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42 results for “Protein condensate”
Data for the publication: Recombinant silk protein condensates show widely different properties depending on the sample background
<p>This entry includes raw data for the publication "Recombinant silk protein condensates show widely different properties depending on the sample background". The original publication was published in: Journal of Materials Chemistry B, DOI: 10.1039/d4tb01422g</p> <p>The folder "Videos_Micropipette_Aspiration_Zenodo.zip" contains 9 TIF files, labeled Number1 - Number9. The numbering corresponds to the numbering of IMAC condensates studied with micropipette aspiration in the publication. Each TIF file is an image stack from a time series.</p> <p>The folders "Videos_IMAC_silk_with_BG_lysate_coalescence.zip", "Videos_HT_silk_coalescence.zip", and "Videos_IMAC_silk_coalescence.zip" all contain subfolders labeled with the purification method, the framerate of the videos and then consecutive numbering. Each of these folders contains the frames of the video as single TIF files.</p> <p>Please find more information in the read_me file uploaded.</p>
The generic nature of the condensed state of proteins | Talk - I PhasAGE International Conference
<p>The <strong>I PhasAGE international conference</strong> brought together members of the PhasAGE consortium as well as outstanding international speakers showcasing high impact achievements in the field of liquid-liquid phase separation in aging and late-onset diseases.</p> <p>For details on conference program please see: https://phasage.eu/phasage-conference-1/ </p>
Supplementary Movies and Source Data for: Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation
<p>Supplementary Movies and raw data for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation":</p> <p>Source_Data.zip: Supplementary Code, Supplementary Data and Weka Analysis</p> <p>Lan_supplementary_movies_AVI.zip: Supplementary movies as AVI</p> <p>Lan_supplementary_movies_MP4.zip: Supplementary movies as MP4</p> <p>Lan_raw_movies.zip: Raw TIFF stacks of the movies.</p> <p>Lan_supplementary_movies.zip: Old version of the movies.</p>
Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface
<h3>Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface</h3> <p>We ran simulations for four different systems:</p> <ul> <li>WT: A1-LCD WT (N=137), wild-type (WT) sequence of the low-complexity domain (LCD) of the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), with electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T260: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T290: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=290K</li> <li>HP: homopolymer consisting of prolines (N=137), at temperature T=550</li> </ul> <p>For every system, we ran five independent simulations over 5µs (1000 frames) and used the last 900 frames (4.5µs) for our analysis.</p> <p>This data repository consists of<br> (1) folders containing the data for every seperate run (*_i, i=1,2,3,4,5) in simulation units<br> (2) folders containing the averaged data of all five runs (*_AVG), converted to SI units<br> (3) a droplet folder, containing the data (square radius of gyration and asphericity) for the whole droplet (for all four systems, all five runs)<br>Units are also clarified in each file's header.</p> <p>The simulation units can be converted to SI units via:</p> <ul> <li>Distance: D = 0.45nm</li> <li>Mass: M = 57.05amu</li> <li>Energy: epsilon = 0.2 kcal/mol</li> </ul> <p> </p> <p>Details for (1) and (2):<br>Each folder (*_i, i=1,2,3,4,5, and *_AVG) contains the following subfolders and files:</p> <p><strong>Ree:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ_{ee}) of the whole chains, where θ_{ee} is the angle between the polymer's center r_c and the chain’s end-to-end vector Ree [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_{ee,s}) of segment seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_{ee,s}) of all segments seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos.dat: distribution of the polymer end positions (whole chains) [Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos_segment_i.dat: distribution of the polymer segment end positions of seg_i</li> <li>distribPolymerEndPos_segment_all.dat: distribution of the polymer segment end positions of all segments</li> <li>distribPolymerRee2_all.dat: distribution of Ree^2 (whole chains), binned via polymer center position r_c</li> <li>distribPolymerRee_segment_i.dat: distribution of Ree^2 of segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerRee_segments_all.dat: distribution of Ree^2 of all segments, binned via segment center position r_{c,s}</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all polymer segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>Rg:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ) of