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1,744 results for “Peptides”

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zenodo40/100

Molecular dynamics simulation data of designed cyclic peptide (ligand-only)

<p>Trajectories&nbsp;of <strong>ligand-only </strong>simulation&nbsp;and simulation set-up files of designed cyclic peptide as MDM2 binders.&nbsp;<br> The original paper of these designed cyclic peptide:&nbsp;Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein&ndash;protein interaction by cyclic &beta;-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386&ndash;10393. http://doi.org/10.1039/C6OB01510G</p>

opencc-by-4.0Apr 2020View details →
zenodo40/100

Proteomes in 3D - Correlation Analyses - Fluxes vs LiP Peptides

<p>Analysis of metabolic fluxes that correlate with protein structural changes across 8 metabolic conditions in E. coli, associated to the manuscript by Cappelletti et al., currently under consideration. Results are expressed as data from a given metabolic condition relative to growth in glucose.</p> <p>Plot title: Protein name_Peptide sequence</p> <p>X-axis:&nbsp;Log<sub>2</sub> FC [LiP peptide (condition/glucose)]</p> <p>Y-axis:&nbsp;Log<sub>2</sub> FC [Flux (condition/glucose)]</p> <p>The following files contain the statistical parameters of the analysis (p and q-values and R-square values) for all the uploaded plots:</p> <p>- Statistics_p_and_q_values.txt</p> <p>- Statistics_R2.txt</p>

opencc-by-4.0Jul 2020View details →
zenodo40/100

Input Files for Peptide Translocation Across Phospholipid Membranes Using Various Collective Variables and Martini Coarse-Grained Simulations

<p>Input files for publication: Ivo Kabelka, Radim Brožek, and Robert V&aacute;cha: Selecting Collective Variables and Free Energy Methods for Peptide Translocation Across Membranes, Journal of Chemical Information and Modeling, submitted</p>

opencc-by-4.0Nov 2021View details →
zenodo40/100

Research data supporting "Pericyte seeded dual peptide scaffold with improved endothelialization for vascular graft tissue engineering"

<p>Raw research data supporting the paper:</p> <p>Campagnolo, P. <em>et al</em>., Pericyte seeded dual peptide scaffold with improved endothelialization for vascular graft tissue engineering, 2016, Advanced Healthcare Materials, 5(23), 3046-3055.</p> <p> </p>

opencc-by-4.0Nov 2016View details →
zenodo40/100

Identification of significant KLH-derived peptide - Fig. S2

<p>Supplemental figure 2 (S2) of the manuscript “Surface LAMP-2 is an endocytic receptor that diverts antigen internalized by human dendritic cells into highly immunogenic exosomes”  by Dario A. Leone et al. (Journal of Immunology). </p> <p>ProPresent® antigen presentation assay from ProImmune was used here to identify potential differences in the presentation of immunogenic regions in Keyhole Limpet Hemocyanin (KLH), compared to KLH conjugated to anti-LAMP2 antibody (H4B4*KLH). The peptide eluted from HLA-DR of MoDC isolated from four different donors were identified using mass spectrometry (LCMSMS)-based analysis in order to identify the putative immunogenic peptides from KLH and two endogenous protein – myeloperoxidase(MPO) and heat shock protein 70 (HSc70). </p>

