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51 results for “protein engineering”
Raw data to accompany the manuscript 'Data for Engineering Lipid Metabolism of Chinese Hamster Ovary (CHO) Cells for Enhanced Recombinant Protein Production' published in the Journal Data in Brief
<p>This repository consists of the raw western blot, microscopy and mass spectrometry data to accompany the manuscript 'Data for Engineering Lipid Metabolism of Chinese Hamster Ovary (CHO) Cells for Enhanced Recombinant Protein Production' published in the Journal Data in Brief and associated with the article '<a href="https://www.ncbi.nlm.nih.gov/pubmed/31805379">Engineering of Chinese hamster ovary cell lipid metabolism results in an expanded ER and enhanced recombinant biotherapeutic protein production</a>' published in the journal Metabolic Engineering (see DOI: 10.1016/j.ymben.2019.11.007). </p> <p>The western blot raw file is associated with Figure 1a and 1b of the Data in Brief manuscript.</p> <p>The confocal microscopy raw image files (x3) are associated with Figure 1c of the Data in Brief manuscript.</p> <p>The mass spectrometry files are the raw data that refers to the samples presented in Figure 5 of the Data in Brief manuscript. Files are labelled as in the Data in Brief and Metabolic Engineering manuscripts. The file name structures is as follows;</p> <p>CHO-Controlpoolai</p> <p>Where 'a' represents replicate 'a' of three biological replicates and 'i' refers to mass spectrometry technical analysis 1 of 3 technical analyses of each replicate (thus for each cell pool or line there are three biological replicates that are each analysed in triplicate such that there are 9 raw mass spectrometry files for each cell pool or line).</p> <p>All the mass spectrometry files are found in the compressed (zip) file named mass_spectrometry_raw_files_archive.zip</p>
Data set for the journal article: Site-Specific Protein Ubiquitylation Using an Engineered, Chimeric E1 Activating Enzyme and E2 SUMO Conjugating Enzyme Ubc9
<p>Mutations observed in evolved chimeric E1 variants. Top row (1.X to 4.X) describes rounds of evolutions with respective variants in the round. </p> <p>Residues that appear to be enriched are highlighted with gray fill. Star (★) marks residues subjected to saturation mutagenesis in the round 4.</p>
Stability Increase of Phenolic Acid Decarboxylase by a Combination of Protein and Solvent Engineering Unlocks Applications at Elevated Temperatures
<p>Enzymatic decarboxylation of biobased hydroxycinnamic acids gives access to phenolic styrenes for adhesive production. Phenolic acid decarboxylases are proficient enzymes that have been applied in aqueous systems, organic solvents, biphasic systems, and deep eutectic solvents, which makes stability a key feature. Stabilization of the enzyme would increase the total turnover number and thus reduce the energy consumption and waste accumulation associated with biocatalyst production. In this study, we used ancestral sequence reconstruction to generate thermostable decarboxylases. Investigation of a set of 16 ancestors resulted in the identification of a variant with an unfolding temperature of 78.1 °C and a half-life time of 45 h at 60 °C. Crystal structures were determined for three selected ancestors. Structural attributes were calculated to fit different regression models for predicting the thermal stability of variants that have not yet been experimentally explored. The models rely on hydrophobic clusters, salt bridges, hydrogen bonds, and surface properties and can identify more stable proteins out of a pool of candidates. Further stabilization was achieved by the application of mixtures of natural deep eutectic solvents and buffers. Our approach is a straightforward option for enhancing the industrial application of the decarboxylation process.</p>
Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands
<p>The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 <sup>1</sup>) and engineered binders P17 Fab <sup>2</sup>, Ty1 VHH <sup>3</sup> and LCB1 peptide <sup>4</sup> reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.</p>
Sensitivity Datasets - Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins
