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16 results for “molecular aggregates”
Dataset of "Molecular Dynamics Simulations Unveil the Aggregation Patterns and Salting out of Polyarginines at Zwitterionic POPC Bilayers in Solutions of Various Ionic Strengths"
<p>Molecular dynamics simulations are performed for a series of model cell-penetrating peptides (in particular nona-arginines) in aqueous solutions, in contact with model phosphocholine (POPC) membranes in conditions of different ionic strengths. The unusual aggregation properties of peptides at model lipid bilayers are analyzed and different sizes and lifetimes of aggregates are presented.<br>This dataset contains molecular dynamics simulation data with trajectories, input files, and topology files for all studied systems. They contain low peptide concentration in water, low NaCl concentration, high NaCl concentration, low CaCl2 concentration, and high CaCl2 concentration.<br>In addition to low peptide concentration, high peptide concentration in water, low NaCl concentration, high NaCl concentration, low CaCl2 concentration, and high CaCl2 concentration are also studied.</p>
Molecular mechanism for the synchronized electrostatic coacervation and co-aggregation of alpha-synuclein and tau
<p><strong><em>The following metadata refers exclusively to electron paramagnetic resonance (EPR) measurements, which represent the contribution of the PARACAT students to this work</em></strong></p> <ul> <li><strong>Data type</strong>: EPR spectroscopic measurements and simulations</li> <li>Files are in <strong>.DTA, .DSC, .m, .mat, and .xlxs, </strong>formats</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements in <strong>.DTA </strong>and<strong> .DSC</strong> formats</li> <li>EPR spectroscopic simulation and analyses in .<strong>m </strong>and<strong> .mat</strong> format</li> <li>“Ready-to-plot”, processed EPR spectra are in <strong>.xlxs</strong> format.</li> </ul> </li> <li>The data are <strong>generated</strong> by: <ul> <li>CW-EPR measurements were performed with a Bruker ELEXSYS E580 X-band spectrometer equipped with a Bruker ER4118 SPT-N1 resonator operating at a microwave (MW) frequency of ∼9.7 GHz. The temperature was set to 25 °C and controlled by a liquid nitrogen cryostat.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP3_20221219_EPR </strong>folder includes EPR spectroscopic measurements and computer simulations/analyses, original data are in <strong> .DTA/.DSC</strong> formats; files in .<strong>m</strong> format were used to process the data.</li> </ul> </li> </ul> <p>NB. See the “READ ME” text file for more detailed information on files organization.</p> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li>Abbreviations: <ul> <li><strong>avg</strong> = averaged</li> <li><strong>aS_24</strong> = alpha-synuclein protein with TEMPOL spin label at position 24 of the polypeptidic chain</li> <li><strong>aS_122</strong> = alpha-synuclein protein with TEMPOL spin label at position 122 of the polypeptidic chain</li> <li><strong>pLK</strong> = poly-lysine</li> <li><strong>Tau441</strong> = Tau protein with complete amino-acid sequence</li> <li><strong>Tau_DNt</strong> = truncated Tau protein lacking N-terminal (see paper methods for further details)</li> </ul> </li> <li>Units of measurement: <ul> <li>Temperature: <strong>°</strong><strong>C</strong> (Celsius)</li> <li>Microwave Frequency: <strong>GHz</strong> (Giga-Hertz), <strong>MHz</strong> (Mega-Hertz), <strong>kHz</strong> (kilo-Hertz)</li> <li>Microwave Power: <strong>mW</strong> (milli-Watt)</li> <li>Magnetic Field: <strong>mT</strong> (milli-Tesla)</li> <li>Time: <strong>s</strong> (seconds)</li> <li>Concentration: <strong>μM</strong> (micro-Molar), <strong>% w/v</strong> (percentage weight-volume)</li> </ul> </li> </ul> </li> </ul>
Molecular Insights into the Effects of F16L and F19L Substitutions on the Conformation and Aggregation Dynamics of Human Calcitonin
<p><a name="_Hlk145518059"></a><span>Human calcitonin (hCT) regulates calcium-phosphorus metabolism, but its amyloid aggregation disrupts physiological activity, increases thyroid carcinoma risk, and hampers its clinical use for bone-related diseases like osteoporosis and Paget’s disease. Improving hCT with targeted modifications to mitigate amyloid formation while maintaining function holds promise as a strategy. Understanding how each residue in hCT's amyloidogenic core affects its structure and aggregation dynamics is crucial for designing effective analogs. Mutants F16L-hCT and F19L-hCT, where Phe residues in the core are replaced with Leu as in non-amyloidogenic salmon calcitonin, showed different aggregation kinetics. However, the molecular effects of these substitutions in hCT are still unclear. Here, </span><a