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Martin Vögele

Researcher at Max Planck Society

Publications -  22
Citations -  471

Martin Vögele is an academic researcher from Max Planck Society. The author has contributed to research in topics: Membrane & Diffusion (business). The author has an hindex of 8, co-authored 18 publications receiving 304 citations. Previous affiliations of Martin Vögele include University of Stuttgart & Stanford University.

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Membrane perforation by the pore-forming toxin pneumolysin

TL;DR: In atomistic and coarse-grained molecular dynamics simulations, this work resolves how pneumolysin docks to cholesterol-rich bilayers and how membrane pores form and confirms critical PLY membrane-binding sites identified previously by mutagenesis.
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Divergent Diffusion Coefficients in Simulations of Fluids and Lipid Membranes

TL;DR: The dependence of single-particle diffusion coefficients on the size and shape of the simulation box in molecular dynamics simulations of fluids and lipid membranes is investigated and it is found that the diffusion coefficients of lipids and a carbon nanotube embedded in a lipid membrane diverge with the logarithm of the box width.
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Coarse-grained simulations of polyelectrolyte complexes: MARTINI models for poly(styrene sulfonate) and poly(diallyldimethylammonium).

TL;DR: The authors' coarse-grained models are able to quantitatively reproduce previous findings like the correct charge compensation mechanism and a reduced dielectric constant of water, which can be interpreted as the underlying reason for the stability of polyelectrolyte multilayers and complexes and validate the robustness of the proposed models.
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Hydrodynamics of Diffusion in Lipid Membrane Simulations.

TL;DR: By performing molecular dynamics simulations with up to 132 million coarse-grained particles in half-micron sized boxes, it is shown that hydrodynamics quantitatively explains the finite-size effects on diffusion of lipids, proteins, and carbon nanotubes in membranes.
Posted Content

ATOM3D: Tasks On Molecules in Three Dimensions

TL;DR: This work develops three-dimensional molecular learning networks for each of these tasks, finding that they consistently improve performance relative to one- and two-dimensional methods.