Monoamine transporters are sodium dependent transmembrane proteins responsible for the reuptake of neurotransmitters from the synaptic cleft. They include the transporters of serotonin, dopamine, and norepinephrine (SERT, DAT, and NET, respectively), and are presumably the most common drug targets for neurological diseases like depression, schizophrenia, and attention deficit disorder. Despite their high degree of sequence and topological conservation, these transporters show different transport efficacy and binding affinity towards distinct substrates and inhibitors. We hypothesized that the formation of disparate interaction networks for substrate recognition and necessary conformational changes for substate transport have imposed both shared and unique constraints on their sequences. While previous studies have identified structural constraints for substrate and inhibitor binding, more extensive studies considering and comparing the dynamics of monoamine transporters is lacking. To understand the structure-dynamics-function relationship of this class of transporters, we studied hSERT and hDAT using molecular dynamics and deep mutational scanning. Simulations for the import of a non-native fluorescent substrate, APP+, used in deep mutagenesis experiments suggest that substrate release is energetically more unfavorable in SERT than it is in DAT, while the opposite is true for the closure of the extracellular vestibule. Indeed, mutations that enhanced transport for were found in the intracellular vestibule of SERT while that in DAT were found instead in the intracellular and extracellular loops. Furthermore, we accurately predict whether a mutation enhances or depletes transport function by implementing interpretable machine learning on the deep mutational scanning data using features derived from molecular dynamics simulations and residue properties, thus providing a strong hypothesis for the basis of substrate specific conformational changes. Our data provides new insights into the determinants of substrate recognition and transport for this important class of drug targets.

Interpreting structure-dynamics-function relationships in monoamine transporters from high-throughput data

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