G Protein-Coupled Receptor-Ligand Pose and Functional Class Prediction

Abstract

G protein-coupled receptor (GPCR) transmembrane protein family members play essential roles in physiology. Numerous pharmaceuticals target GPCRs, and many drug discovery programs utilize virtual screening (VS) against GPCR targets. Improvements in the accuracy of predicting new molecules that bind to and either activate or inhibit GPCR function would accelerate such drug discovery programs. This work addresses two significant research questions. First, do ligand interaction fingerprints provide a substantial advantage over automated methods of binding site selection for classical docking? Second, can the functional status of prospective screening candidates be predicted from ligand interaction fingerprints using a random forest classifier? Ligand interaction fingerprints were found to offer modest advantages in sampling accurate poses, but no substantial advantage in the final set of top-ranked poses after scoring, and, thus, were not used in the generation of the ligand-receptor complexes used to train and test the random forest classifier. A binary classifier which treated agonists, antagonists, and inverse agonists as active and all other ligands as inactive proved highly effective in ligand function prediction in an external test set of GPR31 and TAAR2 candidate ligands with a hit rate of 82.6% actual actives within the set of predicted actives.

Keywords: G protein-coupled receptor (GPCR); docking; interaction fingerprint; machine learning; random forest classifier.

Figure 1

Sources of the six types of ligand–receptor complexes used in our internal dataset. Green and blue cartoon structures are used to represent two different experimentally-determined structures of the same G protein-coupled receptor (GPCR) characterized with the ligand represented by the blue circle labeled ‘L’. Colored ribbon structures represent experimentally-determined GPCR structures and gray ribbon structures represent homology modeled GPCR structures.

Figure 2

Opioid κ receptor (illustrated using Protein Data Bank (PDB) [23] entry 6B73 [162]) ligand interactions observed in the inactive receptor bound with the antagonist JDTic (PDB entry 4DJH [161]) and active receptor bound with the agonist MP1104 (PDB entry 6B73). Pink ribbons indicate sites lacking ligand interactions. Ribbons color-coded according to the legend indicate sites interacting with ligands in either the active receptor complex (red), the inactive receptor complex (blue), or both receptor complexes (white). TM segments are labeled. The middle image is 180° rotated from the left image. The right image shows the top view (looking down at the receptor from the extracellular side of the membrane).

Figure 3

GPCRdb [26] sequence alignment illustrating the differences in transmembrane domain 1 residue numbering for succinate receptor 1 and sphingosine 1-phosphate receptor 3. Yellow, green, purple, red and blue indicate hydrophobic aliphatic, aromatic, polar uncharged, anionic, and cationic amino acid sidechains, respectively. The residue numbers all start with the single-digit numeral in the top row and are followed by either the sequence-based number in the second row or the structure-based number in the third row.

Figure 4

Distributions of ligand–receptor complex types in the (A) training and (B) testing sets used in classifier development.