name="OLE_LINK4"></a><span><span>we systematically investigated the folding and self-assembly conformational dynamics of hCT, F16L-hCT, and F19L-hCT through multiple long-timescale independent atomistic discrete molecular dynamics (DMD) simulations. </span></span><span><span>Our results indicated that the hCT monomer primarily assumed unstructured conformations with dynamic helices around residues 4-12 and 14-21. During self-assembly, the amyloidogenic core of hCT<sub>14-21</sub> converted from dynamic helices to β-sheets. However, substituting F16L did not induce significant conformational changes, as F16L-hCT exhibited similar characteristics to wild-type hCT in both monomeric and oligomeric states. In contrast, F19L-hCT exhibited substantially more helices and fewer β-sheets than hCT, irrespective of their monomers or oligomers. The substitution of F19L significantly enhanced the stability of the helical conformation for hCT<sub>14-21</sub>, thereby suppressing the helix-to-β-sheet conformational conversion. Overall, our findings elucidate the molecular mechanisms underlying hCT aggregation and the effects of F16L and F19L substitutions on the conformational dynamics of hCT, highlighting the critical role of F19 as an important target in the design of amyloid-resistant hCT analogs for future clinical applications.</span></span></p>
An exploration of human γD-crystallin affinity for potential aggregation inhibitors: A molecular docking investigation
<p><span>These files contain the supplementary material to the article “An exploration of human γD-crystallin affinity for potential aggregation inhibitors: A molecular docking investigation” and the molecular docking simulation data.</span></p> <p><span>In this study, we performed a comparative molecular docking analysis of several experimentally investigated molecules of natural origin, that might protect γ-crystallins from destabilization and aggregation. Our specific protein targets are wild-type human γD-crystallin, and its mutant P23T γD-crystallin, associated with congenital cataract. Thirteen phytochemicals were investigated as potential inhibitors of γD-crystallin aggregation, and we compared their binding energies with those of lanosterol, an ingredient present in over-the-counter eye products, to prevent cataracts. We performed a detailed comparative molecular docking analysis and we found that the binding energies of lanosterol outcompete those of all the other investigated potential natural inhibitors.</span></p>
Molecular origin of the two-step mechanism of gellan aggregation
<p>Data presented in the article entitled <strong>Molecular origin of the two-step mechanism of gellan aggregation</strong>.</p>
Main text figure data and scripts for "Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach"
<p>(as README.txt):</p> <p>Main text figure data and scripts for “Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach”, by Tarun Gera, Lipeng Chen, Alex Eisfeld, Jeffrey R. Reimers, Elliot J. Taffet and Doran I. G. B. Raccah.</p> <p>Each directory is dedicated to a particular figure published in the paper. In each directory there are sub-directories which contains the data plotted in each panel. Each data file is a 2-D list in the format of (x,y) for each plot. There are python scripts (Fig_X.py) in each directory to plot the data.</p> <p>Table of contents:</p> <p>Figure_2:</p> <p> - 4_site_edge_contri.npy: Calculated edge sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_inner_contri.npy: Calculated inner sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_total_spectra.npy: Calculated total absorption spectrum for a 4-site chain system v/s energy. </p> <p><br> Figure_3:</p> <p>Panel A:<br> <br> - Mean_Error_Edge.npy: Mean error for the edge case v/s number of trajectories.<br> - Mean_Error_Inner.npy: Mean error for the inner case v/s number of trajectories.<br> - Mean_Error_SS.npy: Mean error for a single site initial condition v/s number of trajectories.<br> - Mean_Error_GD.npy: Mean error for a 4-site chain system with Gaussian distributed site energies v/s number of trajectories.</p> <p>Panel B: </p> <p> - Scaled_error_SS.npy: Mean error for a single site initial condition normalized by the square-root of one v/s number of trajectories.<br> - Scaled_error_PS.npy: Mean error for a pair site initial condition normalized by the square-root of two v/s number of trajectories.<br> - Scaled_error_AS.npy: Mean error for an all site initial condition normalized by the square-root of four v/s number of trajectories.</p> <p>Figure_4: </p> <p>Panel_A:</p> <p> - List_Error.npy: Calculated mean error for a 4-site chain for a set of auxiliary error bounds.</p> <p>Panel_B:</p> <p> - Cw_4S_HOPS.npy: Absorption spectrum for a 4-site chain calculated using dyadic HOPS v/s energy.