In approaching the challenge of predicting novel MCHR1 antagonists, given the absence of available crystal structures, a multi-faceted strategy is essential. The first hurdle is to establish an accurate model of MCHR1, which can be achieved through either homology modeling or utilizing the structure from AlphaFold. To ensure robustness, both methods would be employed, and the resulting structures compared to gauge their accuracy relative to other G protein-coupled receptors (GPCRs).
Following model selection, the next step involves employing the induced fit docking protocol in Schrödinger Maestro. This would entail docking ten known antagonists to the orthosteric binding site of MCHR1 and choosing the optimal pose for each ligand. Subsequently, a high-throughput virtual screen for each of the ten receptor conformations would be conducted, drawing compounds from the Zinc22 database. Given the tendency for antagonists to exceed 500 Da, a criterion for ligand selection would be set, taking into account that compounds over 550 Da would be filtered out by Zinc22. Hence, ligands 550 Da and below will be downloaded for further analysis.
Upon completion of the high-throughput screen, ligands with a Glide score of -8 or more positive would be excluded. The remaining ligands would undergo a subsequent evaluation using Glide SP in Schrödinger Maestro, incorporating the known MCHR1 antagonists as internal controls across all ten receptor conformations. Cutoff scores for Glide SP would be determined based on the highest-ranking score from the control ligands, discarding all ligands that fall below this threshold.
The final step involves conducting a round of Glide XP with expanded sampling. This would be carried out once again, utilizing all control ligands. Ligands failing to outperform the best-ranking control ligand would be discarded. The ultimate selection of ligands would involve identifying those that consistently overlap across all ten screenings, ensuring a comprehensive and robust set of potential MCHR1 antagonists.
