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Fig. 2 — Chromatogram for A in Antimicrobial activity of the crude peptide extracts from Blackfin sea catfish Arius jella Day, 1877
Fig. 2 — Chromatogram for A. jella peptide extract using FPLC: a) 5 % Sep-Pak fraction, b) 40 % Sep-Pak fraction, and c) 80 % Sep-Pak fraction
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>
Dataset for What are key factors for detection of peptides using mass spec-trometry on boron-doped diamond surfaces?
<p>The data set to paper: </p> <p>What are key factors for detection of peptides using mass spec-trometry on boron-doped diamond surfaces?</p> <p>Juvissan Aguedo1, Marian Vojs2, Martin Vrška2, Marek Nemcovic3, Zuzana Pakanova3, Katerina Aubrechtova Dragounova4, Oleksandr Romanyuk4, Alexander Kromka4, Marian Varga5, Michal Hatala6, Marián Marton2 and Jan Tkac1,*</p> <p>1 Institute of Chemistry, Slovak Academy of Sciences, Bratislava, Slovakia<br>2 Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Bratislava, Slovakia<br>3 Centre of Excellence for Glycomic, Slovak Academy of Sciences, Bratislava, Slovakia<br>4 Institute of Physics, Czech Academy of Sciences, Prague 6, Czech Republic<br>5 Institute of Electrical Engineering, Slovak Academy of Sciences, Bratislava, Slovakia<br>6 Department of Graphic Arts Technology and Applied Photochemistry, Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovakia</p> <p>* corresponding author: jan.tkac@savba.sk</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 5. 2023 - 31. 5. 2024</p> <p>All the data shown in the pictures are provided with a described sample in X-Y or X-Y-Z format.<br>The respective figure to which the data belong is always provided in high resolution.<br>The data are in the following formats: <br>Scheme 1: pdf<br>Figure 1: pdf<br>Figure 2: pdf, csv<br>Figure 3: pdf<br>Figure 4: pdf, csv<br>Figure 5: pdf<br>Figure 6: pdf, csv<br>Figure 7: pdf, csv<br>Figure 8: pdf, csv<br>Figure 9: pdf, csv<br><br></p> <p>The comma separated values file (csv) always contain the description of the columns in the first row. In case of composed image the name of the file corresponds to the corresponding figure.</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI: 10.3390/nano14151241</p>
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
Research raw data supporting "Activatable cell-biomaterial interfacing with photo-caged peptides"
<p>Raw data supporting the publication;</p> <p>Lin Y. et al., Activatable cell-biomaterial interfacing with photo-caged peptides, 2018, Chemical Science, DOI: 10.1039/c8sc04725a.</p>
Figure 1. Prostaglandin E 2 in The role of a novel Wolbachia (Rickettsiales: Anaplasmataceae) synthetic peptide, WolFar, in regulating prostaglandin levels in the hemolymph of Acheta domesticus (Orthoptera: Gryllidae)
Figure 1. Prostaglandin E 2 activity of Acheta domesticus at different postinjection times relative to the concentrations of WolFar applied. Each point represents the mean ± SE.
Figure 2 in The role of a novel Wolbachia (Rickettsiales: Anaplasmataceae) synthetic peptide, WolFar, in regulating prostaglandin levels in the hemolymph of Acheta domesticus (Orthoptera: Gryllidae)
Figure 2. Formation of nodules in the internal system and fat body of Acheta domesticus following injections of 100% concentration of WolFar. The red triangles indicate the positions of the nodules. A: Negative control; B: at 24 h; C: at 48 h; D: at 72 h.
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.
UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow
<p>UniSpec is a comprehensive DL spectrum predictor that can predict the intensity of the entire HCD MS/MS fragment ion series, going beyond existing tools limited to b/y ion series. </p> <p>All datasets developed for UniSpec model are shared on Zenodo as part of the UniSpec publication, "UniSpec: Deep Learning for Predicting Comprehensive Peptide Fragment Ion Series to Improve Peptide-Spectrum Matches from Shotgun Proteomics Experiments".</p> <p>This includes UniSpec datasets, downstream evaluation and analysis, and application case studies.</p> <p>1. pre-processed training, evaluation and testing data for machine learning;</p> <p> UniSpec-Datasets.7z, Readme_UniSpecDatasets.txt</p> <p>2. Streamlined input datasets based on the fragmentation dictionary;</p> <p> Streamlined_inputdatasets.7z, Readme_Streamlined_inputdatasets.txt</p> <p>3. Predictions on the validation and test sets;</p> <p> UniSpecPred_Validation-Test.7z, Readme_Predictons_ValidationTest.txt</p> <p>4. Evaluation by comparison with Prosit;</p> <p> a. Predictions: prosit_and_unispec_predictions.7z, Readme_prosit_and_unispec_predictions.txt</p> <p> b. Cosine similarity scores: prosit_vs_unispec_CS.7z, Readme_prosit_vs_unispec_CS.txt</p> <p>5. CSS for Different HCD Fragment Ion Series;</p> <p> CS_for_ion_splits.tsv</p> <p>6. Application 1: PSM rescoring;</p> <p> PSM rescoring_zipfiles.7z, PSM rescoring_readme.txt</p> <p>7. Application 2: In-silico spectral library search </p> <p> in-silico_librarysearch.7z, in-silico_librarysearch_readme.txt</p> <p> </p>
Fig. 1 in Toxicity of the Jaburetox peptide to the multi-host insectpest Helicoverpa armigera (Lepidoptera: Noctuidae) larvae
Fig. 1. Feeding assay with neonate Helicoverpa armigera larvae on maize leaf discs coated with Jaburetox (Jbtx) and Jaburetox Δ-β (Jbtx Δ-β). (a) Accumulated mortality; (b) daily consumption. The arrow indicates the d Jaburetox toxin feeding was stopped. Means with the same letter within the same d do not differ with each other (Tukey test, P <0.05).
Fig. 2 in Toxicity of the Jaburetox peptide to the multi-host insectpest Helicoverpa armigera (Lepidoptera: Noctuidae) larvae
Fig. 2. Percentage of neonate larvae fed with leaf discs treated with Jaburetox, Jaburetox Δ-β, or control solutions that reached the third, fourth, and fifh instar at d 8 of experiment.
Fig. 4 in Toxicity of the Jaburetox peptide to the multi-host insectpest Helicoverpa armigera (Lepidoptera: Noctuidae) larvae
Fig. 4. (a) Percentage of third instar larvae fed with leaf discs treated with Jaburetox and control solution that reached the third, fourth, and fifh instar along the experiment; (b) larvae observed on d 8 of bioassay (1 d before all Jaburetox treated had died): on the lef, Jaburetox larvae are in third instar (80 µg per 5 cm2); on the right, control larvae are in the fifh instar.
Fig 3 in Toxicity of the Jaburetox peptide to the multi-host insectpest Helicoverpa armigera (Lepidoptera: Noctuidae) larvae
Fig 3. Feeding assay with third instar Helicoverpa armigera larvae on maize leaf discs coated with Jaburetox (Jbtx). (a) Accumulated mortality; (b) daily consumption; (c) larvae weight. The arrow indicates the d Jaburetox toxin feeding was stopped. Means with the same letter within the same d do not differ from each other (Student t-test, P <0.05).
