Pharmacophore screening

The basic principle of pharmacophore screening is that the binding of certain drugs with their protein targets is primarily determined by key functional pharmacophores (Rognan, 2010). Thus, the matching of these important pharmacophores can be used to search for new targets of small-molecule drugs (Fang and Wang, 2002). A pharmacophore is the spatial arrangement of functional characteristics that allows molecules to interact with target proteins in a particular binding mode, such as a hydrophobic center (H), hydrogen bond acceptor vector (HBA), hydrogen bond donor vector (HBD), positively charged center (P), or negatively charged center (N) (Kurogi and Güner, 2001). A pharmacophore model is the combination of pharmacophores in a pattern of ligand-protein interaction that give the final pharmacological effect (Leach et al., 2010). Similar to a ligand database for shape screening, a pharmacophore database also requires annotation with target protein information. In pharmacophore screening, the pharmacophore features of the query molecule are successively matched with the features of the pharmacophore models in the database. A higher matching degree indicates that the annotated protein target of the matched pharmacophore model has greater potential to be a target of the query molecule (Steindl et al., 2006). Pharmacophore screening also undergoes two levels of mapping: the first mapping is between the pharmacophore models of the query molecule and of the ligands in the database, and the second mapping is between the matched pharmacophore models of the ligands in the database and their annotated protein targets (Figure ​(Figure22).

The pharmacophore database is built by pharmacophore modeling. The three construction methods are the use of ligands only, receptor structures only, or co-crystallized complex structures, which can be defined as ligand-based, structure-based and complex-based pharmacophore modeling, respectively. Ligand-based pharmacophore modeling was initially designed and is often used for traditional ligand-based virtual screening; an example is the quantitative structure–activity relationship (QSAR; Pulla et al., 2016). The most substantial common features shared by a group of active molecules can be easily extracted by using this method to form a good pharmacophore model to guide the further optimization of active compounds (Leach et al., 2010; Gaurav and Gautam, 2014). However, this approach is seldom used in reverse pharmacophore modeling due to the arbitrariness of pharmacophore models based on a single protein-annotated ligand.

The other two main methods, the use of only receptor structures and the use of protein-ligand complex structures, are forms of structure-based pharmacophore modeling (Gaurav and Gautam, 2014). In receptor-based methods, the pharmacophore features are first extracted from potential binding sites detected by specific protocols, and the pharmacophore models are then derived from the clustering of interaction point information and further refined or validated by using the input of the known ligands and their available or even calculated binding data (Chen and Lai, 2006). For instance, Pocket v.2 (Chen and Lai, 2006) and Catalyst SBP in Discovery Studio (DS) (BIOVIA, 2017) can both produce this type of pharmacophore database. In complex-based methods, pharmacophore models are simply generated via knowledge-based topological rules by using all features, such as hydrogen bonding information, charge, and hydrophobic contacts, based on the interactions between the co-crystallized ligands and receptor atoms (Sutter et al., 2011; Meslamani et al., 2012). Complex-based pharmacophore modeling is commonly used to construct pharmacophore databases, such as PharmaDB in Discovery Studio (Meslamani et al., 2012) and PharmTargetDB in PharmMapper (Liu et al., 2010), due to the stronger association between the built pharmacophore models and the experimentally verified ligand-protein interactions, which can improve the accuracy of target prediction.

The matching process between a pharmacophore model of the query molecule and the pharmacophore models in the pharmacophore database considers the alignment of two core components: pharmacophore feature types and the positions of the feature types (Wolber and Langer, 2005). The alignment of feature types is the matching between the pharmacophore features shared by the query molecule and the database ligands, such as matching between a hydrophobic feature in the molecular structure and those in database ligand pharmacophore models. The alignment of the feature positions is the pairwise matching of the distances between the fitted feature types in the pharmacophore models (Kabsch, 1976). For example, PharmMapper groups pharmacophores into triplets (e.g., H-H-H, H-HBA-HBD) and uses the vertexes of a triangle to represent the pharmacophore feature types and the side length of the triangle to measure the relative positions of these feature types (Liu et al., 2010).

In pharmacophore screening, the pairwise fitness score between pharmacophore models can be used directly as a basis for target evaluation. The fitness score includes the scores obtained from both the alignments between feature types and the alignments between the positions of each pair of pharmacophore models from the query molecule and database ligands. Higher fitness scores indicate higher probabilities (Wang X. et al., 2016). In addition, other matching information, such as the number of matched features and overall shape similarity, can also be used as additional references for target evaluation (Khedkar et al., 2007). If the pharmacophore scoring process does not consider the overall shape of the query molecules, it will be more likely to find pseudo protein targets with high fitness scores for a smaller query molecule because its limited pharmacophore features can be easily matched in the database (Wang X. et al., 2016). Thus, the target score must be recalculated to improve the prediction accuracy (the dotted line in Figure ​Figure2).2). For example, PharmMapper utilizes a normalized fitness score to re-rank the potential targets by standardizing a normal distribution of the fitness score to achieve a higher accuracy (Wang X. et al., 2017).

Since the construction of the pharmacophore database by structure-based pharmacophore modeling is not easy, the development of corresponding tools based on this principle has been somewhat limited. However, compared with shape screening, pharmacophore screening can improve the accuracy of prediction because it focuses on matching the key pharmacophore functional groups. In addition, it can ignore the total size of the molecule. As a result, pharmacophore screening can be used to search for potential targets of a query molecule with a large or small volume and can also be employed to find novel protein targets capable of binding a large diversity of ligands. Although PharmTargetDB, the PharmMapper in-house repository, does incorporate protein structural information, a pharmacophore database can be built to use ligands only. That is, constructing a pharmacophore database based on ligands with currently unavailable target structures is also useful for pharmacophore screening.