<p><strong>Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins</strong></p> <p>The Sensitivity datasets cover more than 800 proteins and are structured as follows. The sensitivity values are the mean of four DeeProtein replicates.</p> <p>It is uploaded as tar.gz. and contains one directory.</p> <p>File names contain the PDB<sup>1</sup> identifier and the respective chain identifier. </p> <p>The sequences and secondary structure information were downloaded from the RCSB Protein Databank and are available here: <a href="https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz">https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz</a> This URL can be found with some explanation at <a href="http://www.rcsb.org/pdb/static.do?p=download/http/index.html">http://www.rcsb.org/pdb/static.do?p=download/http/index.html</a></p> <p>The secondary structure annotation relies on the DSSP Algorithm by Kabsch and Sander<sup>2</sup>.</p> <p> </p> <p><strong>The files are tab-separated and contain the following columns:</strong></p> <ul> <li><strong>Pos</strong> Position in the sequence, starting from zero</li> <li><strong>AA</strong> Amino acid in that position</li> <li><strong>sec</strong> Secondary structure as annotated in the RCSB Protein Databank</li> <li><strong>dis</strong> if a region has not been experimentally observed (sometimes explains mismatches with crystal structures)</li> <li><strong>GO:_______</strong> Sensitivity for the GO term</li> </ul> <p><strong>References</strong></p> <ol> <li>The Protein Data Bank H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne (2000) Nucleic Acids Research, 28: 235-242. doi:10.1093/nar/28.1.235</li> <li>Kabsch, W. & Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637, doi:10.1002/bip.360221211 (1983).</li> </ol>
Protein adsorption on biodegradable mikro/nanofibre materials for tissue engineering
<p><span>Due to their specific properties, nanofibrous materials are increasingly used in regenerative medicine and tissue engineering. Nanofibrous materials can be used as tissue scaffolds for injured (damaged) tissue. The main factor for tissue scaffolds is their biocompatibility. One of the main factors influencing the organism's physiological response is the interaction of the material with proteins. Proteins adsorbed on the material's surface give the tissue scaffolds a "biological identity"</span><span><span>. Cells in the organism subsequently interact with proteins adsorbed on the material's surface and determine the entire organism's response to the implanted material. This work deals with the influence of the morphology and chemical composition of polyester nanofibrous materials on the adsorption of proteins. The materials produced by electrospinning (DC spinning) were characterised from the point of view of morphology and wettability. Then, the adsorption of weakly and strongly bound proteins on the fibre surface was evaluated. Cell adhesion and proliferation on the tested materials were also observed. The results of protein adsorption were compared with the results of cell adhesion and proliferation to determine the effect of the amount of adsorbed proteins on the interaction of cells with the tested materials.</span></span></p>
Experimental Results of "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms"
<p>This repository contains the experimental results of the article "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms" by Duarte Campos, D., Lindsay, C., Roth, J., LeSavage, B., Seymour, A., Krajina, B., Ribeiro, R., Costa, P., Heilshorn, S., published in <em>Front. bioeng. biotechnol. </em><strong>8, 374</strong> (2020). https://doi.org/10.3389/fbioe.2020.00374</p>
Phase transitions as intermediate steps in the formation of molecularly engineered protein fibers
<p>This upload contains raw and unprocessed data sets including: Tensile test, diffraction, simulation, surface tension measurement, viscosity measurement, amino acid sequence and videos.</p>
Protein engineering using variational free energy approximation
