Pharmaceutical Sciences
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12503/29938
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Browsing Pharmaceutical Sciences by Author "Hayatshahi, Sayyed"
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Item Determining the binding site of Carisoprodol on GABAA receptor(2020) Liu, Jin; Huang, Renqi; Claudio, Maria; Hayatshahi, SayyedPurpose: Carisoprodol (CSP) is prescribed to treat musculoskeletal pain. CSP exerts inhibitory action on GABAA receptors (GABAA Rs) in certain concentrations. However, its binding sites remain elusive. The purpose of this study is to determine the binding site for CSP's inhibitory action on GABAA Rs. Our electrophysiological studies have shown that CSP inhibitory action is diminished by alpha1 T261F mutation of the picrotoxin (PTX) binding site. Therefore, we hypothesize that CSP shares PTX's binding site at GABAA Rs. Methods: We docked CSP on wild type alpha1beta2gamma2 and mutant alpha1(T261F)beta2gamma2 GABAA Rs using Glide program. We further performed molecular dynamics (MD) simulations of wild type and mutant GABAA Rs in unbound forms and in complex with PTX and CSP. Results: The docking reproduced the experimental pose of PTX and the effect of mutations on its binding, but could not predict the effect of the mutations on the CSP binding. However, the MD simulations showed that the local channel conformation is changed upon the mutations, and consequently, the binding of both ligands is significantly deteriorated. We further used the observed receptor-ligand interactions of CSP to predict molecular changes that would improve its binding. Conclusions: We demonstrate that the consideration of the pocket dynamics is necessary to capture the changes mutations potentially cause in GABAA Rs. The similar trend for CSP and PTX in MD simulation results validate our hypothesis that the two molecules share the same binding pocket. These data provide further information on how CSP may interact with the receptors.Item Mechanism of RNA-independent cleavage of CRISPR-Cas9(2020) Liu, Jin; Chen, Xiongping; Hayatshahi, Sayyed; Wang, Duen-ShianPurpose: CRISPR(Clustered-Regularly-interspaced-short-palindromic-repeats)-Cas9 (CRISPR-associated-protein-9) uncovered a new path toward gene therapy. However, non-specific cleavage of Cas9 raises concerns on human therapeutic applications so that it is critical to understand and minimize those non-specifics cuttings. Recent in vitro studies showed that Cas9 cleavage occurred even without the guidance from the guide RNA (gRNA) in the presence of Mn2+, implying the serious issue of off-target effect of Cas9. The purpose of this study is to elucidate the mechanism of the RNA-independent cleavage of CRISPR-Cas9, which may provide insights for the improvement of Cas9 specificity. Method: Based on our previously captured structure of catalytically-active Cas9-gRNA-dsDNA complex, we performed molecular dynamic (MD) simulations on Cas9 complexed with and without gRNA, respectively. We also compared the simulations in the presence of Mn2+vs. Mg2+. All MD simulations were performed using AMBER package with GPU acceleration. Result: We expect our MD simulations to demonstrate the different coordination environments of Mn2+ and Mg2+ in the presence or absence of gRNA, elucidating a novel mechanism for Cas9 off-target effects. Conclusion: In this study, we expect to identify the mechanism of RNA-independent cleavage of CRISPR-Cas9, shedding light on the development of new Cas9 variants to reduce off-target effects.Item The effect of phenylthiophene as an allosteric pharmacophore on the affinity of phenylpiperazine derivatives for dopamine receptors(2020) Liu, Jin; Hayatshahi, Sayyed; Amani, ArmaghanPurpose: Dopamine receptors are important therapeutic targets in treatment of many neurological diseases such as schizophrenia and Parkinson's disease. Compounds like phenylpiperazine derivatives that selectively target D3R subtype have shown significant clinical benefits. The selectivity of these compounds for D3R over D2R is shown to increase when a phenylthiophene is added as a second pharmacophores. Here, we used a computational approach to see how the second pharmacophore affects the affinity of these ligands. Method: In an effort to evaluate the difference of these compounds, docking on the crystal structure of D2R and D3R was completed using AutoDock vina after the ligands were prepared for docking with Autodock tools. Results: The binding energies from the docking poses followed expected trend that the addition of phenylthiophene compounds would increase the affinity for D3R. We further studied the binding of the more selective ligands that have 3,5-dicholoro and 3CN substitution on the phenylpiperazine to analyze the important interactions that stabilize the D3R binding. Conclusion: Studying compounds that have higher selectivity towards D3R shed light on important interactions that the phenylthiophene can have with D3R. The added phenylthiophene seems to potentially interact with Tyr7.35 and the ECL2 region in D3R. Additional investigation is needed to determine the neurological applicability of these compounds.Item Using Artificial Intelligence Algorithms to Develop Target-specific Ligands(2020) Emmitte, Kyle; Hayatshahi, Sayyed; Liu, Jin; Escobedo, Daniel; Li, Leo; Yang, YanmingPurpose: Pharmaceutical research has recently taken advantage of the rapid advancement in artificial intelligence (AI). The purpose of this study is to use AI to facilitate target-specific ligands development in drug discovery. Here, we used a machine learning algorithm to identify target-specific features of compounds to the metabotropic G-protein coupled receptors 2 and 3 (mGlu2 and mGlu3), which have been targeted for treatment of CNS disorders. Methods: A convolutional neural network (CNN) with 3 hidden layers was made using Tensorflow. We obtained data sets of 315 mGlu2 and 118 mGlu3 compounds and split them into testing and training sets. 75% of each data set was used for training and the remaining was used for testing. We fed the data sets into the CNN and ran the program over 100 iterations with each data set. Results: The neural network was able to differentiate between mGlu2 and mGlu3 compounds in the testing data set with up to a 99.1% accuracy. Visualization of the hidden layers revealed areas in the 2D images that the CNN viewed as important to distinguish the compounds. These identified chemical features can be considered as the target-specific features of the compounds. Conclusions: The neural network is able to differentiate mGlu2 and mGlu3 compounds by 2D representation alone and provide insight to distinguish target-specific features of the compounds through hidden layer visualization. Further testing through cross validation and introduction of a control data set with compounds that bind to neither receptor is needed.