3D pharmacophore modeling is a powerful tool to encode a ligand’s physicochemical properties in space (pharmacophore features), which are supposed to be important for ligand-target binding. Such a model can be used for virtual screening for novel ligands matching the pharmacophore features. Depending on the available data for a target under investigation, 3D pharmacophores can be generated from either a set of known ligands (ligand-based), ligand-macromolecule complexes (structure/interaction-based), or macromolecules (apo structures) without binding ligands (target-based).
Pharmacophores can be built upon static structures from e.g. X-ray experiments (static pharmacophores), however since ligands and macromolecules are of dynamic nature, molecular dynamic simulations have been more and more employed to add a structure’s dynamic behaviour to pharmacophore modeling (dynamic pharmacophores). We work on two tools, T²F-Pharm and T²F-Flex, for static and dynamic target-based pharmacophore modeling, respectively.
Static target-based pharmacophores
Pharmacophore models are an accurate and minimal tridimensional abstraction of intermolecular interactions between chemical structures, usually derived from a group of molecules or from a ligand-target complex. Only a limited amount of solutions exists to model comprehensive pharmacophores using the information of a particular target structure without knowledge of any binding ligand. In the T²F-Pharm project, we developed an automated and customable tool for truly target-focused pharmacophore modeling.
Dynamic target-based pharmacophores
T²F-Flex extends the functionality of T²F-Pharm and offers pharmacophore modeling from MD simulations of apo structures, i.e. empty binding sites, to represent the dynamic nature of the structures. Interaction hotspots on the grid are calculated per snapshot (subset of the trajectory) and subsequently clustered and summarized into a single pharmacophore model. This method allows to generate pharmacophores in the absence of ligand or interaction information and provides a feature-based description of protein binding sites.