<br> - Cw_4S_DadHOPS.npy: Absorption spectrum for a 4-site chain calculated using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_12S_DadHOPS.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS without including state adaptivity v/s energy.<br> - Cw_12S_DadHOPS_SA.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS with state adaptivity v/s energy.</p> <p>Panel_D:</p> <p> - Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-pigment system.<br> - Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-pigment system.<br> - N_states_DadHOPS.npy: Number of site states required to run a DadHOPS calculation for each N-pigment system.<br> - N_states_HOPS.npy: Number of site states required to run a dyadic HOPS calculation for each N-pigment system.<br> </p> <p>Figure_5:<br> <br> Panel_C: </p> <p> - PSI_Cw_HEOM.npy: PSI absorption spectrum calculated using HEOM v/s energy.<br> - PSI_Cw_HOPS.npy: PSI absorption spectrum calculated using dyadic HOPS v/s energy.</p> <p>Panel_D:</p> <p> - PSI_Error_Random.npy: Calculated mean error, where clusters of 4 were assigned randomly v/s number of trajectories.<br> - PSI_Error_Coupling.npy: Calculated mean error, where clusters of 4 were assigned based on electronic coupling values v/s number of trajectories.</p> <p><br> Figure_6:</p> <p>Panel_A:</p> <p> - PBI_Exp_data_dil.npy: Experimental data for a dilute solution of PBI v/s energy.<br> - PBI_Cw_DadHOPS_300.npy: Calculated spectrum for a PBI monomer with the spread in static disorder of value 300 cm^{-1} v/s energy.<br> - PBI_Cw_DadHOPS_400.npy:: Calculated spectrum for a PBI monomer with the spread in static disorder of value 400 cm^{-1} v/s energy.</p> <p>Panel_B:</p> <p> - PBI_Exp_data_conc.npy: Experimental data for a concentrated solution of PBI v/s energy.<br> - PBI_trimer_Cw_DadHOPS.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_PBI_monomer.npy: Calculated spectrum for a PBI monomer using DadHOPS v/s energy.<br> - Cw_PBI_dimer.npy: Calculated spectrum for a PBI dimer using DadHOPS v/s energy.<br> - Cw_PBI_trimer.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.<br> - Cw_PBI_heptamer.npy: Calculated spectrum for a PBI heptamer using DadHOPS v/s energy.<br> - Cw_PBI_1000mer.npy: Calculated spectrum for a PBI 1000mer using DadHOPS v/s energy.</p> <p>Panel_D:</p> <p> - peak_00_position.npy: relative position of the 00 peak for different number of pigments.<br> - peak_00_position_1000.npy: relative position of the 0,0 peak for a system with 1000 pigments. (Single value file)<br> - peak_I_ratio.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for different number of pigments.<br> - peak_I_ratio_1000.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for a system with 1000 pigments. (Single value file)</p> <p><br> Figure_7:</p> <p> - PBI_N_states_DadHOPS.npy: Number of states required to run a DadHOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-PBI molecules system. </p> <p>The packaged scripts may be run with Python 3.10 and the associated versions of the os, numpy, and matplotlib packages. <br> </p>
Data from: Controlled molecular arrangement of easily aggregated deoxycholate with layered double hydroxide
<p><span>Deoxycholate (DA) is a natural emulsifying agent involved in the absorption of dietary lipids. Due to the facial distribution of hydrophobic-hydrophilic region, DA easily aggregates under ambient conditions, and this property hinders the practical application of DA in clinical application. In this study, we found that the molecular arrangement of DA molecules could be controlled by utilizing layered double hydroxide (LDH) under a specific reaction condition. The effect of reaction methods such as co-precipitation, ion exchange, and reconstruction on the molecular arrangement of DA was investigated by X-ray diffraction, Fourier-transform infrared spectroscopy, high-resolution transmission electron microscopy, and differential scanning calorimetry. It was demonstrated that the self-aggregation of DA molecules could be suppressed by the oriented arrangement of DA between the gallery space of LDH. The DA moiety was well stabilized in the LDH layers due to the electrostatic interaction between DA molecules and LDH layers. The most ordered arrangement of DA molecules was observed when DA was incorporated into LDH via a reconstruction method. The DA molecules arranged in LDH via reconstruction did not show significant exothermic nor endothermic behavior up to 400</span><span>℃</span><span>, showing that the DA moiety lost its intermolecular attraction in between LDH layers.</span></p>
Data from: Controlled molecular arrangement of easily aggregated deoxycholate with layered double hydroxide
Open the record for dataset details and reuse information.