opencc-by-4.0Apr 2017View details →
zenodo40/100

In silico identified signal peptides of Chlamydomonas reinhardtii

<p><strong>Overview</strong></p> <p><em>Chlamydomonas reinhardtii&nbsp;</em>theoretical signal peptides identified&nbsp;by<em>&nbsp;</em>SignalP 4.0 in a protein data set described below:</p> <ul> <li>Protein data set came from &quot;The Genome Portal of the Department of Energy Joint Genome Institute&quot; (http://genome.jgi.doe.gov/)</li> <li>Protein sequences were evaluated in SignalP 4.0&nbsp;Server (http://www.cbs.dtu.dk/services/SignalP/)</li> </ul> <p>&nbsp;</p> <p><strong>File used</strong></p> <p>Chlre4_best_proteins.fasta.gz -&gt; Protein dataset version used for analysis</p> <p>&nbsp;</p> <p><strong>Workflow</strong>&nbsp;</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ______Chlre4_best_proteins.fasta.gz_______</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|</p> <p>&nbsp; &nbsp; &nbsp;Chlre4_best_proteins_fasta_protein_woSP.fasta &nbsp; &nbsp; &nbsp; Chlre4_best_proteins_signalPeptide.fasta</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ___Chlre4_best_proteins_signalPeptide_unique.fasta___</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;Chlre4_best_proteins_signalPeptide_unique.aln &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Signal Peptide Anotation from aligned.xlsx</p> <p>&nbsp;</p> <p><strong>Info</strong></p> <p>Chlre4_best_proteins_fasta_protein_woSP.fasta &nbsp; -&gt; Mature protein sequences from proteins identified without signal peptide</p> <p>Chlre4_best_proteins_signalPeptide.fasta &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;-&gt; Identified signal peptide</p> <p>Chlre4_best_proteins_signalPeptide_unique.fasta -&gt; Unique identified signal peptide</p> <p>Chlre4_best_proteins_signalPeptide_unique.aln &nbsp; &nbsp;-&gt; Align signal peptides (UGENE)</p> <p>Signal Peptide Annotation from aligned.xlsx &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;-&gt; Signal peptide list, highlighted in orange theoretical tested.</p> <p>&nbsp;</p> <p><strong>Citations</strong></p> <p>For use of signal peptide dataset, please cite:</p> <p>Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433</p> <p>and&nbsp;</p> <p><strong>SignalP 4.0: discriminating signal peptides from transmembrane regions</strong><br> Thomas Nordahl Petersen, S&oslash;ren Brunak, Gunnar von Heijne &amp; Henrik Nielsen<br> <em>Nature Methods</em>,&nbsp;<strong>8</strong>:785-786,&nbsp;<strong>2011</strong><br> <br> doi:&nbsp;10.1038/nmeth.1701<br> PMID:&nbsp;21959131<br> Supplementary materials:&nbsp;nmeth.1701-S1.pd</p> <p>and&nbsp;</p> <p><strong>The genome portal of the Department of Energy Joint Genome Institute: 2014 updates</strong></p> <p>H. Nordberg, M. Cantor, S. Dusheyko, S. Hua, A. Poliakov, I. Shabalov, T. Smirnova, I. V. Grigoriev, I. Dubchak, ,</p> <p>Nucleic Acids Res. 42, 26&ndash;31. <strong>2014</strong>&nbsp;</p> <p>doi:10.1093/nar/gkt1069.</p> <p>&nbsp;</p>

opencc-by-sa-4.0May 2017View details →
zenodo40/100

Comprehensive 16s rRNA sequencing and metabolomics to investigate the effect of anticancer bioactive peptides combined with oxaliplatin on gastric cancer

<p>背景: 胃癌的发生、发展与肠道菌群密切相关。既往研究发现抗癌生物活性肽(ACBP)与奥沙利铂(OXA)联合对胃癌有显着的治疗作用,但ACBP-OXA对肠道菌群的影响仍不清楚。</p><p><strong>Methods:</strong> We established a nude mouse model of ACBP-OXA combined therapy for gastric cancer, the diversity of gut microbiota and fecal metabolomics were studied, and the correlation between gut microbiota and metabolites was analyzed.</p><p><strong>Results:&nbsp;</strong>ACBP-OXA联合疗法对肠道菌群具有很强的调节作用。16s rRNA研究发现,在门中,ACBP-OXA处理后,厚壁菌门和拟杆菌门的相对丰度发生显着变化,厚壁菌门的相对丰度下降,拟杆菌门的相对丰度增加。属内,ACBP-OXA组中毛螺菌科NK4AB6组的相对丰度降低,odpribacter和拟杆菌属的相对丰度增加。ACBP组乳酸菌相对丰度增加,ACBP-OXA和OXA组葡萄球菌相对丰度下降。GO和KEGG研究发现联合治疗机制与代谢和免疫有关。通过代谢组学研究,本研究发现差异代谢物与Benzenoids、Ligans、neoligans、其中脂质和脂类大多参与酪氨酸代谢、不饱和脂肪酸生物合成、苯丙氨酸代谢α-生物过程。将代谢组学与16s rRNA长寿素相结合,发现氨基酸相关代谢物与Jetgalilicus、Staphylococcus、Proteiniphilum等细菌属相关。</p><p>结论: &nbsp; ACBP与ACBP-OXA联合治疗可能通过改变肠道菌群的分布多样性和菌群结构来改善和恢复胃癌裸鼠的肠道菌群,这可能是抑制胃癌发生、发展的关键。该研究为进一步研究ACBP-OXA在胃癌治疗中的应用提供了新的方向。</p>