<p>Data generated by PREVENT model and used in manuscript "Protein engineering using variational free energy approximation". Contains raw input data, R scripts and Jupyter Notebooks to process data and output figures used in the main text and supplementary materials of the manuscripts.</p>
Insights into the stability of engineered mini-proteins from their dynamic electronic properties
<p>Coordinates and partial charges from GFN2-xTB and wPBEh/cc-pvdz for 20 ps x 20 replicas for two variants of Trp-cage (TC5b and TC10b) as supporting information.</p>
Associated coordinate and mtz files for "Selective Oxidation of Secondary Coordination Sphere Tyrosine Residues in Engineered Cu Proteins"
<p>Coordinate and mtz files for the associated protein structures reported in "Selective Oxidation of Secondary Coordination Sphere Tyrosine Residues in Engineered Cu Proteins"</p> <p>9CST: Streptavidin-E101Q-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-Sav)</p> <p>9CSU: Streptavidin-E101Q-S112Y-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-S112Y-Sav)</p> <p>9CSV: Streptavidin-E101Q-S112Y-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor oxidized by hydrogen peroxide (Cu-2xm-S112Y-Sav + hydrogen peroxide)</p> <p>9CSW: Streptavidin-E101Q-S112A-K121Y bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-S112A-K121Y-Sav)</p> <p>9E6Z: Streptavidin-E101Q-S112F-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-S112F-Sav)</p>
NMR Relaxometry data for publication "Engineered Nonviral Protein Cages Modified for MR Imaging"
<p>NMRD profiles collected with field-cycling NMR relaxometry of a water solution of Gd-C4-IA, and of AaLS-13 and OP protein cages labeled with the gadolinium(III) complex.</p> <p>Published in: https://doi.org/10.1021/acsabm.2c00892</p> <p>NMRD data acquisition and analysis was performed with the support of the PRIN 2017A2KEPL project “Rationally designed nanogels embedding paramagnetic ions as MRI. probes”, and the European Commision through H2020 FET-Open project HIRES-MULTIDYN<br> award no. 899683 and H2020 INFRAIA iNEXT-Discovery (Structural Biology Research Infrastructures for Translational Research and Discovery) award no. 871037.</p>
NGS data from: Deploying synthetic coevolution and machine learning to engineer protein-protein interactions
<p>Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a synthetic platform for protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes<span class="Apple-converted-space"> </span>drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pre-trained protein language models to expand, <em>in silico</em>, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of generating protein complexes with diverse molecular recognition properties as tools for biotechnology and synthetic biology.</p>
Translocation of linearized full-length proteins through an engineered nanopore under opposing electrophoretic force
<p>This database contains raw electrophysiology data and MD data, organised in two parts: part 1 corresponds to electrophysiology traces and part 2 corresponds to MD files. For detailed information see below.</p> <p><strong>Part 1: electrophysiology data </strong></p> <p>Data separated in two main categories: main text data and SI (only) data. The electrophysiology data is named in the following format: </p> <p>main_FnX_CytK mutant_buffer_substrate_cis_applied potential, where n = figure number and X = panel</p> <p>Data from the main text:</p> <p><strong>Figure 2</strong>:</p> <p>--> C) CytK WT + S1 substrate (file names start with main_F2C_CytK WT)</p> <p>--> D) CytK 2E-4D (K128D K155D Q145D S151D) + S1 substrate (main_F2D...)</p> <p>--> E) CytK 2E-4D (K128D K155D Q145D S151D) + tzatziki substrate (main_F2E...)</p> <p>--> F) CytK 2E-4D (K128D K155D Q145D S151D) + mujdei substrate (main_F2F...)</p> <p><strong>Figure 4</strong>:</p> <p>--> CytK 2E-4D (K128D K155D Q145D S151D/ 4D) + malE219a substrate (main_F4A...)</p> <p>--> CytK 2E-4D (K128D K155D Q145D S151D/ 4D) + H152A-GBP substrate (main_F4B...)</p> <p>--> CytK 2E-4D (K128D K155D Q145D S151D/ 4D) + W30G-W133L-DHFR substrate (main_F4C...)</p> <p> </p> <p><strong>Supporting information figures </strong></p> <p>general name: SI_Sx_CytK mutant_buffer_substrate_cis_applied potential, where x = number figure from supporting information</p> <p> </p> <p>Figure S4. S1 translocation through the K128D K155D CytK mutant nanopore --> SI_S4...