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127 results for “Self-assembly”
Research data supporting "Residue-Specific Solvation Directed Thermodynamic and Kinetic Control over Peptide Self-Assembly with 1D/2D Structure Selection"
<p>Experimental research raw data supporting the publication by Lin, Y. et al, 2019, "Residue-Specific Solvation Directed Thermodynamic and Kinetic Control over Peptide Self-Assembly with 1D/2D Structure Selection", ACS Nano. DOI: 10.1021/acsnano.8b08117.</p> <p>Molecular simulation data is available upon reasonable request from irene.yarovsky@rmit.edu.au.</p>
Dataset for the numerical simulation in the article "Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network"
<p>This dataset includes the essential source code and the corresponding numerical data for the self-assembly of a M6L4 square-based pyramid (SP) complex. </p> <p>The associated study is described in </p> <p><strong>"Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network"</strong>, by T. Abe, S. Takahashi, H. Sato, and S. Hiraoka.</p>
Self-assembled molecules for hole extraction in efficient inverted PbS quantum dot solar cells
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Cartesian Coordinates of Conformational Changes and Coordination Stability of Flexible Tripeptides During Ni(II)-Mediated Self-Assembly
<p>Cartesian Coordinates of Conformational Changes and Coordination Stability of Flexible Tripeptides During Ni(II)-Mediated Self-Assembly</p>
Molecular modeling of self-assembling peptides MELD structures
<p>Top 10 MELD structures for each system used in the "Molecular modeling of self-assembling peptides" paper.</p>
Aqueous self-assembly of a wide range of sophorolipid and glucolipid microbial bioamphiphiles (biosurfactants): considerations about the structure-properties relationship
<p><em>Hypothesis</em></p> <p>Sophorolipids are well-known scaled-up microbial glycolipid biosurfactants with strong commercialization potential for their biological origin, mildness compared to classical surfactants. However, their properties are still poorly understood, they cannot be predicted and their behaviour in solution challenges half a century of knowledge generated in the field of surfactant science. By studying forty different types of sophorolipids, this work contributes to better tackle their structure-property relationship and identify which chemical groups in their molecular structure have a critical influence towards their self-assembly properties in water.</p> <p> </p> <p><em>Experiments</em></p> <p>This work explores the self-assembly properties at room temperature of sophorolipids and sophorosides in water using small angle X-ray scattering (SAXS), optical and cryogenic transmission electron microscopy (cryo-TEM). Structural features like the number of sugar headgroups, acetylation, end-chain functional group, (un)saturation, lactonization and length of chain are varied both to rationalize their impact and to understand their effect on the self-assembly.</p> <p> </p> <p><em>Findings</em></p> <p>The number of sugar groups, pH, (un)saturation and lactonization were found to have a critical impact in respect to the sophorolipids self-assembly. The chemical nature of the end-chain functional group and the chain length were found to have a possibly critical impact, depending on the specific type of chemical function (COOH and long chains are critical). Mono- and diacetylation, as well as position of sophorose on the fatty acid (ω, ω-1), are not critical, i.e., do not significantly influence the sophorolipids self-assembly.</p> <p><em>Hypothesis</em></p> <p>Sophorolipids are well-known scaled-up microbial glycolipid biosurfactants with strong commercialization potential for their biological origin, mildness compared to classical surfactants. However, their properties are still poorly understood, they cannot be predicted and their behaviour in solution challenges half a century of knowledge generated in the field of surfactant science. By studying forty different types of sophorolipids, this work contributes to better tackle their structure-property relationship and identify which chemical groups in their molecular structure have a critical influence towards their self-assembly properties in water.</p> <p> </p> <p><em>Experiments</em></p> <p>This work explores the self-assembly properties at room temperature of sophorolipids and sophorosides in water using small angle X-ray scattering (SAXS), optical and cryogenic transmission electron microscopy (cryo-TEM). Structural features like the number of sugar headgroups, acetylation, end-chain functional group, (un)saturation, lactonization and length of chain are varied both to rationalize their impact and to understand their effect on the self-assembly.