We have adapted a set of classification algorithms, also known as Machine Learning, to the identification of fluid and gel domains close to the main transition of dipalmitoyl-phosphatidylcholine (DPPC) bilayers....

The waxy epicuticle of dragonfly wings contains a unique nanostructured pattern that exhibits bactericidal properties. In light of emerging concerns of antibiotic resistance, these mechano-bactericidal surfaces represent a particularly novel solution by which bacterial colonization and the formation of biofilms on biomedical devices can be prevented. Pathogenic bacterial biofilms on medical implant surfaces cause a significant number of human deaths every year. The proposed mechanism of bactericidal activity is through mechanical cell rupture; however, this is not yet well understood and has not been well characterized. In this study, we used giant unilamellar vesicles (GUVs) as a simplified cell membrane model to investigate the nature of their interaction with the surface of the wings of two dragonfly species, Austrothemis nigrescens and Trithemis annulata, sourced from Victoria, Australia, and the Baix Ebre and Terra Alta regions of Catalonia, Spain. Confocal laser scanning microscopy and cryo-scanning electron microscopy techniques were used to visualize the interactions between the GUVs and the wing surfaces. When exposed to both natural and gold-coated wing surfaces, the GUVs were adsorbed on the surface, exhibiting significant deformation, in the process of membrane rupture. Differences between the tensile rupture limit of GUVs composed of 1,2-dioleoyl-sn-glycero-3-phosphocholine and the isotropic tension generated from the internal osmotic pressure were used to indirectly determine the membrane tensions, generated by the nanostructures present on the wing surfaces. These were estimated as being in excess of 6.8 mN m–1, the first experimental estimate of such mechano-bactericidal surfaces. This simple model provides a convenient bottom-up approach toward understanding and characterizing the bactericidal properties of nanostructured surfaces.

Although cationic cell-penetrating peptides (CPPs) are able to bind to cell membranes, thus promoting cell internalization by active pathways, attachment of cargo molecules to CPPs invariably reduces their cellular uptake. We show here that CPP binding to lipid bilayers, a simple model of the cell membrane, can be recovered by designing cargo molecules that self-assemble into spherical micelles and increase the local interfacial density of CPP on the surface of the cargo. Experiments performed on model giant unilamellar vesicles under a confocal laser scanning microscope show that a family of thermally responsive elastin-like polypeptides that exhibit temperature-triggered micellization can promote temperature triggered attachment of the micelles to membranes, thus rescuing by self-assembly the cargo-induced loss of the CPP affinity to bio-membranes.

Machine Learning‐assisted Lipid Phase Analysis (MLLPA) is a new Python 3 module developed to analyze phase domains in a lipid membrane based on lipid molecular states. Reading standard simulation coordinate and trajectory files, the software first analyze the phase composition of the lipid membrane by using machine learning tools to label each individual molecules with respect to their state, and then decompose the simulation box using Voronoi tessellations to analyze the local environment of all the molecules of interest. MLLPA is versatile as it can read from multiple format (e.g., GROMACS, LAMMPS) and from either all‐atom (e.g., CHARMM36) or coarse‐grain models (e.g., Martini). It can also analyze multiple geometries of membranes (e.g., bilayers, vesicles). Finally, the software allows for training with more than two phases, allowing for multiple phase coexistence analysis.