Gaussian16 data for "Dynamic electronic structure fluctuations in the de novo peptide ACC-dimer revealed by first-principles theory and machine learning"
<p>This is the Gaussian 16 input and corresponding output, which was used as input into the machine learning presented in the paper titled "Dynamic electronic structure fluctuations in the de novo peptide ACC-dimer revealed by first-principles theory and machine learning". This upload is required before submission of the paper.<br><br>The 1001 and 100 snapshots from different extractions are preserved in separated directories. Each snapshot directory <code>*_snapshot</code> has the initial GROMACS snapshot <code>test_*.pdb</code> , the geometry after truncating the solvation shell in various formats, the Gaussian16 input, qsub input and the output directory <code>*.1</code> with a JobID number assigned by qsub. The output directory has the standard output from Gaussian in a <code>.log</code> file and <code>grep</code>ed output from the <code>.fchk</code> file in <code>*.out</code> .</p>
Training data for "PepINVENT: Generative peptide design beyond the natural amino acids"
<p>The zipped file contains the training and the validation data used to train the PepINVENT model.</p>
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>
pH-responsive aminolipid nanocarriers for antimicrobial peptide delivery
<p>Raw data for the paper entitled, "pH-responsive aminolipid nanocarriers for antimicrobial peptide delivery" published in the Journal of Colloid and Interface Science on 11 June 2021.</p> <p> </p> <p>Abstract:</p> <p>pH-responsive aminolipid self-assemblies are promising platforms for the targeted delivery of antimicrobial peptides (AMPs), with the potential to improve their therapeutic efficiency and physicochemical stability. pH-sensitive nanocarriers based on dispersed self-assemblies of 1,2-dioleoyl-3-dimethylammonium-propane (DODAP) with the human cathelicidin LL-37 in excess water were characterized at different pH values using small-angle X-ray scattering, cryogenic transmission electron microscopy, and dynamic light scattering. Fluorescence and electrophoretic mobility measurements were used to probe the encapsulation efficiency of LL-37 and the nanocarriers’ surface potential. Upon decreasing pH in the DODAP/water systems, normal oil-in-water emulsions at pH ≥ 5.0 transitioned to emulsions encapsulating inverse hexagonal and cubic structures at pH between 4.5 and 4.0, and mostly positively-charged vesicles at pH < 4.0. These colloidal transformations are driven by the protonation of DODAP upon pH decrease. The larger lipid-water interfacial area provided by the DODAP self-assemblies at pH ≤ 4.5 allowed for an adequate encapsulation efficiency of LL-37, favouring the formation of vesicles in a concentration-dependent manner. Contrary, LL-37 was found to dissociate from the emulsion droplets at pH 6.0. The knowledge on the pH-triggered self-assembly of LL-37 and DODAP, combined with the results on peptide release from the structures contribute to the fundamental understanding of lipid/peptide self-assembly. The results can guide the rational design of future pH-responsive AMP delivery systems.</p>
Supporting data for: "Hybrid Computational-Experimental Data-Driven Design of Self-Assembling π-Conjugated Peptides"
<p>This repository contains supporting data and code for the paper titled "Hybrid Computational-Experimental Data-Driven Design of Self-Assembling π-Conjugated Peptides" by Kirill Shmilovich, Sayak Subhra Panda, Anna Stouffer, John D. Tovar, and Andrew L. Ferguson.</p>
MD trajectories for "Communication Breakdown: Dissecting the COM Interfaces between the Subunits of Nonribosomal Peptide Synthetases"
<p>This dataset contains Amber MD trajectories for the MD simulations described in the manuscript "Communication Breakdown: Dissecting the COM Interfaces between the Subunits of Nonribosomal Peptide Synthetases" by Christopher D. Fage, Simone Kosol, Matthew Jenner, Carl Öster, Angelo Gallo, Milda Kaniusaite, Roman Steinbach, Michael Staniforth, Vasilios G. Stavros, Mohamed A. Marahiel, Max J. Cryle, and Józef R. Lewandowski published in ACS Catalysis (<a href="https://doi.org/10.1021/acscatal.1c02113">https://doi.org/10.1021/acscatal.1c02113</a>). If you use these data please cite the original manuscript (follow the manuscript DOI for the final citation, which was not available at the time of publishing this data set). </p> <p>To reduce their size the trajectories were stripped of water and ions. Only frames every 1 ns or 5 ns were saved. Please see the Supporting Information of the source manuscript for the conditions for the simulations. </p>
Microscale termophoresis fluorescence time traces testing the interaction between human survivin and a peptide derived from hSgol2
<p>Microscale termophoresis fluorescence time traces testing the interaction between human survivin and a peptide derived from hSgol2 ( sequence: ECQVKKVNKMTSKSKKRKTS). Survivin was chemically labelled and titrated with different concentrations of hSgol2 peptide.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.