and highlight the possible
applications in the field of drug discovery and repositioning, we
first generated pharmacophores for soluble epoxide hydrolase (sEH)
and compared with already-published ones. Then, we reproduced the
binding profile of a reported selective binder of ATAD2 bromodomain
(AM879), testing it against a panel of 1741 pharmacophores related
to 16 epigenetic proteins and automatically generated with PharmaCore,
finally disclosing putative unprecedented off-targets. The computational
predictions were successfully validated with AlphaScreen assays, highlighting
the applicability of the proposed workflow in drug discovery and repositioning.
Finally, the process was also validated on tankyrase 2 and SARS-CoV-2
MPro, confirming the robustness of PharmaCore.