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77 results for “Peptides and proteins”
A versatile "Synthesis Tag" (SynTag) for the chemical synthesis of aggregating peptides and proteins
<p>Raw data for the project "A versatile "Synthesis Tag" (SynTag) for the chemical synthesis of aggregating peptides and proteins".</p><p>Manuscript and supporting information available on ChemRxiv: https://doi.org/10.26434/chemrxiv-2023-7mz2c-v2.</p>
Scripts and analysis files for categorization of PKZILLA matching proteomic peptides into protein-unique, protein-multimatch & exon-unique, exon-multimatch categories.
<p>A .zip file containing the source data files & Jupyter notebook for analysis of the <em>Prymnesium parvum</em> 12B1 PKZILLA-detecting proteomic results (<a href="https://doi.org/10.5281/zenodo.10023441">https://doi.org/10.5281/zenodo.10023441</a>), and the resulting files from the workflow. See "Analysis of proteomic results" section of the manuscript Materials and Methods for further detail. </p> <p><strong>Key files:</strong></p> <ul> <li>'PKZILLA-1_classify_peptides.txt' - A plaintext report of the # of classified peptides for PKZILLA-1</li> <li>'PKZILLA-2_classify_peptides.txt' - A plaintext report of the # of classified peptides for PKZILLA-2</li> <li>'./hierarchical_classified_xlsx/' - Excel spreadsheets with the classified peptides for PKZILLA-1 and PKZILLA-2</li> <li>'./Process_into_polypeptide_coordinates/' - Workflow, results, and plots for back-alignment of peptides back to PKZILLA-1 and PKZILLA-2 genomic loci</li> </ul>
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain
Figure. Percent mortality of R. padi treated with crude venom of P. birmanica (CVPB), crude venom of P. sumatrana (CVPS), protein fraction of P. birmanica venom (PFPB), and protein fraction of P. sumatrana venom (PFPS). Bars with the same letters represent nonsignificant differences. in Insect-specific peptides in the venom of wolf spiders (Araneae: Lycosidae)
Figure. Percent mortality of R. padi treated with crude venom of P. birmanica (CVPB), crude venom of P. sumatrana (CVPS), protein fraction of P. birmanica venom (PFPB), and protein fraction of P. sumatrana venom (PFPS). Bars with the same letters represent nonsignificant differences.
Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)
<p>Availability of highly parallelized immunoassays has renewed interest in the discovery of serology-based biomarkers for infectious diseases. Protein and peptide microarrays now provide a high-throughput platform for immunological screening of potential antigens and B-cell epitopes. However, there is still a need to prioritize relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize antigenic targets starting from a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We applied this method to the prioritization of potential diagnostic antigens and peptides in a number of pathogen proteomes and human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucellosis), Coxiella burnetii (Q fever), Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia), Leishmania braziliensis (Leishmaniasis), Leptospira interrogans (Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis (Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis (Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus pyogenes (Group A Streptococcal infections), Toxoplasma gondii (Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). After training a linear regression model the method achieves good to excellent performance on most species, measured by the enrichment of validated antigens at the top of the ranking. An unbiased validation using independent data sets shows APRANK is successful in predicting antigenicity for all pathogen species tested. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.</p>
Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)
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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>
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>
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>
PepBench: Dataset for Protein-Binding Peptide Design
