Molecular mechanisms of neurotransmitter transport in neurons

Chan, Matthew C

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https://hdl.handle.net/2142/117611 Copy

Description

Title

Molecular mechanisms of neurotransmitter transport in neurons

Author(s)

Chan, Matthew C

Issue Date

2022-10-04

Director of Research (if dissertation) or Advisor (if thesis)

Shukla, Diwakar

Doctoral Committee Chair(s)

Shukla, Diwakar

Committee Member(s)

• Procko, Erik
• Higdon, Jonathan
• Zhao, Huimin

Department of Study

Chemical & Biomolecular Engr

Discipline

Chemical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Molecular Dynamics
• Markov State Models
• Membrane Transporter
• Neurotransmitter
• Slc6

Language

eng

Abstract

Neurotransmitters are endogenous signaling molecules responsible for enabling chemical communication between neurons throughout the nervous system with implications on many physiological functions such as mood, behavior, cognition, appetite, and sleep. During neurotransmission, the concentration of neurotransmitters in the synaptic cleft is tightly regulated though specialized membrane transport proteins that initiate the reuptake of the extracellular neurotransmitters into the presynaptic neuron thereby terminating the neuronal signaling process. One class of neurotransmitter transporters, and the focus of this Thesis, is the neurotransmitter:sodium symporter (NSS) family and includes proteins that transport a variety of neurotransmitters such as serotonin, dopamine, glycine, and gamma-aminobutyric acid. Other members of the NSS family transport nutrient amino acids, osmolytes, and metabolites. While substantial biochemical characterization of these transporters have provided the molecular basis of transport activity and inhibition, the understanding of the conformational dynamics associated with these molecular functions and between intermediate states is not maintained. Furthermore, despite the recent surge of structural data and resolution of many eukaryotic NSS transporters, static conformations cannot solely provide the mechanistic basis of the ion-substrate coupling transport process nor the structural consequences of post-translational modifications and other regulatory mechanisms. Deemed as a computational microscope, molecular dynamics (MD) simulations have emerged as an unrivaled biophysical method to characterize protein structure and dynamics at full atomistic resolution. The research presented in this Thesis focuses on the synergic use of computational and experimental techniques to elucidate the molecular mechanisms and regulation of neurotransmitter transport in the NSS family. The first part of the Thesis details a suite of large-scale MD simulations, Markov state modeling, and deep mutational scanning to characterize the substrate import mechanism of two closely related NSS members: the serotonin transporter, SERT, and the dopamine transporter, DAT. Using a parallel sampling scheme known as adaptive sampling, the conformational landscape of the two transporters in the presence of different substrates was exhaustively explored, totaling in over 2.48 milliseconds of aggregated simulation data. Markov state models were constructed from the simulation data to delineate the thermodynamic free energy barriers and kinetics between conformational states. Furthermore, the intermediate states and structural dynamics involved in the substrate import process were identified. To compliment the atomistic view of the substrate import, deep mutational scans of selected for the uptake of the fluorescent neurotransmitter analogue APP+ was performed on both SERT and DAT. Hundreds of gain-of-function mutations for APP+ transport were identified along the substrate permeating pathway in SERT, with most of these mutations were notably located in the intracellular vestibule. In contrast, the mutations in the intracellular exit pathway of DAT, and throughout the remainder of the transporter were generally deleterious for transport. Together, the simulations and deep mutagenesis support a distinct difference in the conformational equilibria of states and associated energetics between these two structurally and sequentially similar transporters. The second part of the Thesis examines the molecular regulatory mechanisms of NSS transporter and characterizes its effect on the transporter structure and conformational dynamics. Microsecond-long timescale simulations identified how phosphorylation of the serotonin transporter induced a structural rearrangement of the intracellular hydrogen bonding network that decreases the free energy barrier to form the inward-facing state for neurotransmitter release. Additionally, the effects of N-linked glycosylation was systemically explored for four NSS transporters: SERT, DAT, the glycine transporter GlyT1, and the neutral amino acid transporter B\textsuperscript{0}AT1. These simulations revealed that glycosylation does not significantly affect transporter structure, but un-uniformally alters the dynamics of the glycosylated loop. In all, these studies provide an atomistic view into the in vivo regulation, and dysregulation, mechanism of the NSS family. The remainder of the Thesis details the design and implementation of a machine learning algorithm to predict the effect of variants from deep mutational datasets. By training a model on the deep mutational scan on one protein, the co-evolutionary coupling of residue pairs can be transferred to a homologous protein to obtain an in silico mutational scan. The use of resulting method, named TLmutation, is also described to predict higher order mutations of the angiotensin converting enzyme 2 for the design of a high-affinity soluble decoy as an alternative therapeutic for SARS-CoV-2 infection. The Thesis concludes with an auxiliary study investigating the substrate transport mechanism of the cyanobacteria bicarbonate transporter BicA and a brief outlook of the research.

Graduation Semester

2022-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/117611

Copyright and License Information

Copyright 2022 Matthew Chan

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