Elucidation of partial activation of cannabinoid receptor type 2 and identification of potential partial agonists: Molecular dynamics simulation and structure-based virtual screening

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

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.

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning

The endocannabinoid system, which includes cannabinoid receptor 1 and 2 subtypes (CB1R and CB2R, respectively), is responsible for the onset of various pathologies including neurodegeneration, cancer, neuropathic and inflammatory pain, obesity, and inflammatory bowel disease. Given the high similarity of CB1R and CB2R, generating subtype-selective ligands is still an open challenge. In this work, the Cannabinoid Iterative Revaluation for Classification and Explanation (CIRCE) compound prediction platform has been generated based on explainable machine learning to support the design of selective CB1R and CB2R ligands. Multilayer classifiers were combined with Shapley value analysis to facilitate explainable predictions. In test calculations, CIRCE predictions reached ∼80% accuracy and structural features determining ligand predictions were rationalized. CIRCE was designed as a web-based prediction platform that is made freely available as a part of our study.

Computational analysis of drug resistance of taxanes bound to human β-tubulin mutant (D26E)

The single-point mutation D26E in human β-tubulin is associated with drug resistance seen with two anti-mitotic taxanes (paclitaxel and docetaxel) when used to treat cancers. The molecular mechanism of this resistance remains elusive. However, docetaxel and a third-generation taxane, cabazitaxel, are thought to overcome this resistance. Here, structural models of both the wildtype (WT) and D26E mutant (MT) human β-tubulin were constructed based on the crystal structure of pig β-tubulin in complex with docetaxel (PDB ID: 1TUB). The three taxanes were docked into the WT and MT β-tubulin, and the resulting complexes were submitted to three independent runs of 200 ns molecular dynamic simulations, which were then averaged. MM/GBSA calculations revealed the binding energy of paclitaxel with WT and MT β-Tubulin to be -101.5 ± 8.4 and -90.4 ± 8.9 kcal/mol, respectively. The binding energy of docetaxel was estimated to be -104.7 ± 7.0 kcal/mol with the WT and -103.8 ± 5.5 kcal/mol with the MT β-tubulin. Interestingly, cabazitaxel was found to have a binding energy of -122.8 ± 10.8 kcal/mol against the WT and -106.2 ± 7.0 kcal/mol against the MT β-tubulin. These results show that paclitaxel and docetaxel bound to the MT less strongly than the WT, suggesting possible drug resistance. Similarly, cabazitaxel displayed a greater binding propensity against WT and MT β-tubulin than the other two taxanes. Furthermore, the dynamic cross-correlation matrices (DCCM) analysis suggests that the single-point mutation D26E induces a subtle dynamical difference in the ligand-binding domain. Overall, the present study revealed how the single-point mutation D26E may reduce the binding affinity of the taxanes, however, the effect of the mutation does not significantly affect the binding of cabazitaxel.

Binding of a positive allosteric modulator CDPPB to metabotropic glutamate receptor type 5 (mGluR5) probed by all-atom molecular dynamics simulations

Positive allosteric modulators (PAMs) of metabotropic glutamate receptor type 5 (mGluR5) potentiate positive receptor response and may be effective for the treatment of schizophrenia and cognitive disorders. Although crystal structures of mGluR5 complexed with the negative allosteric modulators (NAMs) are available, no crystal structure of mGluR5 complexed with PAM has been reported to date. Thus, conformational changes associated with the binding of PAMs to mGluR5 remain elusive. Here, a PAM CDPPB, and two NAMs MTEP and MFZ10-7 used as a negative control, were docked to the crystal structure. The docked complexes were submitted to molecular dynamics simulations to examine the activation of the PAM system. An MM/GBSA binding energy calculation was performed to estimate binding strength. Furthermore, molecular switch analysis was done to get insights into conformational changes of the receptor. The PAM CDPPB displays a stronger binding affinity for mGluR5 and induces conformational changes. Also, a salt bridge between TM3 and TM7, corresponding to the ionic lock switch in class A GPCRs is found to be broken. The PAM-induced receptor conformation is more like the agonist-induced conformation than the antagonist-induced conformation, suggesting that PAM works by inducing conformation change and stabilizing the active receptor conformation.