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1,744 results for “peptide”
Source Data File – Dual role of the peptide loading complex as proofreader and limiter of MHC-I presentation – PNAS 2023-21600
<p>Source Data Files</p>
Design of linear and cyclic peptide binders
<p>The data is compressed with zstd: https://github.com/facebook/zstd and tar. To uncompress do: </p> <p>tar --use-compress-program full/path/to/zstd -xvf file.tar.zst</p> <p>PDB files of predicted protein-peptide complexes from the design process:</p> <p>1.1G 1ssc_untargeted_cyclic_afm.tar.zst <br>1.1G 1ssc_untargeted_cyclic.tar.zst<br>1.1G 1ssc_untargeted_linear_afm.tar.zst<br>1.1G 1ssc_untargeted_linear.tar.zst</p> <p>Selected PDB files of predicted protein-peptide complexes from the design process:</p> <p>(440 KB each). 1ssc_adversarial_linear_sel, 1ssc_tp_cyclic_sel, 1ssc_tp_linear_sel</p> <p>Metrics:</p> <p>1ssc_cyclic_merged_with_solubility.csv - all cyclic design metriccs</p> <p>1ssc_linear_merged_with_solubility.csv - all linear design metriccs</p> <p>1ssc_linear_adversarial_sel.csv - adversarial selection for linear design</p> <p>1ssc_linear_tp_sel.csv - top selection for linear design</p> <p>spr_results.csv - SPR affinities (Kd)</p> <p>1ssc_cyclic_tp_sel.csv - top selection for cyclic design</p> <p>linear_top_adversarial_pae.csv - PAE scores for linear top and adversarial designs</p>
Geometric deep learning improves generalizability of MHC-bound peptide predictions
<p>Full dataset and trained models from the manuscript "<strong>Geometric deep learning improves generalizability of MHC-bound peptide predictions</strong>".</p> <p>"outputs_and-BA_data.zip" contains the networks' outputs for each cross-validation experiment and a "full_dataset.csv" containing the initial BA data.<br>Note: this file has been updated (2024/11/26) due to errors in generating some of the previous csvs. In the earlier version, both MLP and CNN outputs reported were wrong. The correct values are now reported in the updated csvs.</p> <p>"trained_models.zip" contains all the trained models parameters</p> <p>"propedia_ssl.zip" contains all the 3D models from propedia used to train the 3D-SSL</p> <p>"pdb.zip" contains 3D models generated in PANDORA and used to train CNN, GNN and EGNN. It amounts to 145665 .pdb files, one for each human binding affinity entry from the initial dataset from O'Donnell et al. The list of entries used to actually train networks after filtering can be found in outputs_and-BA_data.zip", in the "full_dataset.csv" file. </p> <p> </p> <p>CHANGELOG v4:</p> <p>- In outputs_and-BA-data.zip, updated CNN_AlleleClustered_test_crossval.csv and CNN_shuffled_test_crossval.csv. These file had the wrong IDs paired with the network outputs.The IDs and labels are now consistent with the outputs.</p> <p>- Updated reference from the preprint to the published article. </p> <p> </p>
Cognition-Associated Protein Structural Changes in a Rat Model of Aging are Related to Reduced Refolding Capacity – Peptide Quantifications
<p>Cognitive decline during aging represents a major societal burden, causing both personal and economic hardship in an increasingly aging population. There are a few well-known proteins that can misfold and aggregate in an age-dependent manner, such as amyloid β and α-synuclein. However, many studies have found that the proteostasis network, which functions to keep proteins properly folded, is impaired with age, suggesting that there may be many more proteins that incur structural alterations with age. Here, we used limited-proteolysis mass spectrometry (LiP-MS), a structural proteomic method, to globally interrogate protein conformational changes in a rat model of cognitive aging. Specifically, we compared soluble hippocampal proteins from aged rats with preserved cognition to those from aged rats with impaired cognition. We identified several hundred proteins as having undergone cognition-associated structural changes (CASCs). We report that CASC proteins are substantially more likely to be nonrefoldable than non-CASC proteins, meaning they typically cannot spontaneously refold to their native conformations after being chemically denatured. The potentially cofounding variable of post-translational modifications is systematically addressed, and we find that oxidation and phosphorylation cannot significantly explain the limited proteolysis signal. These findings suggest that noncovalent, conformational alterations may be general features in cognitive decline, and more broadly, that proteins need not form amyloids for their misfolded states to be relevant to age-related deterioration in cognitive abilities.