Cross-species computational analysis of selenium-based compound interactions with serotonin transporter (SERT) orthologs

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Authors: Mustafa Munir Mustafa Dahleh, Gustavo Petri Guerra, Marina Prigol
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Abstract

Serotonin transporter (SERT) are key regulators of serotonergic neurotransmission and fundamental to the control of mood and emotions. Selenium-based compounds, known for their neuroactive properties, have emerged as promising candidates for modulating antidepressant and anxiolytic actions. Six SERT orthologs, from invertebrates to mammals, were analyzed using phylogenetic reconstruction, cavity mapping, molecular docking, and dynamics, with serotonin, fluoxetine, and selenium-based derivatives, including ebselen (EbSe), diphenyl diselenide ((PhSe)2), p-chloro-diphenyl diselenide ((p-ClPhSe)2), and m-trifluoromethyl-diphenyl diselenide ((m-CF3-PhSe)2). Mammalian SERT conserves polar and aromatic residues critical for hydrogen-bonding and π-stacking, whereas invertebrate orthologs show substitutions that alter cavity polarity and electrostatic complementarity. Selenium-based derivatives with electron-withdrawing groups (CF3, Cl) outperformed endogenous and reference ligands by forming halogen bonds, directional hydrogen bonds, and cation–π interactions. Unsubstituted derivatives relied on π–π and dispersion forces, remaining stable mainly in mammalian SERTs, while EbSe introduced alternative chalcogen-bonding interactions. Molecular dynamics simulations revealed species-dependent behavior in which ligands were variably stable in invertebrate transporters but consistently stable in vertebrate ones, reflecting the conservation of key interaction residues in vertebrates and substitutions that alter engagement in invertebrate orthologs. While (p-ClPhSe)2 also showed favorable interaction with SERT, the trifluoromethyl-substituted derivative (m-CF3-PhSe)2 exhibited markedly superior stability and binding affinity, standing out as the most promising SERT modulator among the tested compounds.

Publication details

Journal / source
Figshare
Publication date
1 January 2026
Document type
Dataset