the whole chains, where θ is the angle between the polymer's center r_c and the eigenvector belonging to the largest eigenvalue of the chain’s gyration tensor [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_s) of segment seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_s) of all segments seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribMonomerRg_all.dat: distribution of monomer weighted Rg^2 (whole chains), referred to as R_{g,mono}^2 (following Farag et. al) [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_all.dat: distribution of Rg^2 (whole chains), binned via polymer center position r_c [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_segment_i.dat: distribution of Rg^2 of segment seg_i, referred to as R_{g,s}^2, binned via segment center position r_{c,s} [Fig. S4a, Fig. S7a, Fig. S10a, Fig. S13a]</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>resDist:</strong></p> <ul> <li>distribPolymerRee2_base_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRee2_center_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRg2_base_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> </ul> <p>distribPolymerRg2_center_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14] </p> <p> </p> <p>Details for (3):<br>The folder '<strong>droplet</strong>' contains four system folders (HP, WT, WT_noEL_T260, WT_noEL_T290). Each of those folders contains the following files:</p> <ul> <li>runX_cluster_Rg2_Rg2Normal_kappa2.dat: for every run X, one finds the time evolution (in simulation units, with 1e8 timesteps = 1µs) of the square radius of gyration Rg2 of the full droplet, its x-, y- and z-components, its three eigenvalues and the droplet asphericity A (referred to as kappa2 in the header) [Fig.S1c, Fig.S1d]</li> <li>AVG_cluster_Rg2_Rg2Normal_kappa2.dat: average of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> <li>STD_cluster_Rg2_Rg2Normal_kappa2.dat: standard deviation of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> </ul>
Mp4-Version of the supplementary Movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"
<p>Videos in 'mp4'-format of the 8 supplementary movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"</p>
Interplay of self-organization of microtubule asters and crosslinking protein condensates Data
<p>Data sets from all figures and supplemental figures for manuscript entitled "Interplay of self-organization of microtubule asters and crosslinking protein condensates" accepted at PNAS Nexus.</p>
Condensate targeting as a strategy to prevent irreversible protein aggregation: implications for ALS and FTD | Talk - I PhasAGE International Conference
<p>The <strong>I PhasAGE international conference</strong> brought together members of the PhasAGE consortium as well as outstanding international speakers showcasing high impact achievements in the field of liquid-liquid phase separation in aging and late-onset diseases.</p> <p>For details on conference program please see: https://phasage.eu/phasage-conference-1/ </p>
Data for Taylor Dispersion-Induced Phase Separation for the Efficient Characterisation of Protein Condensate Formation
<p>This archive contains data files the Python 3 code needed to reproduce the analysis done for the publication "Taylor Dispersion-Induced Phase Separation for the Efficient Characterisation of Protein Condensate Formation". Most of the data files are recorded on the Fida 1 instrument (Fidabio, Denmark) and consists of fluorescence recordings at the end of a 1 m long microfluidic channel (Ø 75 µm). Additional data file types include raw microscopy images (Leica SP8 confocal microscope, Germany), spectroscopy and light scattering files from Probedrum/Labbot (Labbot, Sweden), and simulation files generated by Comsol 6 (COMSOL AB, Sweden). Comsol project files are also supplied.</p> <p>The Python code is supplied in the form of Jupyter Notebooks. A python file "TDIPS.py" contains general routines used in the data analysis notebooks, and is for example capable of calculating the viscosity of various salt solution mixtures using table values. This is used for normalisation of the Fida 1 instrument data, when several measurements are done at different salt concentrations.</p> <p>The scripts are running Python 3.11.7 and packages Numpy (1.26.4), Matplotlib (3.8.0), Pandas (2.1.4), Scipy (1.11.4), and LMfit (1.2.2).</p> <p>All figures are included as Scalable Vector Graphics (<em>SVG</em>) files and can be opened using Inkscape.</p> <p>© Technical University of Denmark</p>
Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation - ESI
<p>ESI data for the paper 'Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation'.</p>
Simulation data for: Poly(A)-binding protein is an ataxin-2 chaperone that regulates biomolecular condensates.
