Cannabinoid Receptors and Their Ligands: Ligand–Ligand and Ligand–Receptor Modeling Approaches 253biological activities and the structural and electronic features of a series of small
molecules (Nakanishi et al. 1995).
There are many computer modeling/QSAR (quantitative structure–activity re-
lationship) techniques that can be used to deduce information about a receptor
binding site based upon ligand SAR. Among these, conformational analysis, molec-
ular electrostatic potential mapping, receptor steric and receptor essential volume
mapping, and the comparative molecular field analysis (CoMFA) QSAR method
have been used in the literature to gain indirect information about the CB recep-
tors. Central to many of these techniques is a structural superimposition using
hypothesized key pharmacophoric features as molecular alignment guides. The
quality of results emanating from this approach is highly dependent on these
chosen alignments with template molecules. Much of the driving force for struc-
tural superpositions in the CB literature has been the fact that the four structural
classes of CB agonist ligands and the CB antagonist ligands for a particular recep-
tor sub-type displace one another in radioligand binding experiments. This fact
has led to the assumption that key molecular features must superimpose because
the molecules must interact with the same key amino acids of the receptor and
share the same binding site to be able to displace one another. However, ligands do
not necessarily have to occupy exactly the same space nor interact with the same
key amino acids in order to displace one another. The presence of steric overlap
between binding sites is sufficient to account for ligand displacement data. This
means that alignments that incorporate structurally diverse classes of CB ligands
may not lead to the best results.
In a ligand–receptor approach, CB receptor models are probed for binding
sites for ligand classes and binding sites can be screened using energetic criteria,
as well as ligand SAR and the CB mutation literature. The quality of the research
emanatingfromthisapproachdependsheavilyonthequalityofthereceptormodel,
including the state that this model represents. The reliability of ligand binding sites
identified based on energetic criteria is completely dependent on the model itself.
If this model is far from the true receptor structure, then the identification of low
energy binding sites will have little relevance for the CB field. Since the publication
of the 2.8 Å X-ray crystal structure of bovine rhodopsin (Rho) in 2000 (Palczewski
et al. 2000), most models of GPCRs, including the CB receptors (Barnett-Norris et
al. 2002b; Hurst et al. 2002; McAllister et al. 2003; Salo et al. 2004; Shim et al. 2003;
Xie et al. 2003), have been based upon this crystal structure. It is important to note
that this structure represents the dark (inactive) state structure of Rho in which
the inverse agonist, 11-cis-retinal is covalently bound. For ligand–receptor studies
that employ a homology model of Rho, the relevant conformational state of the
receptor should be taken into consideration because the inactive and active states
of a GPCR are fundamentally different in conformation (Ghanouni et al. 2001b;
Hulme et al. 1999; Jensen et al. 2001). Therefore, the state of the receptor for which
an inverse agonist has high affinity (the inactive state) is not the state for which
agonists have high affinity (the activated state).
In the next section, the use of both ligand–ligand and ligand–receptor ap-
proaches in the CB field will be discussed. This discussion is organized around
individual structural classes of CB ligands.