</p> <p> </p> <p><em>Findings</em></p> <p>The number of sugar groups, pH, (un)saturation and lactonization were found to have a critical impact in respect to the sophorolipids self-assembly. The chemical nature of the end-chain functional group and the chain length were found to have a possibly critical impact, depending on the specific type of chemical function (COOH and long chains are critical). Mono- and diacetylation, as well as position of sophorose on the fatty acid (ω, ω-1), are not critical, i.e., do not significantly influence the sophorolipids self-assembly.</p>
Patchy particle insights into self-assembly of transparent, graded index squid lenses
<p>Squids have spherical, gradient index lenses that maximize optical sensitivity while minimizing light scattering and geometric aberration. Previous studies have shown that the constituent lens proteins behave like patchy particles, and that a density gradient of packing fraction $\sim 0.01$ to $1$ assembles from a gradient of average particle valence, $\langle M\rangle$ $\approx 2.1$ to $\langle M\rangle$ $>6$. A priori, transparency requires that all regions within the larger gradient must minimize density fluctuations at length scales close to the wavelength of visible light. It is not known how a material can achieve this at all possible packing fractions via attractive interactions. We also observe that the set of proteins making the lens is remarkably polydisperse (there are around 40 isoforms expressed). Why does nature employ so many geometrically similar isoforms when theory suggests a few would suffice, and what, if any, is the physical role of the polydispersity? This study focuses on answering these questions for the sparsest regions of the lens, where the patchy nature of the system will have the largest influence on the final structure. We first simulated mixtures of bi- and trivalent patchy particles and found a strong influence of patch angle on the percolation and gel structure of the system. We then investigated the influence of the interaction polydispersity on the structure of the $M=2.1$ system. We find that increasing the variance in patch energies and single-patch angle appears decrease the length scale of density fluctuations while also moving the percolation line to lower temperature. S-crystallin geometry and polydispersity appear to contribute to promoting regular percolation of a gel structure while also limiting density fluctuations to small length scales, thereby promoting transparency in the annealed structure.</p>
Isothermal self-assembly of multicomponent and evolutive DNA nanostructures
<p>This dataset contains the cadnano files of DNA origamis used in this work as well as all original AFM images used for the data analyses shown in Fig. 1C, Fig. 2A, and Fig. 5.</p>
Lateral interactions govern self-assembly of the bacterial biofilm matrix protein BslA (experimental and simulation data)
<p>The soil bacterium Bacillus subtilis is a model organism to investigate the formation of biofilms, the predominant form of microbial life. The secreted protein BslA self-assembles at the surface of the biofilm to give the B. subtilis biofilm its characteristic hydrophobicity. To understand the mechanism of BslA self-assembly at interfaces, here we built a molecular model based on the previous BslA crystal structure and the newly determined crystal structure of the BslA paralogue YweA. Our analysis revealed two conserved protein-protein interaction interfaces supporting BslA self-assembly into an infinite 2d lattice that fits previously determined transmission microscopy images. Molecular dynamics simulations and in vitro protein assays further support our model of BslA elastic film formation, while mutagenesis experiments highlight the importance of the identified interactions for biofilm structure. Based on this knowledge, YweA was engineered to form more stable elastic films and rescue biofilm structure in bslA deficient strains. These findings shed new light on protein film assembly and will inform the development of BslA technologies which range from surface coatings to emulsions in fast-moving consumer goods.</p>
Artificial cell synthesis using biocatalytic polymerisation-induced self-assembly