</p> <p>This deposition provides processed peptide quantifications for all LC-MS/MS proteomics experiments conducted for this study.</p>
MicroED datasets of peptide crystals (AVAAGA) at different accumulated electron exposures
<p>Continuous rotation data sets in SMV format were collected on a Tecnai F30 (300 kv) equipped with a TVIPS TEM CAM XF416 detector. Datasets were collected from 3 different crystals. To simulate increasing dose each crystal had 4 datasets collected from it with an approximate fluence of 3 e<sup>- </sup>Å<sup>2</sup> per dataset (12 e<sup>- </sup>Å<sup>2 </sup>total accumulated exposure per crystal). Datasets at each exposure were indexed with XDS and merged with XSCALE to produce a final set of intensities at each total exposure. Experimental parameters are as follows:<br> <br> Accelerating voltage: 300 kV<br> Wavelength: 0.019687<br> Cameralength: 1320 mm<br> Exposure time: 3 s<br> Rotation speed: 0.3 degrees s<sup>-1</sup><br> Rotation: +/- 45 degrees <br> Detector area: 2048 x 2048 (Bin 2)<br> Pixel size: 0.031 mm<br> Spot size: 11<br> C2 aperture: 20 µm<br> SA aperture: 1 µm</p> <p>Datasets are grouped as follows: Crystal 1 = mov1-4, Crystal 2 = mov5-8, Crystal 3 = mov9-12. An example XDS and XSCALE file are provided. Note: It has been brought to our attention that the zip file does not always unpack in a linux environment. The following workaround was found by T. Nakane using OpenJDK (17.0.1)</p> <p><code>jar -xvf</code> AVAAGA_dose_series.zip</p>
Comparing natural hydrogels to self-assembling peptides in spinal cord injury treatment: a systematic review
<p><strong>Abstract</strong></p> <p><em><strong>Background:</strong></em><em> </em>In many cases, central nervous system (CNS) injury is unchanging due to the absence of neuronal regeneration and repair capabilities.<strong> </strong>In recent years, regenerative medicine, and especially hydrogels, have reached a significant amount of attention for their promising results for the treatment of spinal cord injury (SCI) currently considered permanent. Hydrogels are categorized based on their foundation: synthetic, natural, and combination. The objective of this study was to compare the properties and efficacy of commonly used hydrogels, like collagen, and other natural peptides with synthetic self-assembling peptide hydrogels in the treatment of SCI. </p> <p><em><strong>Methods</strong></em><em>:</em><em> </em>Articles were searched in PubMed, Scopus, Web of Science, and Embase. All studies from 1985 until January 2020 were included in the primary search. Eligible articles were included based on the following criteria: administering hydrogels (both natural and synthetic) for SCI treatment, soley foucsing on spinal cord injury treatment, and published in a peer-reviewd journal. Data surronding xonal regeneration, revascularization, elasticity, drug delivery efficacy, and porosity were extracted.</p> <p><em><strong>Results:</strong></em> A total of 24 articles were included for full-text review and data extraction. There were only one experimental study directly comparing Collagen I (as natural hydorgel) and PEG (as synthetic hydrogels) in an <em>in vitro </em>setting. The included study suggested PEG’s cell behavior is more expectable in the injury site, which makes it a more reliable scaffold.</p> <p><em><strong>Conclusions:</strong></em> There is limited research comparing and evaluating both types of natural and self-assembling peptides (SAPs) in the same animal or <em>in vitro</em> study, despite its importance. Although we assume that the remodeling of natural scaffolds may lead to a stable hydrogel, there was not a definitive conclusion that synthetic hydrogels are more beneficial than natural hydrogels in neuronal regeneration.</p>
