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Phys Chem Chem Phys. 2018 Nov 28;20(46):29513-29527. doi: 10.1039/c8cp06232c.

Computational characterization of the selective inhibition of human norepinephrine and serotonin transporters by an escitalopram scaffold.

Author information

1
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China. zhufeng@zju.edu.cn prof.zhufeng@gmail.com.

Abstract

Human norepinephrine and serotonin transporters (hNET and hSERT) are closely related monoamine transporters (MATs) that regulate neurotransmitter signaling in neurons and are primary targets for a wide range of therapeutic drugs used in the treatment of mood disorders. The subtle modifications of an escitalopram scaffold exhibit distinct selective inhibition profiles of hNET and hSERT. However, the structural details of escitalopram scaffold binding to hSERT and (or) hNET are poorly understood and still remain a great challenge. In this work, on the basis of more recently solved X-ray crystallographic structure of hSERT in complex with escitalopram, 3 μs long all-atom MD simulations and binding free energy calculations via MM/GB(PB)SA, thermodynamic integration (TI) and MM/3D-RISM methods were performed to reproduce experimental free energies. And both MM/GBSA and TI have a high correlation coefficient (R2 = 0.95 and 0.96, respectively) between the relative binding free energies of the calculated and experimental values. Furthermore, MM/GBSA per-residue energy decomposition, molecular interaction fingerprints and thermodynamics-structure relationship analysis were employed to investigate and characterize the selectivity of the escitalopram scaffold with three modifications (escitalopram, ligand10 and talopram) to hNET and hSERT. As a result, 4 warm spots (A73, Y151, A477 and I481) in hNET and 4 warm spots (A96, A173, T439 and L443) in hSERT were thus discovered to exert a pronounced effect on the selective inhibition of hNET and hSERT by the studied ligands. These simulation results would provide great insight into the design of inhibitors with the desired selectivity to hNET and hSERT, thus further promoting the research of more efficacious antidepressants.

PMID:
30457616
DOI:
10.1039/c8cp06232c
[Indexed for MEDLINE]

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