<p>These are the raw data underlying the pre-print Synthesis of artificial cells via biocatalytic polymerisation-induced self-assembly <a href="https://doi.org/10.26434/chemrxiv-2023-c3nhg">10.26434/chemrxiv-2023-c3nhg</a> , to be published in Nature Chemistry (with the updated title Artificial cell synthesis using biocatalytic polymerisation-induced self-assembly)</p>
Data from: Gramicidin and chlorhexidine encapsulated in bicontinuous microemulsions: Antimicrobial activity performance and their impact on self-assembly
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Improved small-angle x-ray scattering of nanoparticle self-assembly using a cell with a flat liquid surface
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Patchy particle insights into self-assembly of transparent, graded index squid lenses
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Data from: Facile fabrication of fluoro-polymer self-assembled ZnO nanoparticles mediated, durable and robust omniphobic surfaces on polyester fabrics
<p>Omniphobic surfaces have been widely used in many applications, especially due to their self-cleaning property. Omniphobicity of a surface is directly interpreted by measuring the contact angle that it makes with a liquid of interest. In this study, polyester fabric was made omniphobic with a measured water contact angle (WCA) of 152° by reducing the surface free energy of the fabric surface via the polymerization of 3,3,4,4,5,5,6,6,7,7,8,8,8-tridecafluorooctyl methacrylate(TDM) on a ZnO seed layer. The fabrics with and without the seed layer were characterized using various analytical techniques. The WCA of the fabric with the seed layer was 153° compared to 142° of the fabric without the seed layer. The treated fabric made a contact angle of 132° with SAE 40 motor oil indicating its oleophobicity while non-treated fabric made no contact angle. In addition to that, the treated fabric is omniphobic against milk tea, coffee, coconut oil, and ethanol. The morphological analysis using scanning electron microscopy (SEM) of the treated and non-treated fabrics revealed that the particle size of the seed layer applied fabric was ranging from 100-300 nm and upon the TDM application it became less than 100 nm. Elemental analysis by EDS showed the presence of fluorine and FT-IR analysis confirmed the polymerization of TDM. The polymerization of fluoropolymer was further confirmed by TGA and DSC analyses. The contact angle of the surface-modified fabric remained unchanged even after 1.5 h washing and 50 cycles of abrasion. The modified fabric is robust and no change was observed in the colour of the fabric during the process. More importantly the preparation method of the fabric is simple, low cost and quick.</p>
Brewster angle optical reflection observation of self-limiting nanoparticle monolayer self-assembly at a liquid/liquid interface
<p>Real-time optical reflection of incident <i>p</i>-polarized light near Brewster's angle shows that after drop-casting iron oxide nanoparticles (NPs) in heptane on top of a diethylene glycol (DEG) liquid substrate, an iron oxide NP layer forms at the DEG/heptane interface, and it self-limits to a monolayer even when there are excess NPs dispersed in the upper heptane phase. Most modes of NP self-assembly do not self-limit growth after the formation of a single monolayer. Observations are compared to a reflection model incorporating the reflectances expected at each interface. An effective medium model of the dielectric constant is used to model the reflectance of the NP layer at the DEG/heptane interface.</p>
Self-assembly of ordered graphene nanodot arrays
<p>Raw data associated with the publication "Self-assembly of ordered graphene nanodot arrays" published in Nature Communications under the DOI: 10.1038/s41467-017-00042-4</p> <p>Abstract: The ability to fabricate nanoscale domains of uniform size in two-dimensional (2D) materials could potentially enable new applications in nanoelectronics and the development of innovative metamaterials. However, achieving even minimal control over the growth of 2D lateral heterostructures at such extreme dimensions has proven exceptionally challenging. Here we show the spontaneous formation of ordered arrays of graphene nano-domains (dots), epitaxially embedded in a 2D boron-carbon-nitrogen alloy. These dots exhibit a strikingly uniform size of 1.6nm ± 0.2nm and strong ordering, and the array periodicity can be tuned by adjusting the growth conditions. We explain this behaviour with a model incorporating dot-boundary energy, a moiré-modulated substrate interaction, and long-range repulsion between dots. This new 2D material, which theory predicts to be an ordered composite of uniform-size semiconducting graphene quantum dots laterally integrated within a larger-bandgap matrix, holds promise for novel electronic and optoelectronic properties, with a variety of potential device applications.</p>