</p> <p>Figure S5. S1 translocation through the K128D K155D Q145D CytK mutant nanopore --> SI_S5...</p> <p>Figure S6. S1 translocation through the K128D K155D T147D CytK mutant nanopore --> SI_S6...</p> <p>Figure S7. Translocation of S1 through the 2E-1D-1Q-Q122D-CytK nanopore --> SI_S7...</p> <p>Figure S8. Translocation of S1 through 2E-4D-CytK nanopores --> see F2D main text (main_F2D...)</p> <p>Figure S9. Tzatziki and the CytK 2E-2D nanopore --> SI_S9...</p> <p>Figure S10. Tzatziki translocation through the K128D Q145D S151D K155D CytK nanopore --> see F2D main text (main_F2E...)</p> <p>Figure S11. Tzatziki translocation through the K128D K155D Q145D CytK nanopore --> SI_S11...</p> <p>Figure S12. Tzatziki translocation through the K128D K155D T147D CytK mutant nanopore --> SI_S12...</p> <p>Figure S14. Translocation of mujdei through 2E-4D-CytK nanopores --> see F2D main text (main_F2F...)</p> <p>Figure S22. Translocation of malE219a through 2E-4D-CytK nanopores in 2 M urea --> see F4A main text (main_F4A...)</p> <p>Figure S23: Translocation GBP H152A through the 2E-4D CytK nanopore in 2.4 M urea --> see F4B main text (main_F4B...)</p> <p>Figure S24. Translocation of W30G-W133L-DHFR through 2E-4D-CytK nanopore in 2.6 M urea --> see F4B main text (main_F4B...)</p> <p>Figure S26. MalE219a translocation through the 2E-4D CytK mutant in 1 M and 1.8 M Gu.HCl --> SI_S26... (1 M GuHCl and 1.8 M GuHCl are included in the file name)</p> <p>Figure S27: WT-CytK tested with the malE219a and malE219aD10ssrA proteins in 1.5 M Gu.HCl --> SI_S27...</p> <p>Original SDS-PAGE gels of the substrates in a powerpoint file</p> <p> </p> <p><strong>Part 2: MD data</strong></p> <p>The data corresponding to the MD simulations is bundled in a zip file named MD_files.zip</p> <p>This file contains the following subfiles linked to <strong>main text Figure 3</strong>:</p> <p><em>panel A</em>:</p> <p>- main_F3A_CytK_E2-D2 </p> <p>- main_F3A_CytK_E2-D3</p> <p>- main_F3A_CytK_E2-D4</p> <p>- main_F3A_CytK_WT</p> <p><em>panels C, D and E</em>:</p> <p>- main_F3CDE_CytK_E2-D4_rep1</p> <p>- main_F3CDE_CytK_E2-D4_rep2</p> <p>- main_F3CDE_CytK_E2-D4_rep3</p> <p>and files linked to the supporting information <strong>Figure S20</strong>:</p> <p>- SI_S20_ABC_CytK_E2-D4_TZA11_rep1</p> <p>- SI_S20_ABC_CytK_E2-D4_TZA11_rep2</p> <p>- SI_S20_ABC_CytK_E2-D4_TZA11_rep3</p>
Data from: Engineered reactivity of a bacterial E1-like enzyme enables ATP-driven modification of protein C termini
Open the record for dataset details and reuse information.
NGS data from: Deploying synthetic coevolution and machine learning to engineer protein-protein interactions
Open the record for dataset details and reuse information.
Molecular basis for the increased affinity of an RNA recognition motif protein engineered to re-direct its specificity -PART 5
<p>Trajectories and input files for the simulations (REST2, REST2-PS and unbiased MD) of the Rbfox*·pre-miR20b* complex.</p>
Structural studies of the IFNλ4 receptor complex using cryoEM enabled by protein engineering
<p>MD trajectories for use with the analysis code in https://github.com/bylehn/ifnl4-structure-paper</p>
Supplementary data for "Engineering PD-1-targeted small protein variants for in vitro diagnostics and in vivo PET imaging"
<p>Supplementary data for "Engineering PD-1-targeted small protein variants for in vitro diagnostics and in vivo PET imaging". The docking.zip file contains ClusPro protein/protein docking results for all binding protein variants using either murine PD-1 structure (3bikB) or human PD-1 AF2 model as receptors. The pymol session contains data and scenes used to generate the Figures in the paper.</p>
Allogeneic Engineered Hematopoietic Stem Cell Transplant (HCT) Lacking the CD33 Protein, and Post-HCT Treatment With Mylotarg, for Patients With CD33+ AML or MDS
ClinicalTrials.gov study NCT04849910. IPD Sharing: Not stated. Countries: 2. Publications: 1.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
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.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.