<p>Datasets and splits of protein-peptide complexes benchmark from <a href="https://arxiv.org/abs/2402.13555">PepGLAD</a>.</p> <p>V2 Updates:</p> <ol> <li>The size of ProtFrag augmentation dataset is 70498 instead of 70645. The latest index file has deleted duplicated entries.</li> <li>Clustering results for complexes in training/validation sets are uploaded in train_valid.</li> </ol>
Protein Identification by Nanopore Peptide Profiling
<p>This dataset belongs to “Protein Identification by Nanopore Peptide Profiling” and describes the raw data and analysis of tryptic digested peptides translocating through a mutant Fragaceatoxin C nanopore. A jupyter notebook describing the analysis and structure is added to this dataset.</p> <p> </p> <p><strong>Data description:</strong></p> <p><strong>Protein Identification by Nanopore Peptide Profiling.ipynb</strong></p> <p> Jupyter notebook contained data analysis of data contained in data_0.zip and data_1.zip (Python 3.7)</p> <p><strong>python_scripts.zip</strong></p> <p> Supplementary scripts belonging to “Protein Identification by Nanopore Peptide Profiling.ipynb”. See explanation of custom classes in the jupyter notebook.</p> <p><strong>data_0.zip</strong> - Folder containing raw electrophysiology data and result after analysis with “Protein Identification by Nanopore Peptide Profiling.ipynb”, with each folder containing the following:</p> <p> Alpha casein: Tryptic digest of alpha casein</p> <p> Beta casein: Tryptic digest of beta casein</p> <p> BSA: Tryptic digest of bovine serum albumin</p> <p> Control: Tryptic digest of water (no protein, control measurement)</p> <p> Cytochrome c: Tryptic digest of cytochrome c</p> <p> DHFR_His6: Tryptic digest of dihydropholate reductase (His6 tagged)</p> <p> EFP: Tryptic digest of elongation factor P</p> <p> HMW1Act: Tryptic digest of high molecular weight adhesin protein</p> <p><strong>data_1.zip</strong> - Folder containing raw electrophysiology data, comma-separated MS peptide masses, and result after analysis with “Protein Identification by Nanopore Peptide Profiling.ipynb”, with each folder containing the following:</p> <p> Lysozyme: Tryptic digest of lysozyme </p> <p> PAN: Tryptic digest of proteasome-activating nucleotidase</p> <p> TbpA_Y27A: Tryptic digest of periplasmic binding protein</p> <p> Trypsin: Tryptic digest of bovine trypsin</p> <p> Mass_spec: csv files containing measured ESI-MS peptides</p> <p> Lysozyme synthetic peptides: Synthetic peptides:</p> <p> Lys1: TPGSR</p> <p> Lys2alk: C(+57.02)ELAAAMK</p> <p> Lys3: HGLDNYR</p> <p> Lys4alk: WWC(+57.02)NDGR</p> <p> Lys5: GTDVQAWIR</p> <p> Lys6alk: GYSLGNWVC(+57.02)AAK</p> <p> Lys7: FESNFNTQATNR</p> <p>The structure of the data files is registered data_1.zip in <strong>'index.csv'</strong> (digested proteins) and <strong>'index</strong><strong>_peptides</strong><strong>.csv'</strong> (synthetic peptides) contained in the data folder. In this file, we describe the protein that was measured as well as the folder location and the expected baseline / standard deviation.<br> <br> <strong>Structure of <em>./data/index.csv</em></strong></p> <p><strong>Protein (string) | Folder (string) | Baseline (pA) (float) | Baseline Error (pA) (float)</strong></p> <p> </p> <p>In each <strong>Folder</strong>, there is another <strong>'index.csv'</strong>, explaining which files are with protein and which are without (blank).<br> <br> <strong>Structure of <em>./data/[protein]/[repeat]/index.csv</em></strong></p> <p><strong>blank (boolean) | fname (string)</strong></p> <p> </p> <p>Each folder in data_0.zip and data_1.zip contains a folder for each measure protein, which contains a folder for each repeat. The repeats contain raw axon binary files (.abf), each file contains measurement conditions as follows:</p> <p> [Date of measurement]_[Pore type]_[Buffer conditions]_[added analyte(s)]_[operator initials]</p> <p> <em>e.g</em>: 20200312_1M_KCl_50mM_Citricacid_50mM_BTP_pH_38_FraC_G13F_neg70mV_20ul_CytC_TrypsinGold_FL_0000</p> <p> Measured on 12-03-2020, in 1M KCl buffered with Citricacid (50 mM) adjusted using bis-tris-propane to pH 3.8, using Fragaceatoxin C mutant G13F at a negatively applied potential of 70 mV. 20 µL cytochrome c was added to the cis compartment.