An Investigation of the Allosteric Effects of Agonist and Antagonist Ligands on Sigma-1 Receptor using MD Simulation and Machine Learning Methods

Purpose: Allosteric regulation is the control of the activity of a protein or protein complex by the binding of a ligand or effector molecule, at a site topographically distinct from the active site of the protein. The sigma-1 receptor (Sig1R), a small-ligand operated transmembrane protein, has been implicated in various neural processes such as calcium signalling, cell survival and function, inflammation, and synaptogenesis. Many small molecules act as agonist or antagonist ligands to Sig1R based on their ability to recapitulate the phenotype of receptor overexpression or knockdown, respectively. Sig1R exists in multiple oligomeric states, and agonist and antagonist are found to have a different impact on the oligomeric form of the receptor. The crystal structure of human Sig1R reveals that both agonist and antagonist ligands share the same binding pocket. However, why agonists and antagonists have distinct activities while binding to the same pocket remains unknown. It is also not clear why binding to a pocket not at the oligomer interface could allosterically affect oligomer formation of Sig1R. Our objective is to gain a molecular-level understanding of how agonist and antagonist ligands allosterically modulate the oligomer interactions differently. Method: An atomistic molecular dynamics (MD) simulation study was employed to investigate how the interface of homotrimer human Sig1R bound to agonist ((+)-pentazocine) and antagonist (PD 144418) ligands are allosterically affected. Machine learning algorithms developed by our lab were used to identify the residues that are impacted allosterically. Results: A significant decrease in the interactions between the interface residues of protomer units in agonist bound Sig1R has been found. MM/GBSA and PCA analysis reveal lowered stability of agonist-bound trimer in simulations compared to an antagonist-bound structure. The coordinated actions between the pocket and interface residues depend substantially on the type of ligands present in the binding pocket. The residue response map obtained using machine learning algorithms reflects that the properties of most of the interface residues (T141, H54, H55, G87, L111, H116, R119, A183, D188, S192, Q194, D195, and T198) are affected in different manners. Conclusion: It is shown that even though agonist and antagonist ligands bound at the same pocket, their ability to allosterically impact the interface residues is significantly different which may lead to lesser stability of high molecular weight oligomers in the agonist bound Sig1R. Our research presents a potential to collaborate MD and machine learning methods to identify the allosteric response of different ligands binding at the same pocket in protein.