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bioRxiv · 10.1101/2020.06.18.156216

Membrane Interactions of α-Synuclein Revealed by Multiscale Molecular Dynamics Simulations, Markov State Models, and NMR

Abstract

-Synuclein is a presynaptic protein that binds to cell membranes and is linked to Parkinsons disease (PD). Whilst the normal function of remains -synuclein remains uncertain, it is thought that oligomerization of the protein on the cell membrane contributes to cell damage. Knowledge of how -synuclein binds to lipid bilayers is therefore of great interest as a likely first step in the molecular pathophysiology of PD, and may provide insight of the phenotype of PD-promoting mutations. We use coarse-grained and atomistic simulations in conjunction with NMR and cross-linking mass spectrometry studies of -synuclein bound to anionic lipid bilayers to reveal a break in the helical structure of the NAC region, which may give rise to subsequent oligomer formation. Coarse-grained simulations of -synuclein show that the interhelical region leads recognition and binding to both POPG and mixed composition bilayers and identifies important protein-lipid contacts, including those in the region between the two helices in the folded structure. We extend these simulations with all-atom simulations of the initial binding event to reveal details of the time-progression of lipid binding. We present secondary structure analysis that reveals points of possible {beta}-strand formation in the structure, and investigate intramolecular contacts with simulations and mass-spectrometry crosslinking. Additionally we show how Markov state models can be used to investigate possible conformational changes of membrane bound -synuclein in the NAC region, and we extract representative structures. These structural insights will aid the design and development of novel therapeutic approaches.

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BibTeXRIS

Amos, S.-B., Schwarz, T., Shih, J., Cossins, B., Baker, T., Taylor, R., Konrat, R., Sansom, M.. 2020-06-18. Membrane Interactions of α-Synuclein Revealed by Multiscale Molecular Dynamics Simulations, Markov State Models, and NMR. https://doi.org/10.1101/2020.06.18.156216

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