Metadata - Direct C−H Arylation of Dithiophene-Tetrathiafulvalene: Tuneable Electronic Properties and 2D Self-Assembled Molecular Networks at the Solid/Liquid Interface
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Data from: Cation identity in clay-polyelectrolyte self-assembled hydrogels: Rheological and NMR study of the polyitaconate-counterion interactions
<p>The upload contains data associated with the publication, including raw data in the original file format whenever possible. Dataset content: NMR and rheology.</p> <p>This work was financially supported by the Lead Agency bilateral a Czech-Polish project provided by the Czech Science Foundation (21-07004K) and National Science Center Poland (CEUS-UNISONO project grant no. 2020/02/Y/ST5/00021).</p>
Data from: Insight into the aqueous Laponite nanodispersions for self-assembled poly(itaconic acid) nanocomposite hydrogels: The effect of multivalent phosphate dispersants
<p>The upload contains data associated with the publication, including raw data in the original file format whenever possible. Dataset content: SAXS, NMR reology, zeta potential.</p> <p>This work was financially supported by the Lead Agency bilateral a Czech-Polish project provided by the Czech Science Foundation (21-07004K) and National Science Center Poland (CEUS-UNISONO project grant no. 2020/02/Y/ST5/00021).</p>
Simulation Data for Design of Peptides that Fold and Self-Assemble on Graphite
<p>This data set for the manuscript entitled "Design of Peptides that Fold and Self-Assemble on Graphite" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software NAMD, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text and supporting information. They include molecular model structure files (NAMD psf or Amber prmtop format), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and trajectories in dcd format (downsampled to 10 ns per frame). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.</p> <p>Version: 2.0</p> <p>Changes versus version 1.0 are the addition of the free energy of folding, adsorption, and pairing calculations (Sim_Figure-7) and shifting of the figure numbers to accommodate this addition.</p> <p><br> Conventions Used in These Files<br> ===============================</p> <p>Structure Files<br> ----------------<br> - graph_*.psf or sol_*.psf (original NAMD (XPLOR?) format psf file including atom details (type, charge, mass), as well as definitions of bonds, angles, dihedrals, and impropers for each dipeptide.)</p> <p>- graph_*.pdb or sol_*.pdb (initial coordinates before equilibration)<br> - repart_*.psf (same as the above psf files, but the masses of non-water hydrogen atoms have been repartitioned by VMD script repartitionMass.tcl)<br> - freeTop_*.pdb (same as the above pdb files, but the carbons of the lower graphene layer have been placed at a single z value and marked for restraints in NAMD)<br> - amber_*.prmtop (combined topology and parameter files for Amber force field simulations)<br> - repart_amber_*.prmtop (same as the above prmtop files, but the masses of non-water hydrogen atoms have been repartitioned by ParmEd)</p> <p>Force Field Parameters<br> ----------------------<br> CHARMM format parameter files:<br> - par_all36m_prot.prm (CHARMM36m FF for proteins)<br> - par_all36_cgenff_no_nbfix.prm (CGenFF v4.4 for graphene) The NBFIX parameters are commented out since they are only needed for aromatic halogens and we use only the CG2R61 type for graphene.<br> - toppar_water_ions_prot_cgenff.str (CHARMM water and ions with NBFIX parameters needed for protein and CGenFF included and others commented out)</p> <p>Template NAMD Configuration Files<br> ---------------------------------<br> These contain the most commonly used simulation parameters. They are called by the other NAMD configuration files (which are in the namd/ subdirectory):<br> - template_min.namd (minimization)<br> - template_eq.namd (NPT equilibration with lower graphene fixed)<br> - template_abf.namd (for adaptive biasing force)</p> <p>Minimization<br> -------------<br> - namd/min_*.0.namd</p> <p>Equilibration<br> -------------<br> - namd/eq_*.0.namd</p> <p>Adaptive biasing force calculations<br> -----------------------------------<br> - namd/eabfZRest7_graph_chp1404.0.namd<br> - namd/eabfZRest7_graph_chp1404.1.namd (continuation of eabfZRest7_graph_chp1404.0.namd)</p> <p>Log Files<br> ---------<br> For each NAMD configuration file given in the last two sections, there is a log file with the same prefix, which gives the text output of NAMD. For instance, the output of namd/eabfZRest7_graph_chp1404.0.namd is eabfZRest7_graph_chp1404.0.log.