Human serotonin transporter (hSERT), a membrane protein from the neurotransmitter sodium symporter family, is implicated in depression disorder and has been the primary target of antidepressant discovery research for several decades. Since the currently available antidepressants may cause adverse effects and have several limitations, novel drugs are highly desired. However, the efforts to develop better therapeutics are hampered by the lack of a crystal structure of hSERT. Knowledge of the binding site of the drug and its orientation in the protein is crucial in structure-based drug discovery. We employed a novel computational protocol comprised of active site detection, docking, scoring, molecular dynamics simulations, and absolute binding free energy (ABFE) calculations to elucidate the binding site and the binding mode of a dual hSERT/5HT-1A blocker SSA-426 and our in-house hSERT inhibitor DJLDU-3-79 in hSERT. Through this approach, we propose that both of these inhibitors bind in the S1 pocket of hSERT and in a similar orientation. This disproves the earlier hypothesis that both these inhibitors bind in the S2 site; however, we are in agreement with the earlier hypothesis that both of the ligands orient similarly. Further, we resolved the ambiguity in binding energies and binding trends of the tricyclic antidepressant drugs clomipramine, imipramine, and desipramine with leucine transporter (LeuT) (a bacterial homologue of hSERT) through relative binding free energy (RBFE) calculations. Based on our RBFE results, we proposed that clomipramine should have the highest affinity for LeuT, followed by imipramine and desipramine. Finally, to achieve accuracy in binding energy estimations and to perform all CHARMM simulations, we developed CHARMM general force field parameters (CGenFF) for fifteen monoamine transporter ligands. / Bayer School of Natural and Environmental Sciences; / Chemistry and Biochemistry / PhD; / Dissertation;
| Identifer | oai:union.ndltd.org:DUQUESNE/oai:digital.library.duq.edu:etd/197230 |
| Date | 18 May 2016 |
| Creators | Immadisetty, Kalyan |
| Contributors | Jeffry Madura, Jeffrey Evanseck, David Seybert, Christopher Surratt |
| Source Sets | Duquesne University |
| Detected Language | English |
| Rights | Worldwide Access; |
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