Pharmaceutical Sciences
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12503/30821
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Browsing Pharmaceutical Sciences by Author "Kumari, Pratibha"
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Item An Investigation of the Allosteric Effects of Agonist and Antagonist Ligands on Sigma-1 Receptor using MD Simulation and Machine Learning Methods(2022) Kumari, Pratibha; Liu, JinPurpose: 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.Item Identification of Potential Positive Allosteric Modulators of Sigma-1 Receptor using Computational Molecular Docking and Virtual Screening(2022) Olson, Zachary Gunnar; Kumari, Pratibha; Liu, JinPurpose: Coronaviruses (such as SARS-COV-2) can achieve replication in host cells by activating pathways in the endoplasmic reticulum (ER), which causes ER stress. As it is known that the mortality rate of elderly populations in COVID-19 infection is dramatically high, indicating a vital role in the timely response of cell stress response signaling pathways in the management of the treatment of COVID-19. The sigma-1 receptor (Sig1R) is an important upstream modulator of ER stress, which regulates folding/degradation of proteins, Ca+2 homeostasis, ER stress responses, and cellular survival. Therefore, ligands enhancing Sig1R activities may improve the treatment of COVID-19 of the elderly patients. Positive Allosteric Modulators (PAM) can enhance protein activities by binding at an allosteric site. Several PAMs of Sig1R have been reported. However, the molecular basis of interactions of PAMs in Sig1R is poorly understood. Further, we do not have much information about the allosteric binding sites in Sig1R yet. Our purpose in this research is to identify possible chemical scaffolds/compounds that can bind at the allosteric sites of Sig1R and selectively elicit the activity of Sig1R. Method: In this study, we have assessed several known PAMs of Sig1R to investigate their binding affinity, the molecular basis of their interactions at three possible allosteric binding sites in Sig1R using the efficient docking suite, Glide. In addition to this, we explored ZINC and DRUG bank databases to search for compounds/chemical scaffolds that are similar to PAMs, which can be docked and engineered further to get a highly efficient drug target/PAM of Sig1R. Results: We have found that methylphenylpiracetam, SKF38393, and SCH23390 show high affinity for allosteric pockets. Further, by virtual screening of small drug-like compounds of the ZINC database in Auto Dock Vina, we obtained a list of 1000 compounds for each allosteric pocket of Sig1R. In the next step, we plan to continually refine our search by performing docking of these compounds and the compounds we obtained through ligand-based search in Glide to identify the promising set of compounds that bind efficiently at an allosteric site in Sig1R. Conclusion: Using molecular docking, we have found three compounds methylphenylpiracetam, SKF38393, and SCH23390 that bind to Sig1R at the allosteric pockets with high binding affinities and identified a list of 1000 compounds for each potential allosteric sites, shedding light on the further development of selective PAMs of Sig1R.Item Molecular Docking Study of Positive Allosteric Modulators of the Sigma-1 Receptor for the COVID treatment of elderly patients(2022) Contractor, Sareena; Kumari, Pratibha; Liu, JinPurpose: Our goal with this project is to determine conformations of ligands that have strong affinities to allosteric sites of the Sigma-1 Receptor (Sig1R). The SARS-CoV-2 strain virus is part of a group of viruses known as RNA Positive Sense Coronaviruses. The virus enters the cell through endocytosis and replicates in a cellular compartment derived for the Endoplasmic Reticulum (ER). Viral replication causes ER stress and forces the cell to adapt. The Sig1R found within the membranes of the nucleus, ER, and mitochondria. The receptor protein can change conformation to help the cell cope with ER stress. Positive Allosteric Modulators (PAMs) bind to the Sig1R and cause conformational changes that can alter its response to natural ligand binding. By targeting the Sig1R we can create therapeutic responses to the SARS-CoV-2 virus by modulating ER stress response signaling pathways for elderly patients. Method: We performed molecular docking for the Nonselective, Selective and Putative ligands of Sig1R.With molecular docking, we were able to determine the binding affinities and conformations of three types of PAMs in relation to the Sig1R. Results: The study showed that nonselective allosteric modulators have the strongest binding affinities for the Sig1R. Through molecular docking and 3D visualization, the data showed that most PAMs bind to the monomeric Sig1R at an orthosteric binding site. The only ligand to bind at a site other than the orthosteric site was SCH23390. The rest of the ligands bound to the receptor at the orthosteric site, which would indicate that they would need to compete with the receptor's natural ligands for binding. To avoid the competition, the PAMs need to bind to the receptor at a site different from their first pose, or most preferred configuration. Conclusion: This project showed us that non-selective PAMs have a higher binding affinity. We were also able to identify a need to explore other binding configurations, besides the first pose, to find allosteric binding of the PAMs to the Sig-1R.