Data from: High-molecular-weight polymers from dietary fiber drive aggregation of particulates in the murine small intestine
The lumen of the small intestine (SI) is filled with particulates: microbes, therapeutic particles, and food granules. The structure of this particulate suspension could impact uptake of drugs and nutrients and the function of microorganisms; however, little is understood about how this suspension is re-structured as it transits the gut. Here, we demonstrate that particles spontaneously aggregate in SI luminal fluid ex vivo. We find that mucins and immunoglobulins are not required for aggregation. Instead, aggregation can be controlled using polymers from dietary fiber in a manner that is qualitatively consistent with polymer-induced depletion interactions, which do not require specific chemical interactions. Furthermore, we find that aggregation is tunable; by feeding mice dietary fibers of different molecular weights, we can control aggregation in SI luminal fluid. This work suggests that the molecular weight and concentration of dietary polymers play an underappreciated role in shaping the physicochemical environment of the gut.
Data for "Minimizing non-radiative decay in molecular aggregates through control of excitonic coupling"
<p>Data for "Minimizing non-radiative decay in molecular aggregates through control of excitonic coupling". </p> <p>"Adding 'FIG.5c-20230615.npz' to match the revised manuscript" - Updated 2023.06.15</p>
Effects of Low Molecular Weight Heparin Versus Dabigatran on Platelet Aggregation in Patients With Stable Coronary Artery Disease
ClinicalTrials.gov study NCT02389582. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: High-molecular-weight polymers from dietary fiber drive aggregation of particulates in the murine small intestine
Open the record for dataset details and reuse information.
RNA Profiles Reveals Familial Aggregation of Molecular Subtypes in non-BRCA1/2 Breast Cancer Families
GEO Series GSE49481. Homo sapiens. 253 samples. Type: Expression profiling by array.
Presence of aggregates of smooth endoplasmic reticulum in MII oocytes affects oocyte development competence: a molecular-based evidence
GEO Series GSE106222. Homo sapiens. 6 samples. Type: Expression profiling by array.
Molecular Dynamics trajectories and portable binary run input (TPR) files for the dual role of anionic lipids in amyloid aggregation
<h1>About this repository</h1> <p>We conducted Coarse-Grained molecular dynamics simulations to study the aggregation of the amyloid-beta fragment, Aβ16-22 (K16LVFFAE22), on mixed lipid bilayers composed of POPC and POPS. Three bilayer compositions were examined: 0% PS-100% PC, 10% PS-90% PC and 30% PS-70% PC. Simulations were performed using GROMACS 2019.4, employing the WEPROM forcefield for peptide modeling and WEPMEM for lipid modeling. For each POPS percentage, four independent replicas were run for 3000 ns. </p> <h1><strong>Contents</strong></h1> <h3>> Abeta_PCPS.zip </h3> <p>This zip file contains three main folders: 0PS, 10PS, and 30PS. These represent different percentages of PS present in the lipid bilayer.</p> <p>Each folder contains:</p> <p>1. Initial structures:<br> - em1.gro: Lipid bilayer without peptides<br> - eq1.gro: Lipid bilayer at 95 Ų area-per-lipid (APL)<br> - em2.gro: Lipid bilayer at 95 Ų APL with 16 peptides<br> - eq2.gro: Final equilibrated structure with peptides (used for production)</p> <p>2. Supporting files:<br> - index.ndx: Index groups for peptides and lipid bilayer<br> - run.pdf: Structure file for VMD visualization</p> <p>3. Four replica folders (replica1 to replica4), each containing:<br> - red.tpr: Binary input for analysis (peptides and lipid bilayer only)<br> - full_trj_pbc.xtc: Trajectory file (peptides and lipid bilayer only, centered in box)</p> <p>Note: The `.tpr` files were created using the `gmx convert-tpr` tool.</p> <p><strong>To access the source files with which these simulations were set-up, and a brief tutorial, see: </strong><a href="https://github.com/suhasgotla/heparin_amyloid_self-assembly">https://github.com/meenaljainumd/PCPS_amyloid_aggregation</a></p>
Elucidating the molecular determinants of Aβ aggregation with deep mutational scanning
GEO Series GSE139122. Homo sapiens. 18 samples. Type: Other.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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