opencc-by-4.0Dec 2022View details →
zenodo40/100

Human antimicrobial peptide inactivation mechanism of enveloped viruses

<p>This dataset provides the raw data supporting the paper: Human antimicrobial peptide inactivation mechanism of enveloped viruses. <a href="https://doi.org/10.1016/j.jcis.2023.11.055">https://doi.org/10.1016/j.jcis.2023.11.055</a></p><p>It comprises the infectivity data (Figure 1), DLS data (Figure 2 and Figures S1-5), cryo-TEM images (Figure 2), SAXS data (Figure 3), SANS data (Figure 3), and the Zeta-potential measurement (in text).</p><p>Setup and conditions for the experiments are described in the experimental section of the published (open access) manuscript.</p>

opencc-by-4.0Nov 2023View details →
zenodo40/100

Dataset for article: Antimicrobial peptide induced colloidal transformations in bacteria-mimetic vesicles: Combining in silico tools and experimental methods

<p><strong>Dataset for publication:</strong></p><p>Antimicrobial peptide induced colloidal transformations in bacteria-mimetic vesicles: Combining in silico tools and experimental methods<br><i>Rafael V.M. Freire, Yeny Pillco-Valencia, Gabriel C.A. da Hora, Madeleine Ramstedt, Linda Sandblad, Thereza A. Soares, Stefan Salentinig</i><br>Journal of Colloid and Interface Science Volume 596, 15 August 2021, Pages 352-363 &nbsp;https://doi.org/10.1016/j.jcis.2021.03.060</p><p>Setup and conditions for the experiments are described in the experimental section of the published (open access) manuscript.</p><p>Data description in README.txt file.</p>

opencc-by-4.0Dec 2023View details →
zenodo40/100

Self-Assembled Proteomimetic (SAP) with Antibody-like Binding from Short PNA-Peptide Conjugates

<p><span><span>Affinity proteins </span><span>based on </span><span>a </span><span>three-helix </span><span>bundle</span> <span>(</span><span>affibodies, </span><span>alphabodies</span><span> and computationally </span></span><span><span>de novo</span></span><span> <span>designed</span><span> ones)</span><span> have shown to be a general platform to discover binders with properties reminiscent of </span><span>antibodies</span><span>, combining </span><span>high </span><span>target </span><span>specificity</span><span> with </span><span>affinities reaching well below</span> <span>the </span><span>nanomolar</span><span>.</span> <span>Herein</span><span>,</span><span> we report a new strategy</span><span>, coined self-assembled proteomimetic (SAP)</span><span>,</span><span> to mimic </span><span>such</span><span> three-helix bundle</span><span> architecture with a hybridization-enforced two-helix </span><span>coiled</span> <span>coil</span><span> that is obtained by templated</span> <span>native chemical ligation (</span><span>T-</span><span>NCL) of PNA-peptide conjugates.</span> <span>This SAP </span><span>strategy</span> <span>stands out by</span><span> its</span><span> synthetic accessibility reducing the length on the longest </span><span>synthetic</span><span> peptide to </span><span>less than 30 amino acids, readily attainable by standard SPPS methodologies</span><span>. We show that the </span><span>T</span><span>-NCL dramatically accelerates the </span><span>ligation</span><span>, enabling this chemistry to </span><span>proceed</span> <span>in a combinatorial fashion </span><span>at</span><span> low</span> <span>micromolar</span><span> concentration</span><span>s</span><span>.</span> <span>We </span><span>demonstrate</span> <span>that small </span><span>combinatorial </span><span>libraries of </span><span>SAP</span><span>s</span><span> can be prepared in one operation and used directly in </span><span>affinity selection</span><span>s</span><span> against a target of interest </span><span>with an</span><span> LC-MS </span><span>analysis</span><span> of the fittest binders</span><span>.</span> <span>Moreover, we </span><span>show</span><span> that </span><span>the underlying</span> <span>design</span><span> paradigm</span><span> is functional for</span><span> SAPs based on structurally distinct three-helix peptides </span><span>aimed at</span><span> different </span><span>therapeutic </span><span>targets, namely</span> <span>HER2 </span><span>and</span><span> spike&rsquo;s RBD</span><span>,</span></span> <span><span>reaching picomolar </span><span>affinities</span></span><span><span>. We further </span><span>illustrate </span><span>that the</span> <span>affinity </span><span>of the </span><span>S</span><span>AP</span><span> can be allosterically regulated using a toehold displacement</span><span> of the hybridizing PNAs</span><span> to disrupt the </span><span>coiled coil</span><span> stabilization.</span> <span>Finally, w</span><span>e show that </span><span>an RBD-targeting </span><span>SAP effectively inhibits viral </span><span>entry </span><span>of SARS-CoV-2</span> <span>with an IC</span></span><span><span>50</span></span><span><span> of </span><span>2.8</span> <span>nM</span><span>.</span></span><span>&nbsp;</span></p>

