Identification of Allosteric Modulators of Metabotropic Glutamate 7 Receptor Using Proteochemometric Modeling

Abstract

Proteochemometric modeling (PCM) is a computational approach that can be considered an extension of quantitative structure-activity relationship (QSAR) modeling, where a single model incorporates information for a family of targets and all the associated ligands instead of modeling activity versus one target. This is especially useful for situations where bioactivity data exists for similar proteins but is scarce for the protein of interest. Here we demonstrate the application of PCM to identify allosteric modulators of metabotropic glutamate (mGlu) receptors. Given our long-running interest in modulating mGlu receptor function we compiled a matrix of compound-target bioactivity data. Some members of the mGlu family are well explored both internally and in the public domain, while there are much fewer examples of ligands for other targets such as the mGlu₇ receptor. Using a PCM approach mGlu₇ receptor hits were found. In comparison to conventional single target modeling the identified hits were more diverse, had a better confirmation rate, and provide starting points for further exploration. We conclude that the robust structure-activity relationship from well explored target family members translated to better quality hits for PCM compared to virtual screening (VS) based on a single target.

Figure 1

(A) Pie chart showing the reported ligands for mGlu receptors in the Thomson Reuters Integrity database. The most explored are mGlu₅, mGlu₂, and mGlu₁. Extracted on March 15th 2017. (B) Known mGlu₇ receptor reference compounds: allosteric antagonist/NAM MMPIP and agonist/PAM AMN-082.

Figure 2

(A) Nonsequential alignment of chosen binding site amino acids, coloring is based on Clustal X similarity. (B) mGlu₁ and mGlu₅ 7-TM crystal structures showing NAMs and binding site amino acids. (C) An example of mGlu₇ 7-TM model receptor generated based on the sequence alignment and showing the same corresponding allosteric binding site amino acids.

Figure 3

PCM model random learning curve external validation. (A) External validation ROC plot for overall performance (0.96 yellow), the best performing receptor (human mGlu₄, 0.99 in blue), and the worst performing receptor (rat mGlu₅, 0.81 in orange). (B) Performance of learning curves with increasing training sets specifically on human mGlu₇. As the training set size increases the ROC is seen to increase from 0.79 for 30% (blue), through 0.83 for 50% (yellow), to 0.88 for 70% (orange) training set size, respectively.

Figure 4

Enrichment curves showing the retrieval of known actives versus % of database searched for Janssen internal mGlu₁ (A) and mGlu₂ (B) data sets.

Figure 5

Stochastic proximity embedding (SPE) diversity map capturing the substructural diversity of the primary screening hits. Primary screen hits from PCM are shown in red, and hits from only fingerprint analogues of mGlu₇ actives are shown in blue. The plot highlights the diversity of the PCM hits (red) compared to the initial fingerprint queries (green) and the resulting fingerprint hits (blue). ECFP4 fingerprints were used as descriptors. SPE generates low-dimensional Euclidean embeddings that preserve the similarities between the chemical structures. Confirmed hits from fingerprints are molecules numbered 1 and 2 whose structures are shown in the top left, and their location in the diversity map is within the blue circle. Meanwhile, hits from PCM are numbered 3 to 6, their structures are shown in the bottom of the figure, and their locations in the diversity map are circled in red. The hits from PCM extend into a diversity space beyond those of the fingerprint queries and hits.