The importance of discerning shape in molecular pharmacology

Abstract

Shape is a fundamentally important molecular feature that often determines the fate of a compound in terms of molecular interactions with preferred and non-preferred biological targets. Complementarity of binding in small-molecule-protein, peptide-receptor, antigen-antibody and protein-protein interactions is the key to life and survival and also to targeting molecules with bioactivity. We review the application of shape in various biological systems such as substrate recognition, ligand specificity or selectivity and antibody recognition in the context of computational methods such as docking, quantitative structure-activity relationships, classification models and similarity-search algorithms. These in silico pharmacology methods have recently demonstrated the importance and applicability of determining molecular shape in drug discovery, virtual screening and predictive toxicology. The results from recently published studies show that shape and shape-based descriptors are at least as useful as other traditional molecular descriptors.

Box 1. Examples to illustrate the importance of shape

Figure A. simplified example to demonstrate how a protein represented by the Tupperware toy, could have multiple potential binding sites of different shape and size, into which specific shaped molecules may fit. B. Analogous to the Tupperware toy is an example of favorable protein-protein interactions that makes the homotetrameric structure of turkey beta adrenergic receptor. C. An example of a protein with multiple binding sites: surface representation of dihydroorotate dehydrogenase of plasmodium falciparum with bound inhibitor A26 colored red, flavin mononucleotide colored yellow, orotic acid colored green and N8E colored blue.

Figure 1. Models to show favorable and unfavorable ligand-protein interactions

A. structural model of the wild type Dopamine D4 receptor with bound ligand L750,667 (stick model and colored atom type with C-green, O-red, N-blue, H-white, I-purple) . The residues F2.61 and V3.28 in the second and third transmembrane domain (seen as orange sticks) aid in favorable interactions with the ligand. B. structural model of the wild type Dopamine D2 receptor with bound ligand L750,667. The residues V2.61 and F3.28 residues in TM2 and TM3 poses steric hindrance to the ligand.

Figure 2. Using 2D-Structural similarity to understand immunoassay false positives

A. 2D similarity of compounds in the database (FDA approved drugs and some metabolites) to the target compounds of several DOA immunoassays. B. Similarity of five compounds to the target compound desipramine (target of some tricyclic antidepressant screening assays) calculated by MDL public keys using the Tanimoto similarity measure. Of the five compounds, clomipramine is the most similar (Tanimoto similarity = 0.750) while ibuprofen is the least similar (Tanimoto similarity = 0.146).

Figure 3. Using 3D pharmacophores to understand immunoassay false positives

A. 3D-pharmacophore of amitriptyline and desipramine (yellow) generated with Catalyst Discovery Studio 2.0 (Accelrys, San Diego, CA) to illustrate how these could represent the shape of the antigen. B. Mapping of protriptyline (Fit value 3.964 and shape similarity 0.786) the best fitting molecule to the pharmacophore features and inside the van der Waals shape (shaded volume) after performing the pharmacophore-shape search. C. Mapping of clomipramine (Fit value 3.926 and shape similarity 0.761) to the pharmacophore features and inside the van der Waals shape. The cyan blobs represent hydrophobic features and the red blob represents a positive ionizable feature.