Computational approaches for the study of serotonin and its membrane transporter SERT: implications for drug design...

Serotonin (5-hydroxytryptamine, 5-HT), a monoamine neurotransmitter of the central nervous and peripheral systems (CNS), plays a critical role in a wide variety of physiological and behavioral processes. In the serotonergic system, deregulation of the tightly controlled extracellular concentration of 5-HT appears to be at the origin of a host of metabolic and psychiatric disorders. A key step that regulates 5-HT external level is the re-uptake of 5-HT into cells by the 5-HT transporter (SERT), which is besides the target of numerous drugs interacting with the serotonergic system. Therapeutic strategies have mainly focused on the development of compounds that block the activity of SERT, for instance reuptake inhibitors (e.g. tricyclics, "selective" serotonin reuptake inhibitors) and in the past, specific substrate-type releasers (e.g. amphetamine and cocaine derivatives). Today, generation of new drugs targetting SERT with enhanced selectivity and reduced toxicity is one of the most challenging tasks in drug design. In this context, studies aiming at characterizing the physicochemical properties of 5-HT as well as the biological active conformation of SERT are a prerequisite to the design of new leads. However, the absence of a high-resolution 3D-structure for SERT has hampered the design of new transporter inhibitors. Using computational approaches, numerous efforts were made to shed light on the structure of 5-HT and its transporter. In this review, we compared several in silico methods dedicated to the modeling of 5-HT and SERT with an emphasis on i) quantum chemistry for study of 5-HT conformation and ii) ligand-based (QSAR and pharmacophore models) and transporter-based (homology models) approaches for studying SERT molecule. In addition, we discuss some methodological aspects of the computational work in connection with the construction of putative but reliable 3D structural models of SERT that may help to predict the mechanisms of neurotransmitter transport.