Using multiscale molecular dynamics simulations to explore the fusion machinery underlying neurotransmitter release

Area of Science:

• Biophysics
• Neuroscience
• Computational Biology

Background:

• Neurotransmitter release relies on synaptic vesicle fusion, a process driven by SNARE proteins and accessory factors.
• Experimental methods face spatiotemporal limitations in fully elucidating fusion mechanisms.
• Molecular dynamics (MD) simulations offer high-resolution insights into these complex biological events.

Purpose of the Study:

• To review the application of all-atom (AA) and coarse-grained (CG) MD simulations in understanding SNARE-mediated membrane fusion.
• To explore the regulatory roles of Synaptotagmin and Complexin in fusion dynamics.
• To discuss current limitations and future directions, including AI applications in modeling fusion machinery.

Main Methods:

• Utilizing all-atom (AA) and coarse-grained (CG) molecular dynamics simulations.
• Examining competing hypotheses on the driving forces of SNARE-mediated fusion.
• Analyzing conserved fusion pathways, including membrane adhesion, stalk formation, and fusion pore (FP) dynamics.
• Investigating Ca²⁺-dependent interactions and the roles of specific protein domains (e.g., Synaptotagmin C2 domains, Complexin helices).

Main Results:

• MD simulations reveal a conserved fusion pathway across different models, from adhesion to FP formation.
• SNARE transmembrane domains (TMDs) and post-translational modifications like palmitoylation are critical for rapid fusion.
• Synaptotagmin and Complexin play distinct regulatory roles in controlling fusion timing and neurotransmitter release.
• Simulations provide insights into ion selectivity within fusion pores.

Conclusions:

• MD simulations are powerful tools for dissecting the spatiotemporal dynamics of membrane fusion beyond experimental resolution.
• Understanding SNARE complex regulation by accessory proteins is key to deciphering neurotransmitter release.
• Future research should focus on integrating AI for comprehensive modeling of fusion machinery and isoform-specific functions.