Predicted peptides: Another lesson from unmapped reads – in depth analysis of RNA-Seq reads from various horse tissues
<p>Predicted peptides – peptides predicted based on the assembled transcripts using TransDecoder software</p> <p> </p> <p>Data: loin adipose - AD, hoof lamina - LM, liver - LI, longissimus muscle - LO, left lung - LU, heart left ventricle - LV, ovary- OV and parietal cortex – PC<br> 1 – horse ECA_UCD_AH1<br> 2 – horse ECA_UCD_AH2</p> <p> </p>
AHTpin results for peptides identified in the filtrate of nanofiltration of Cynara cardunculus swine blood hydrolysate
<p>AHTpin results for peptides identified by peptidomics in the filtrate of nanofiltration of <em>Cynara cardunculus</em> swine blood hydrolysate. Peptides with a SVM> 1.0 were predicted as antihypertensive.</p>
Choline acetyltransferase (ChAT) - Amyloid beta peptides complex Molecular dynamics Trajectories
<p>In silico molecular dynamics study was performed for the choline acetyltransferase (ChAT) - Abeta peptides complex. The molecular docking of Aβ<sub>40 </sub>and Aβ<sub>42</sub> on ChAT suggested three most probable binding clusters for both the Aβ peptides. Thus generating ChAT-Aβ<sub>40</sub> Cluster-0, ChAT-Aβ<sub>40</sub> Cluster-1, ChAT-Aβ<sub>40</sub> Cluster-2 for Aβ<sub>40</sub> peptide on ChAT, likewise ChAT-Aβ<sub>42</sub> Cluster-0, ChAT-Aβ<sub>42</sub> Cluster-1, ChAT-Aβ<sub>42</sub> Cluster-2 were generated for Aβ<sub>42</sub> peptide on ChAT.</p> <p>Each of the folders contains the topology file with a ‘.gro’ extension and a trajectory file with ‘.xtc’ extension generated from the 100 ns molecular dynamics performed for each of the clusters mentioned above that were generated from the molecular docking. The folders are named as follows:</p> <ol> <li>ChAT_AB40_Cluster_0: Containing the topology file (ab40_0.gro) and the trajectory file (ab40_0.xtc)</li> <li>ChAT_AB40_Cluster_1: Containing the topology file (ab40_1.gro) and the trajectory file (ab40_1.xtc)</li> <li>ChAT_AB40_Cluster_2: Containing the topology file (ab40_2.gro) and the trajectory file (ab40_2.xtc)</li> <li>ChAT_AB42_Cluster_0: Containing the topology file (ab42_0.gro) and the trajectory file (ab42_0.xtc)</li> <li>ChAT_AB42_Cluster_1: Containing the topology file (ab42_1.gro) and the trajectory file (ab42_1.xtc)</li> <li>ChAT_AB42_Cluster_2: Containing the topology file (ab42_2.gro) and the trajectory file (ab42_2.xtc</li> </ol>
Raw data for the article "N-terminal selective C-H azidation of proline-containing peptides: a platform for late-stage diversification"
<p>Raw NMR, IR and MS data for the article "N-terminal selective C-H azidation of proline-containing peptides: a platform for late-stage diversification" published in Chemistry- A European Journal, DOI: </p> <p><a href="http://dx.doi.org/10.1002/chem.202200368">http://dx.doi.org/10.1002/chem.202200368</a>.</p> <p>The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.</p>
Amyloid-beta 16-22 peptide monomer simulation with the CHARMM-Drude force field and OpenMM (Run 1)
<p>Amyloid-beta 16-22 peptide (monomer) simulations with the CHARMM-Drude force field and OpenMM. This is the first independent simulation runs out of 3.</p> <p>Part 1-2 are 200 ns long, 3-8 are 100 ns each. Total trajectory length is 1 microseconds. Frame saving frequency is 10 ps.</p> <p>The system contains ~ 150 mM NaCl.</p>
Gold Nanoparticles Synthesized in the Presence of Peptides - UV-Vis Spectra, Fluorescence, USAXS, Electron Microscopy; pre-publication version