opencc-by-4.0Nov 2024View details →
zenodo40/100

Novel Peptide-Based PET Probe for Non-invasive Imaging of C-X-C Chemokine Receptor Type 4 (CXCR4) in Tumors

<p>These are RAW data datasets of the following final paper</p> <p>Trotta, A.M., Aurilio, M., D&#39;Alterio, C., Ieran&ograve;, C., Di Martino, D., Barbieri, A., Luciano, A., Gaballo, P., Santagata, S., Portella, L., Tomassi, S., Marinelli, L., Sementa, D., Novellino, E., Lastoria, S., Scala, S., Schottelius, M., Di Maro, S.</p> <p>Novel Peptide-Based PET Probe for Non-invasive Imaging of C-X-C Chemokine Receptor Type 4 (CXCR4) in Tumors, (2021) Journal of Medicinal Chemistry, 64 (6), pp. 3449-3461. ISSN 00222623</p> <p>https://doi.org/10.1021/acs.jmedchem.1c00066</p> <p>Abstract</p> <p>The recently reported CXCR4 antagonist 3 (Ac-Arg-Ala-[DCys-Arg-2Nal-His-Pen]-CO2H) was investigated as a molecular scaffold for a CXCR4-targeted positron emission tomography (PET) tracer. Toward this end, 3 was functionalized with 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) and 1,4,7-triazacyclononanetriacetic acid (NOTA). On the basis of convincing affinity data, both tracers, [68Ga]NOTA analogue ([68Ga]-5) and [68Ga]DOTA analogue ([68Ga]-4), were evaluated for PET imaging in &ldquo;in vivo&rdquo; models of CHO-hCXCR4 and Daudi lymphoma cells. PET imaging and biodistribution studies revealed higher CXCR4-specific tumor uptake and high tumor/background ratios for the [68Ga]NOTA analogue ([68Ga]-5) than for the [68Ga]DOTA analogue ([68Ga]-4) in both in vivo models. Moreover, [68Ga]-4 and [68Ga]-5 displayed rapid clearance and very low levels of accumulation in all nontarget tissues but the kidney. Although the high tumor/background ratios observed in the mouse xenograft model could partially derive from the hCXCR4 selectivity of [68Ga]-5, our results encourage its translation into a clinical context as a novel peptide-based tracer for imaging of CXCR4-overexpressing tumors.</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Mar 2021View details →
zenodo40/100

Dataset Friebus-Kardash et al, A chemerin peptide analog stimulates tumor growth in two xenograft mouse models of human colorectal carcinoma

<p>Dataset Friebus-Kardash et al, A chemerin peptide analog stimulates tumor growth in two xenograft mouse models of human colorectal carcinoma</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2021View details →
zenodo40/100

Gene-guided discovery and ribosomal biosynthesis of anticancer moroidin peptides

<p>Moroidin is a bicyclic plant octapeptide with tryptophan side-chain crosslinks, originally isolated as a pain-causing agent from Australian stinging tree <em>Dendrocnide moroides</em>. Moroidin and its analog celogentin C, derived from <em>Celosia argentea</em>, are inhibitors of tubulin polymerization and, thus, lead structures for cancer therapy. However, low isolation yields from source plants and challenging organic synthesis hinder moroidin-based drug development. Here, we present biosynthesis as an alternative route to moroidin-type bicyclic peptides and report that they are ribosomally synthesized and posttranslationally modified peptides (RiPPs) derived from BURP-domain peptide cyclases in plants.</p> <p>This submission includes the 793 plant&nbsp;transcriptomes from the 1kp databases [1]&nbsp;of Table S2 which were assembled de novo by rnaSPAdes [2,3] and searched for moroidin peptide cyclases.</p> <ol> <li>Yan, Z., Carpenter, E.J., Wickett, N.J., Mirarab, S., Nguyen, N., Warnow, T., Ayyampalayam, S., Barker, M. and Burleigh, J.G., 2014. Data access for the 1,000 Plants (1KP) project.&nbsp;<em>Gigascience</em>,&nbsp;<em>3</em>(1), pp.2047-217X.</li> <li>Bankevich, A., Nurk, S., Antipov, D., Gurevich, A.A., Dvorkin, M., Kulikov, A.S., Lesin, V.M., Nikolenko, S.I., Pham, S., Prjibelski, A.D. and Pyshkin, A.V., 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.&nbsp;<em>Journal of computational biology</em>,&nbsp;<em>19</em>(5), pp.455-477.</li> <li>Bushmanova, E., Antipov, D., Lapidus, A. and Prjibelski, A.D., 2019. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data.&nbsp;<em>GigaScience</em>,&nbsp;<em>8</em>(9), p.giz100.</li> </ol> <p>&nbsp;</p>

