Binding and Membrane Damage Behaviors of Self-Aggregated Beta-Amyloid Oligomers on Lipid Raft Surfaces from Microsecond Molecular Dynamics Simulations
Description
Our hypothesis is that self-aggregated Beta-Amyloid Oligomers can bind to phase-separated lipid nanodomains and damage the membrane structures. The data were generated by microsecond molecular dynamics simulations of the binding events of beta-amyloid oligomers to phase-separated lipid rafts with or without glycolipid clusters, i.e., GM-raft or CO-raft. The lipid domain preference and binding energies of disordered amyloid aggregate to highly dynamic and heterogeneous lipid bilayers in structurally specific lipid nanodomains, e.g., glycolipid-clusters, cholesterol-enriched liquid-ordered (Lo) or cholesterol-depleted liquid-disordered (Ld), or mixed Lo/Ld (Lod) region, and annular lipid shells surrounding the membrane-bound protein, provide helpful insight into guiding future experiments to understand the regulation of lipid composition and structures on amyloid binding to cell membranes. The information is also helpful for the design of drug interventions and novel imaging markers targeting membrane-bound amyloidogenic oligomers. The results will guide the new design of single-molecule experiments aiming at understanding amyloidogenic proteins binding to complex but realistic lipid membranes, containing multiple lipid components and varying domain sizes and structures. The details of the procedures of modeling and simulations of beta-amyloid oligomers in lipid rafts have been described in a research article by Pham, T. and K.H. Cheng, Exploring the Binding Kinetics and Behaviors of Self-Aggregated Beta-Amyloid Oligomers to Phase-Separated Lipid Rafts with or without Ganglioside-Clusters. Biophysical Chemistry, 2022. (https://www.sciencedirect.com/science/article/abs/pii/S0301462222001168) This work has been supported by the Robert A. Welch Foundation [W-2057-20210327], National Science Foundation [OAC 153159], National Institutes of Health [RCC1GM090897], Williams Endowment for Interdisciplinary Physics and Murchison Undergraduate Research Fellowship of Trinity University.
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Analysis methods involve selection of lipid nanodomain, lipid order parameter calculations, minimum-distance analysis and maps, protein-protein and protein-lipid binding energy and contact maps, and molecular visualization. Briefly, a data-filtering tool from GROMACS [Ref. 1], g_select, was used to select DPPC-rich Lo-domain, DLPC-rich Ld-domain, and mixed Lo/Ld or Lod domain, as well as annular lipid shells, based on the proximity of the lipid and protein atoms. A structural analysis tool from GROMACS [Ref. 1], g_order, was used to calculate the segmental orientation order of phospholipid acyl chain or cholesterol ring group within each lipid nanodomain or annular lipid shell. The minimum-distance tool from GROMACS [Ref. 1 ], g_mind, was used to determine the minimum distance between the atoms of lipid and protein vs. time, the number of atom contacts between lipid and protein vs. time, and the time-averaged minimum distance between lipid and protein atoms vs. protein residue number. The protein contact map tools from GROMACS [Ref.1], g_mdmat, and CONAN [Ref. 2] were used to create 3D Protein Residue Contact maps and GIF movies of protein in solution and on raft surfaces. A membrane structure analysis tool, FATSLiM [Ref. 3], and a python-based plotting program, MATPLOTLIB [Ref. 4], were used to calculate the area-per-lipid and thickness of the lipid bilayer and to generate 3D area-per-lipid and thickness GIF movies. Details of the analysis can be found in a research article [Ref. 5]. 1. Hess, B., et al., GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J Chem Theory Comput, 2008. 4(3): p. 435-47. 2. Mercadante, D., F. Grater, and C. Daday, CONAN: A Tool to Decode Dynamical Information from Molecular Interaction Maps. Biophys J, 2018. 114(6): p. 1267-1273. 3. Buchoux, S., FATSLiM: a fast and robust software to analyze MD simulations of membranes. Bioinformatics, 2017. 33(1): p. 133-134. 4. Hunter, J.D., Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 2007. 9(3): p. 90-95. 5. Pham, T. and K.H. Cheng, Exploring the Binding Kinetics and Behaviors of Self-Aggregated Beta-Amyloid Oligomers to Phase-Separated Lipid Rafts with or without Ganglioside-Clusters. Biophysical Chemistry, 2022. (in press).