Defense Date

11-12-2012

Graduation Date

Spring 2013

Availability

Immediate Access

Submission Type

thesis

Degree Name

MS

Department

Pharmacology

School

School of Pharmacy

Committee Chair

Christopher Surratt

Committee Member

Jeffry Madura

Committee Member

Jane Cavanaugh

Keywords

Antidepressant, Binding, Computational model, Mutagenesis, Serotonin, SERT

Abstract

A major obstacle for developing new antidepressants has been limited knowledge of the structure and function of a central target, the serotonin transporter (SERT). Established SERT inhibitors (SSRIs) were docked to an in silico SERT model to identify likely binding pocket amino acid residues. When mutated singly, no one of five implicated residues was critical for high affinity in vitro binding of SSRIs or cocaine. The in silico SERT model was used in ligand virtual screening (VS) of a small molecule structural library. Selected VS "hit" compounds were procured and tested in vitro; encouragingly, two compounds with novel structural scaffolds bound SERT with modest affinity. The combination of computational modeling, site-directed mutagenesis and pharmacologic characterization can accelerate binding site elucidation and the search for novel lead compounds. Such compounds may be tailored for improved serotonin receptor selectivity and reduced affinity for extraneous targets, providing superior antidepressants with fewer adverse effects.

Format

PDF

Language

English

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