7,841 research outputs found
Impact of Cholesterol on Voids in Phospholipid Membranes
Free volume pockets or voids are important to many biological processes in
cell membranes. Free volume fluctuations are a prerequisite for diffusion of
lipids and other macromolecules in lipid bilayers. Permeation of small solutes
across a membrane, as well as diffusion of solutes in the membrane interior are
further examples of phenomena where voids and their properties play a central
role. Cholesterol has been suggested to change the structure and function of
membranes by altering their free volume properties. We study the effect of
cholesterol on the properties of voids in dipalmitoylphosphatidylcholine (DPPC)
bilayers by means of atomistic molecular dynamics simulations. We find that an
increasing cholesterol concentration reduces the total amount of free volume in
a bilayer. The effect of cholesterol on individual voids is most prominent in
the region where the steroid ring structures of cholesterol molecules are
located. Here a growing cholesterol content reduces the number of voids,
completely removing voids of the size of a cholesterol molecule. The voids also
become more elongated. The broad orientational distribution of voids observed
in pure DPPC is, with a 30% molar concentration of cholesterol, replaced by a
distribution where orientation along the bilayer normal is favored. Our results
suggest that instead of being uniformly distributed to the whole bilayer, these
effects are localized to the close vicinity of cholesterol molecules
DenMune: Density peak based clustering using mutual nearest neighbors
Many clustering algorithms fail when clusters are of arbitrary shapes, of
varying densities, or the data classes are unbalanced and close to each other,
even in two dimensions. A novel clustering algorithm, DenMune is presented to
meet this challenge. It is based on identifying dense regions using mutual
nearest neighborhoods of size K, where K is the only parameter required from
the user, besides obeying the mutual nearest neighbor consistency principle.
The algorithm is stable for a wide range of values of K. Moreover, it is able
to automatically detect and remove noise from the clustering process as well as
detecting the target clusters. It produces robust results on various low and
high-dimensional datasets relative to several known state-of-the-art clustering
algorithms.Comment: pyMune is a Python package that implements this clustering algorithm
proposed in this paper, DenMune. It is opensource and reproducible,
doi:10.1016/j.simpa.2023.10056
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