<p>Content Summary:</p> <ul> <li>Data from experiments in which gold nanoparticles were synthesized in the presence of peptides using a liquid-handling robot. Samples were analyzed using UV-Vis spectroscopy, fluorescence emission, USAXS, TEM, and SEM. </li> <li>Notebooks for loading and plotting data</li> <li>Code for synthesizing samples using an OT2 Opentrons liquid-handling robot.</li> </ul> <p>README:</p> <blockquote> <p><strong>/Data</strong></p> <p>Contains all UV-Vis, electron microscopy, fluorescence, and SAXS data for gold nanoparticles synthesized in the presence of peptides and HEPES.</p> <p><strong>/Data/2021_12_30_Prepared_UV_Vis_Data</strong></p> <p>The primary portion of the experimental dataset. UV-Vis spectroscopy data collected on a Biotek Epoch 2 microplate spectrophotometer 24 hours after samples were synthesized using a liquid handling robot (Opentrons OT2). The <strong>4x4x4_SI.csv </strong>file is the compilation of all sample information:</p> <ul> <li>Concentrations (M) of peptide, HAuCl4, and HEPES</li> <li>UID – unique ID based on date of synthesis, sample position, and peptide which was used to synthesize the sample.</li> <li>Peptide names: Z2: RMRMKMK; MZ2: myristoylated - RMRMKMK; MZ2R: myristoylated - KMKMRMR; PZ2: palmitoylated – RMRMKMK; Z2M6I: RMRMKIK; Z2M246I: RIRIKIK; AG3: AYSSGAPPMPPF.</li> </ul> <p>Each sample’s UID is a key to match with UV-Vis measurement result stored in the {<strong>UID}.txt </strong>files. Each of these files contains the wavelength, absorbance, and absorbance after subtraction of a water measurement.</p> <p><strong>/Data/ElectronMicroscopy</strong></p> <p>Scanning electron microscopy and transmission electron microscopy results of gold nanoparticles formed from the reduction of HAuCl4 in the presence or absence of different peptides.</p> <p>Fig A, B, C, D, E/F were prepared in the presence of Z2, Z2M6I, Z2M246I, no peptide, and MZ2R, respectively.</p> <p><strong>/Data/Fluorescence</strong></p> <p>Pyrene fluorescence data collected in the presence of different concentrations of lipidated peptides (MZ2, MZ2R, and PZ2) for estimation of the peptide critical micelle concentration.</p> <p><strong>/Data/SAXS</strong></p> <p>SAXS data of a high concentration of MZ2 which was fit using a cylindrical model form factor. The evaluated model is also shared in this directory.</p> <p><strong>/Data/USAXS</strong></p> <p>Similarly to the UV-Vis data directory, the <strong>USAXS_SI.csv</strong> file contains sample information for all of the USAXS measurements. The <strong>dsm_rg.csv</strong> file contains the output of AUTORG evaluated on the desmeared data after subtraction of a flat background at high-q. <strong>/DSM_Nexus, DSM_sub_AUTORG, </strong>and <strong>SMR_Nexus</strong> contain the desmeared, desmeared with background subtraction, and smeared versions of the USAXS data, respectively.</p> <p><strong>/Notebooks</strong></p> <p>Notebooks for plotting the shared data and estimating the CMC from the fluorescence data. See <strong>/Notebooks/environment.yml</strong> for packages necessary to execute the notebooks here and in <strong>/Synthesis_Protocol</strong>. Refer to <a href="https://github.com/SasView/sasmodels">https://github.com/SasView/sasmodels</a> for specific instructions on how to install the sasmodels module.</p> <p><strong>/Figures</strong></p> <p>Figures generated from <strong>/Notebooks</strong>.</p> <p><strong>/Synthesis_Protocol</strong></p> <p>Please read the instructions within <strong>/Synthesis_Procol/Example.ipynb</strong>. In short, this folder contains the code used to synthesize the samples in this dataset using an OT2 Opentrons liquid handling robot.</p> <p> </p> </blockquote>
The lower airways microbiota and antimicrobial peptides indicate dysbiosis in sarcoidosis