</p> <p>The total volume of the container used for all electrophysiology experiments was 400 µL, all samples were prepared at a 1 g/L concentration. A prefix “perf” before analyte description indicates that the chamber was flushed with approximately 2 mL fresh buffer prior to analysis. The buffer condition "BTP" means bis-tris-propane, which is used to titrate to the exact pH of 3.8.</p> <p>Each analysed folder contains<strong> results.pkl</strong> file, containing the analysis result as provided by “Protein Identification by Nanopore Peptide Profiling.ipynb” - see the jupyter notebook</p> <p>Each analysed folder contains <strong>results_analysis.xlsx</strong>, which contains sheets with excluded currents, standard deviations, dwell time and beta value for the pore without analyte added “Blank” and results from the analyte added in “Results”. Parameters used for fitting are contained in “Parameters”. The “Histograms” tab shows the raw data of the excluded current spectra.</p> <p> </p> <p><strong>mass_spec_peaks.zip</strong> – Folder containing mass spectrometry files as analysed by PEAKS Studio</p> <p>The folder contains an subfolder for each protein measured using electrospray ionisation mass spectrometry (ESI-MS).</p> <p> acasein: alpha casein protein</p> <p> b_casein: beta casein protein</p> <p> BSA: bovine serum albumin</p> <p> CytC: cytochrome C digested</p> <p> DHFR: dihydropholate reductase</p> <p> HMW1_Act: high molecular weight adhesin protein</p> <p> PAN: proteasome-activating nucleotidase</p> <p> ThBP: periplasmic thiamine binding protein</p> <p> Trypsin: bovine trypsin</p>
Fluorescence complementation enables quantitative imaging of cell penetrating peptide-mediated protein delivery in plants including WUSCHEL transcription factor
<p>These are data related to the manuscript titled "Fluorescence complementation enables quantitative imaging of cell penetrating peptide-mediated protein delivery in plants including WUSCHEL transcription factor" whose preprint can be found here: https://doi.org/10.1101/2022.05.03.490515</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>
Simulation trajectories for CLas BamA protein interacting with high-affinity short peptides
<p>Trajectories of molecular dynamics simulations done using Schrodinger Desmond for interaction of BamA protein of <em>Candidatus </em>Liberibacter asiaticus with three short peptides with a high affinity towards BamA. Simulations were conducted for 100 ns. </p>
Dataset for Peptide binder design with inverse folding and protein structure prediction
<p>Dataset for a paper on peptide design</p> <p> </p> <p><br> mutated_peptides - results for randomly intriduced mutations in protein-peptide complexes that can be predicted at 2 Å (Figure 1)<br> pdb_peptide - variation in the number of recycles (1-10) for 96 peptides (Figure 1)<br> minibinder - results for the minibinder set (Figure 2)<br> Pfam - results for the Pfam set (Figures 4+5)<br> protein_mpnn - results on protein_mpnn test set (Figure 6)</p> <p> </p> <p> </p>
GP96 Heat Shock Protein-Peptide Complex Vaccine in Treating Patients With Recurrent or Progressive Glioma
ClinicalTrials.gov study NCT00293423. IPD Sharing: NO. Countries: 1. Publications: 2.
The predicted and experimental peptide binding information (PEPBI) database described in: A paired database of predicted and experimental protein peptide binding information
Open the record for dataset details and reuse information.
Data from: Delivered complementation in planta (DCIP) enables measurement of peptide-mediated protein delivery efficiency in plants
Open the record for dataset details and reuse information.
Figure 1 in A fossil protein chimera; difficulties in discriminating dinosaur peptide sequences from modern cross-contamination
Figure 1. Tandem mass spectrum from high-resolution (HCD) fragmentation analysis of the peptide sequence (GPPGESGAVGPAGPIGSR) matched from our analysis of ostrich bone collagen that is homologous to the peptide proposed as unique to T. rex and B. canadensis.
Figure 2 in A fossil protein chimera; difficulties in discriminating dinosaur peptide sequences from modern cross-contamination
Figure 2. Tandem mass spectrum of the peptide sequence claimed as being endogenous to both dinosaurs with sequence (GLPGESGAVGPAGPPGSR) downloaded from the B. canadensis analysis by Schweitzer et al. [14].
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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.