<p>Trajectory data and analysis scripts for the simulation component in: <strong>Poly(A)-binding protein is an ataxin-2 chaperone that regulates biomolecular condensates, </strong>by Steven Boeynaems et al.</p>
Intrinsically Disordered Regions Promote Protein Refoldability and Facilitate Retrieval from Biomolecular Condensates–Peptide Quantifications
<p>Many eukaryotic proteins contain intrinsically disordered regions (IDRs) that intersperse globular folded domains, in contrast with bacterial proteins which are typically highly globular. Recent years have seen great progress in identifying biological functions associated with these elusive protein sequence: in specific cases, they mediate liquid- liquid phase separation, perform molecular recognition, or act as sensors to changes in the environment. Nevertheless, only a small number of IDRs have annotated functions despite their presence in 64% of yeast proteins, stimulating some to question what ‘general purpose’ they may serve. Here, by interrogating the refoldability of two fungal proteomes (Saccharomyces cerevisiae and Neurosporra crassa), we show that IDRs render their host proteins more refoldable from the denatured state, allowing them to cohere more closely to Anfinsen’s thermodynamic hypothesis. The data provide an exceptionally clear picture of which biophysical and topological characteristics enable refoldability. Moreover, we find that almost all yeast proteins that partition into stress granules during heat shock are refoldable, a finding that holds for other condensates such as P-bodies and the nucleolus. Finally, we find that the Hsp104 unfoldase is the principal actor in mediating disassembly of heat stress granules and that the efficiency with which condensed proteins are returned to the soluble phase is also well explained by refoldability. Hence, these studies establish spontaneous refoldability as an adaptive trait that endows proteins with the capacity to reform their native soluble structures following their extraction from condensates. Altogether, our results provide an intuitive model for the function of IDRs in many multidomain proteins and clarifies their relationship to the phenomenon of biomolecular condensation.</p> <p>This dataset provides peptide quantifications (and their respective P-values) from three separate types of experiments used to support the claims in this study.</p> <p>1. Peptide quantifications from global refolding reactions, assessed with limited-proteolysis mass spectrometry (LiP-MS), carried out on two fungal organisms (S. cerevisiae [yeast] & N. crassa), at three refolding times, repeated on three separate iterations (for yeast).</p> <p>2. Peptide quantifications from LiP-MS experiments conducted on yeast extracts during heat shock or recovery from heat shock</p> <p>3. Annotations for peptides in #1 that are associated with linker regions between folded domains.</p>
Viscoelasticity of globular protein-based biomolecular condensates
Open the record for dataset details and reuse information.
Data from: Hierarchical interactions between nucleolar and heterochromatin condensates are mediated by a dual-affinity protein
Open the record for dataset details and reuse information.
Datasets associated with the manuscript Protein Condensate Atlas from predictive models of heteromolecular condensate composition
<p>Datasets associated with the manuscript "Protein Condensate Atlas from predictive models of heteromolecular condensate composition".</p>
Co-condensation of proteins with single- and double-stranded DNA. Source Data
<p>Source data for: Co-condensation of proteins with single- and double-stranded DNA, PNAS, 2022</p>
Adaptive preservation of orphan ribosomal proteins in chaperone-stirred condensates
<p>Python and Fiji code used for the study "Adaptive preservation of orphan ribosomal proteins in chaperone-stirred condensates"</p>
IGF2BP1 phosphorylation regulates ribonucleoprotein condensate formation by impairing low-affinity protein and RNA interactions (RNA-Seq)
GEO Series GSE272874. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
L-bodies are novel RNA-protein condensates driving RNA transport in Xenopus oocytes
GEO Series GSE158246. Xenopus laevis. 12 samples. Type: Expression profiling by high throughput sequencing.
Nuclear Condensation of NUP98-Fusion Proteins drives leukemogenic gene expression
GEO Series GSE159037. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Preservation of orphan ribosomal proteins during stress in chaperone-stirred condensates
GEO Series GSE237174. Saccharomyces cerevisiae. 8 samples. Type: Expression profiling by high throughput sequencing.
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