opencc-by-4.0Dec 2021View details →
zenodo40/100

BIOPEP-UWM: Calculations for top 23 selected peptides from ATHpin ranking of Cynara cardunculus swine blood hydrolysate FNF

<p>Calculations by BIOPEP (https://biochemia.uwm.edu.pl/biopep-uwm/) for top 23 selected peptides from ATHpin ranking of Cynara cardunculus swine blood hydrolysate FNF.</p>

opencc-by-4.0Jan 2022View details →
zenodo40/100

A Multiplexed Cell-Free Assay to Screen for Antimicrobial Peptides in Double Emulsion Droplets

<p>Data underlying the figures in the publication &ldquo;A Multiplexed Cell-Free Assay to Screen for Antimicrobial Peptides in Double Emulsion Droplets&rdquo;, published in <em>Angew. </em><em>Chem. Int. Ed.,</em> <strong>2022</strong>, e202114632.</p> <p><a href="https://onlinelibrary.wiley.com/doi/10.1002/anie.202114632">https://onlinelibrary.wiley.com/doi/10.1002/anie.202114632</a></p> <p>&nbsp;</p> <p>Table of contents:</p> <p><strong>1. Figure 1b</strong>: Bright-field image of the double emulsions droplets produced on the microfluidic chip (scale bar 40 &mu;m).</p> <p><strong>2. Figure 1c</strong>: Source video of the image in <em>Figure 1c</em>. Overlaid fluorescence and bright-field image of a double emulsion in a hydrodynamic trap, containing LUVs loaded with a self-quenching concentration of SRB in the cell-free extract, showing background fluorescence (scale bar 20 &mu;m).</p> <p><strong>3. Figure 2a</strong>: Excel file containing the experimental data for <em>Figure 2a</em>. Cell-free protein production. Cell-free production of sfGFP in double emulsion (DE) droplets. The expression and folding of sfGFP was confirmed by the increase of fluorescence at 516 nm (ex. 488 nm). The dashed ribbon represents standard deviation (n=150).</p> <p><strong>4. Figure 2c</strong>: Excel files containing the experimental data for <em>Figure 2c</em>. Mean fluorescence intensities of b) after incubation at room temperature for 16 hours. no DNA: DEs without any alpha-hemolys in plasmid DNA(n=107), &alpha;-HL:DEs with the alpha-hemolys in plasmid DNA(n=258), SDS: double emulsions without any alpha-hemolys in plasmid DNA, exposed to a solution of 0.5% SDS in buffer throughout the incubation (n=204).</p> <p><strong>5. Figure 2d</strong>: Excel file containing the experimental data for <em>Figure 2d</em>. Fluorophore leakage kinetics from mammalian-like LUVs with SRB and from bacteria-like LUVs with 6-FAM, induced by the cell-free expression of pneumolysin in a 384 well-plate, starting at time 0. Fractional fluorescence (fF) is calculated by setting the zero level to the vesicle fluorescence in the absence of DNA, and the maximum level of fluorescence, scaled to a value of 1, to the value obtained by lysing the vesicles with 0.5% SDS. Solid lines represent the average of three independent reactions visible below.</p> <p><strong>6. Figures 2e and 2f</strong>: FACS data for <em>Figures 2e</em> and <em>2f</em>.</p> <p><strong>7. Figure 3a</strong>: Excel file containing the experimental data for <em>Figure 3a</em>. &nbsp;&nbsp;Fluorophore leakage kinetics from mammalian-like LUVs with SRB and bacteria-like LUVs with 6-FAM, induced by the cell-free expression of meucin-25 in a 384 well-plate. Each well contained 8 nM of plasmid (Supporting Information Table 1). Solid lines represent the average of three technical replicates displayed as well (the lines are overlapping, thus not visible).</p> <p><strong>8. Figure 3c</strong>: Excel file containing the experimental data for <em>Figure 3c</em>. &nbsp;&nbsp;Bacterial viability assay with increasing meucin-25 concentrations, measured by flow cytometry. Propidium iodide (PI) cannot pass intact bacterial membranes and only intercalates the DNA of permeabilized dead bacteria (&ldquo;PI positive&rdquo;). Constitutively expressed sfGFP proteins normally efficiently retained in intact bacterial cells (&ldquo;GFPpositive&rdquo;) but lost in suitably permeabilized cells. Error bars indicate standard deviation (n=10000).</p> <p><strong>9. Figure SI_2</strong>: Excel files containing the experimental data for <em>Supplementary Figure 2</em>.</p> <p><strong>10. Figure SI_3</strong>: Excel file containing the experimental data for <em>Supplementary Figure 3</em>.</p> <p><strong>11. Figure SI_4a</strong>: Excel files containing the experimental data for <em>Supplementary Figure 4a</em>.</p> <p><strong>12. Figure SI_4b</strong>: Excel files containing the experimental data for <em>Supplementary Figure 4b</em>.</p> <p><strong>13. Figure SI_5</strong>: Excel files containing the experimental data for <em>Supplementary Figure 5</em>.</p> <p><strong>14. Figure SI_6</strong>: Excel files containing the experimental data for <em>Supplementary Figure 6</em>.</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Feb 2022View details →
zenodo40/100

