Репозиторий Университета

© 2020 The Author(s). Published by IOP Publishing Ltd Printed in the UK Graphene-based nano-porous materials (GNM) are potentially useful for all those applications needing a large specific surface area (SSA), typical of the bidimensional graphene, yet realized in the bulk dimensionality. Such applications include for instance gas storage and sorting, catalysis and electrochemical energy storage. While a reasonable control of the structure is achieved in micro-porous materials by using nano-micro particles as templates, the controlled production or even characterization of GNMs with porosity strictly at the nano-scale still raises issues. These are usually produced using dispersion of nano-flakes as precursors resulting in little control on the final structure, which in turn reflects in problems in the structural model building for computer simulations. In this work, we describe a strategy to build models for these materials with predetermined structural properties (SSA, density, porosity), which exploits molecular dynamics simulations, Monte Carlo methods and machine learning algorithms. Our strategy is inspired by the real synthesis process: starting from randomly distributed flakes, we include defects, perforation, structure deformation and edge saturation on the fly, and, after structural refinement, we obtain realistic models, with given structural features. We find relationships between the structural characteristics and size distributions of the starting flake suspension and the final structure, which can give indications for more efficient synthesis routes. We subsequently give a full characterization of the models versus H2 adsorption, from which we extract quantitative relationship between the structural parameters and the gravimetric density. Our results quantitatively clarify the role of surfaces and edges relative amount in determining the H2 adsorption, and suggest strategies to overcome the inherent physical limitations of these materials as adsorbers. We implemented the model building and analysis procedures in software tools, freely available upon request.

© 2020 Elsevier B.V. This work proposes an original method for generating atomistic models of multi-branched and arbitrary-shaped seamless junctions of different nanostructures. Atomic frameworks of new hybrid systems with a wide variety of topological forms based on 1D and 2D structures can be obtained using this method. The topological diversity of generated hybrid systems is provided by the some features of the developed method. This method combines a triangulated nanomesh framework generation with a molecular dynamics (MD) method that allows us to generate dozens of topological configurations of the contact region of different objects. Energetically favorable junctions of carbon nanostructures, including Y- and X-shaped junctions of carbon nanotubes, a fullerene-nanotube junction, and a fullerene-graphene hybrid system are created using the developed original method.

© 2020 Elsevier B.V. Amphiphilic maleic acid-containing copolymers account for a recent methodical breakthrough in the study of membrane proteins. Their application enables a detergent-free extraction of membrane proteins from lipid bilayers, yielding stable water-soluble, discoidal lipid bilayer particles with incorporated proteins, which are wrapped with copolymers. Although many studies confirm the potential of this approach for membrane protein research, the interactions between the maleic acid-containing copolymers and extracted lipids, as well as possible effects of the copolymers on lipid-embedded proteins deserve further scrutinization. Here, we combine electron paramagnetic resonance spectroscopy and coarse-grain molecular dynamics simulations to compare the distribution and dynamics of lipids in lipid particles of phospholipid bilayers encased either by an aliphatic diisobutylene/maleic acid copolymer (DIBMALPs) or by an aromatic styrene/maleic acid copolymer (SMALPs). Nitroxides located at the 5th, 12th or 16th carbon atom positions in phosphatidylcholine-based spin labels experience restrictions of their reorientational motion depending on the type of encasing copolymer. The dynamics of the lipids was less constrained in DIBMALPs than in SMALPs with the affinity of spin labeled lipids to the polymeric rim being more pronounced in SMALPs.

© Copyright © 2019 Delfino, Porozov, Stepanov, Tamazian and Tozzini. Transitions between different conformational states are ubiquitous in proteins, being involved in signaling, catalysis, and other fundamental activities in cells. However, modeling those processes is extremely difficult, due to the need of efficiently exploring a vast conformational space in order to seek for the actual transition path for systems whose complexity is already high in the stable states. Here we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to Calmodulin, based on an empirical force field with a partial structural bias, to explore the transition paths between the apo-closed state and the Ca-bound open state of the protein. We then select representative structures along the trajectory based on a structural clustering algorithm and build a cleaned-up trajectory with them. We finally compare this trajectory with that produced by the online tool MinActionPath, by minimizing the action integral using a harmonic network model, and with that obtained by the PROMPT morphing method, based on an optimal mass transportation-type approach including physical constraints. The comparison is performed both on the structural and energetic level, using the coarse-grained and the atomistic force fields upon reconstruction. Our analysis indicates that this method returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multi-stable proteins transition pathways.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Sterols change the biophysical properties of lipid membranes. Here, we analyzed how sterols affect the activity of widely used antimicrobial membrane-active compounds, sodium dodecyl sulfate (SDS) and benzalkonium chloride (BAC). We also tested a novel benzalkonium-like substance, Kor105. Our data suggest that benzalkonium and Kor105 disturb the ordering of the membrane lipid packaging, and this disturbance is dampened by cholesterol. The disturbance induced by Kor105 is stronger than that induced by BAC because of the higher rigidity of the Kor105 molecule due to a shorter linker between the phenyl group and quaternary nitrogen. On the contrary, individual SDS molecules do not cause the disturbance. Thus, in the tested range of concentrations, SDS-membrane interaction is not influenced by cholesterol. To study how sterols influence the biological effects of these chemicals, we used yeast strains lacking Lam1-4 proteins. These proteins transport sterols from the plasma membrane into the endoplasmic reticulum. We found that the mutants are resistant to BAC and Kor105 but hypersensitive to SDS. Together, our findings show that sterols influence the interaction of SDS versus benzalkonium chloride and Kor105 with the membranes in a completely different manner.

© The Author 2018. SLURP-1 is a member of three-finger toxin-like proteins. Their characteristic feature is a set of three beta strands extruding from hydrophobic core stabilized by disulfide bonds. Each betastrand carries a flexible loop, which is responsible for recognition. SLURP-1 was recently shown to act as an endogenous growth regulator of keratinocytes and tumor suppressor by reducing cell migration and invasion by antagonizing the pro-malignant effects of nicotine. This effect is achieved through allosteric interaction with α7 nicotinic acetylcholine receptors (alpha-7 nAChRs) in an antagonist-like manner. Moreover, this interaction is unaffected by several well-known agents specifically alpha-bungarotoxin. In this work, we carry out the conformational analysis of the SLURP-1 by a microsecond-long full-atom explicit solvent molecular dynamics simulations followed by clustering, to identify representative states. To achieve this timescale we employed a GPU-accelerated version of GROMACS modeling package. To avoid human bias in clustering we used a non-parametric clustering algorithm Affinity Propagation adapted for biomolecules and HPC environments. Then, we applied protein-protein molecular docking of the ten most massive clusters to α7-nAChRs in order to test if structural variability can affect binding. Docking simulations revealed the unusual binding mode of one of the minor SLURP-1 conformations.