MemSurfer - data

Data for MemSurfer: A Tool for Robust Computation and Characterization of Curved Membranes

Advances in simulation methodologies, code efficiency, and computing power have enabled larger, longer, and more-complicated biological membrane simulations. The resulting membranes can be highly complex and have curved geometries that greatly deviate from a simple planar state. Studying these membranes requires appropriate characterization of geometric and topological properties of the membrane surface before any local lipid properties, such as areas and curvatures, can be computed. We present MemSurfer, an efficient and versatile tool to robustly compute membrane surfaces for a wide variety of large-scale molecular simulations. MemSurfer works independent of the type of simulation, and directly on 3D point coordinates. As a result, MemSurfer can handle a variety of membranes. Using Delaunay triangulations and surface parameterizations, MemSurfer not only computes common lipid properties of interest, but also provides direct access to the membrane surface itself, allowing the user to potentially conceive and compute a variety of nonstandard properties. The software provides a simple-to-use Python API and is released open-source under a GPL3 license.

MemSurfer is freely available on GitHub:

Here are structure (.gro) and run parameter (.tpr) files used to generate the MemSurfer examples in the paper.

An example of a simple, three-component mixture that undergoes phase separation, taken from Carpenter et al. (1).

10us.35fs-DPPC.40-DIPC.30-CHOL.30.gro 10us.35fs-DPPC.40-DIPC.30-CHOL.30.tpr                                                                      


An example of a highly complex membrane of ~60 different lipid types. This complex mixture is representative of the lipid environment found in the mammalian brain, as examined in Ingólfsson et al. (2).  


An example of a plasma membrane (PM) tether from Baoukina et al. (3) was kindly provided by S. Baoukina and D.P. Tieleman.  This tether was pulled from the complex mammalian PM mixture described in Ingólfsson et al. (4) with the inner leaflet on the outside of the tether and outer leaflet inside the tether. 

290k-in.40us.gro 290k-in.tpr  


(1) Carpenter T.S., C.A. López, C. Neale, C. Montour, H.I. Ingólfsson, F. Di Natale, F.C. Lightstone, and S. Gnanakaran. 2018. Capturing Phase Behavior of Ternary Lipid Mixtures with a Refined Martini Coarse-Grained Force FieldJournal of Chemical Theory and Computation, 14, (11) 6050-6062.

(2) Ingólfsson, H.I, T.S. Carpenter, H. Bhatia, P.-T. Bremer, S.J. Marrink, and F.C. Lightstone. 2017. Computational lipidomics of the neuronal plasma membraneBiophysics Journal 113 (10), 2271-2280.

(3) Baoukina, S., H.I. Ingólfsson, S.J. Marrink, and D.P. Tieleman. 2018. Curvature-Induced Sorting of Lipids in Plasma Membrane Tethers. Adv. Theory Simul. 25:1800034–4.

(4) Ingólfsson, H.I., M.N. Melo, F.J. van Eerden, C. Arnarez, C.A. Lopez, T.A. Wassenaar, X. Periole, A.H. De Vries, D.P. Tieleman, and S.J. Marrink. 2014. Lipid Organization of the Plasma Membrane. JACS. 136:14554–59.