<p><span><strong>Rationale</strong>: </span><span>The role of the pulmonary microbiome in sarcoidosis is unknown.</span><br><br><span><strong>Objectives</strong>: </span><span>The objectives of the current study was to: 1) </span><span>Examine whether the pulmonary fungal and bacterial microbiota differed in patients with sarcoidosis compared with controls. 2) Examine whether there was an association between the microbiota and levels of the antimicrobial peptides (AMPs) in protected bronchoalveolar lavage (PBAL), indicating an interaction with the innate immune response.</span><br><br><span><strong>Methods</strong>: </span><span>35 sarcoidosis patients and 35 healthy controls underwent bronchoscopy and were sampled with oral wash (OW), protected BAL (PBAL) and left protected sterile brushes (LPSB). The fungal ITS1 region and the V3V4 region of the bacterial 16SrDNA gene were sequenced. Bioinformatic analyses were performed with QIIME 2. The AMPs secretory leucocyte protease inhibitor (SLPI) and human beta defensins 1 and 2 (hBD-1 & hBD-2), were measured in PBAL by enzyme linked immunosorbent assay (ELISA).</span><br><br><span><strong>Measurements and Main Results</strong>: </span><em><span>Aspergillus</span></em><span> dominated the PBAL samples in sarcoidosis. Differences in bacterial taxonomy were minor. There was no significant difference in fungal alpha diversity between sarcoidosis and controls, but the bacterial alpha diversity in sarcoidosis was significantly lower in OW (p=0.047) and PBAL (p=0.03) compared with controls. The beta diversity for sarcoidosis compared with controls differed for both fungi and bacteria. AMP levels were significantly lower in sarcoidosis compared to controls (SLPI & hBD-1: p<0.01). No significant correlations were found between </span><span>a</span><span>-diversity and AMPs.</span><br><br><span><strong>Conclusions</strong>: </span><span>The pulmonary fungal and bacterial microbiota in sarcoidosis differed from controls, with lower antimicrobial peptides levels in sarcoidosis.</span></p>
Dipolar Relaxation of Water Protons in the Vicinity of a Collagen-Like Peptide: Input Files for Simulation
<p>Input files to run the simulations in the paper</p> <p>Journal: The Journal of Physical Chemistry B<br> Title: Dipolar relaxation of water protons in the vicinity of a collagen-like peptide<br> Authors: Jouni Karjalainen, Henning Henschel, Mikko J. Nissi, Miika T. Nieminen, Matti Hanni<br> DOI: 10.1021/acs.jpcb.2c00052</p>
FASST Structure Database files for "Tertiary motifs as building blocks for the design of protein-binding peptides"
<p>FASST Database files for use in the <a href="https://github.com/swanss/peptide_design">peptide design</a> pipeline.</p> <p>A complete list of structures provided in the databases is provided in the supplementary information of the Protein Science article.</p> <p>Singlechain structures: <a href="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fpro.4322&file=pro4322-sup-0004-TableS6.txt">pro4322-sup-0004-TableS6.txt</a> (format: PDBID_CHAINID)</p> <p>Multichain structures: <a href="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fpro.4322&file=pro4322-sup-0005-TableS7.txt">pro4322-sup-0005-TableS7.txt</a> (format: PDBID)</p>
Amyloid-beta 16-22 peptide monomer simulation (150 mM NaCl) with the CHARMM36m force field and Gromacs (Run 3)
<p>MD simulations of the Amyloid-beta 16-22 monomer at 150 mM NaCl concentration with CHARMM36m force field and Gromacs. This repository contains the third out of three independent runs. </p> <p>Files belong to the publication "<a href="https://doi.org/10.1021/acs.jcim.0c01063">https://doi.org/10.1021/acs.jcim.0c01063</a>"</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with Gromacs 2018.3</p> <p>Total simulation time is 500 ns. Frames are saved with 100 ps frequency. </p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 2)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the second out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 3)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the third out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 1)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the first out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Histidine ligated iron-sulfur peptides
<p>Data sets of the paper: "Luca Valer, Daniele Rossetto, Taylor Parkkila, Lorenzo Sebastianelli, Graziano Guella, Amber L. Hendricks, James A. Cawan, Lingzi San, and Sheref S. Mansy. Histidine ligated iron-sulfur peptides. ChemBioChem (2022). <a href="https://doi.org/10.1002/cbic.202200202">https://doi.org/10.1002/cbic.202200202</a>."</p>
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