</p> <p>Simulation Output<br> -----------------<br> The simulation output files (which match the names of the NAMD configuration files) are in the output/ directory. Files with the extensions .coor, .vel, and .xsc are coordinates in NAMD binary format, velocities in NAMD binary format, and extended system information (including cell size) in text format. Files with the extension .dcd give the trajectory of the atomic coorinates over time (and also include system cell information). Due to storage limitations, large DCD files have been omitted or replaced with new DCD files having the prefix stride50_ including only every 50 frames. The time between frames in these files is 50 * 50000 steps/frame * 4 fs/step = 10 ns. The system cell trajectory is also included for the NPT runs are output/eq_*.xst.</p> <p>Scripts<br> -------<br> Files with the .sh extension can be found throughout. These usually provide the highest level control for submission of simulations and analysis. Look to these as a guide to what is happening. If there are scripts with step1_*.sh and step2_*.sh, they are intended to be run in order, with step1_*.sh first.</p> <p><br> CONTENTS<br> ========</p> <p>The directory contents are as follows. The directories Sim_Figure-1 and Sim_Figure-8 include README.txt files that describe the files and naming conventions used throughout this data set.</p> <p>Sim_Figure-1: Simulations of N-acetylated C-amidated amino acids (Ac-X-NHMe) at the graphite–water interface.</p> <p>Sim_Figure-2: Simulations of different peptide designs (including acyclic, disulfide cyclized, and N-to-C cyclized) at the graphite–water interface.</p> <p>Sim_Figure-3: MM-GBSA calculations of different peptide sequences for a folded conformation and 5 misfolded/unfolded conformations.</p> <p>Sim_Figure-4: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-5: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 295 K.</p> <p>Sim_Figure-5_replica: Temperature replica exchange molecular dynamics simulations for the peptide cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) with 20 replicas for temperatures from 295 to 454 K.</p> <p>Sim_Figure-6: Simulation of the peptide molecule cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) in free solution (no graphite).</p> <p>Sim_Figure-7: Free energy calculations for folding, adsorption, and pairing for the peptide CHP1404 (sequence: cyc(GTGSGTG-GPGG-GCGTGTG-SGPG)). For folding, we calculate the PMF as function of RMSD by replica-exchange umbrella sampling (in the subdirectory Folding_CHP1404_Graphene/). We make the same calculation in solution, which required 3 seperate replica-exchange umbrella sampling calculations (in the subdirectory Folding_CHP1404_Solution/). Both PMF of RMSD calculations for the scrambled peptide are in Folding_scram1404/. For adsorption, calculation of the PMF for the orientational restraints and the calculation of the PMF along z (the distance between the graphene sheet and the center of mass of the peptide) are in Adsorption_CHP1404/ and Adsorption_scram1404/. The actual calculation of the free energy is done by a shell script ("doRestraintEnergyError.sh") in the 1_free_energy/ subsubdirectory. Processing of the PMFs must be done first in the 0_pmf/ subsubdirectory. Finally, files for free energy calculations of pair formation for CHP1404 are found in the Pair/ subdirectory.</p> <p>Sim_Figure-8: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) where the peptides are far above the graphene–water interface in the initial configuration.</p> <p>Sim_Figure-9: Two replicates of a simulation of nine peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-9_scrambled: Two replicates of a simulation of nine peptide molecules with the control sequence cyc(GGTPTTGGGGGGSGGPSGTGGC) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-10: Adaptive biasing for calculation of the free energy of the folded peptide as a function of the angle between its long axis and the zigzag directions of the underlying graphene sheet.</p> <p> </p>
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