Peptide Mass Fingerprint Library of Monoclonal Murine anti-SARS-CoV-2 Antibodies

<p>The dataset contains fingerprints of monoclonal antibodies that can be used to identify the antibodies&nbsp;by using the open-source software ABID 2.0&nbsp;<a href="https://bam.de/ABID">https://bam.de/ABID</a><br> More information can be found in the publication where this data was used to rapidly distinguish between 35 monoclonal murine Anti-SARS-CoV-2 antibodies:&nbsp;<a href="https://doi.org/10.3390/antib11020027">https://doi.org/10.3390/antib11020027&nbsp;</a></p>

opencc-by-4.0Apr 2022View details →
zenodo40/100

Gold Nanoparticles Synthesized in the Presence of Peptides - UV-Vis Spectra, Fluorescence, USAXS, Electron Microscopy

<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.&nbsp;</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> <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 &ndash; 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 &ndash; RMRMKMK; Z2M6I: RMRMKIK; Z2M246I: RIRIKIK; AG3: AYSSGAPPMPPF.</li> </ul> <p>Each sample&rsquo;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/2022_02_13_AuPeptide_Kinetics</strong></p> <p><strong>Measurement_Data.xlsx</strong> and <strong>Measurement_Times.xlsx </strong>contain UV-Vis spectra at several time points for each well measured, and the time corresponding to each time step, respectively. See <strong>/Notebooks/UV_Vis_Kinetics.ipynb</strong> for data plotting and sample concentration information.</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>. We recommend installing this environment by using:</p> <p>conda env create -f /environment.yml</p> <p>Refer to&nbsp; <a href="https://github.com/SasView/sasmodels">https://github.com/SasView/sasmodels</a> and the first cell of <strong>/Notebooks/USAXS.ipynb</strong> for specific instructions on how to complete installation of the sasmodels module (sasmodels will be installed by Pip if you correctly use the shared environment.yml file).</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>&nbsp;</p>

opencc-by-4.0May 2022View details →
zenodo40/100

Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies

<p>Supporting datasets for preprint version of &quot;Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies&quot;.</p>

opencc-by-4.0Jul 2022View details →
zenodo40/100

Merging Flow Synthesis and Enzymatic Maturation to Expand the Chemical Space of Lasso Peptides

<p>LC-MS, UHPLC, and LC-IM-MS data of the corresponding publication</p>

opencc-by-4.0May 2024View details →
zenodo40/100

The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures

<p><span>The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial Peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance.</span></p> <p><span>Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution <em>in vitro</em> to that of single sequence AMPs, using the ESKAPE pathogen <em>Pseudomonas aeruginosa</em> (<em>P. aeruginosa</em>) as a model Gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to</span><span> form biofilms. </span><em><span>P. aeruginosa</span></em><span> was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied, and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined.</span></p> <p><span>Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved <em>in vitro</em>, providing an alternative to classic antibiotics worth pursuing.</span></p>

opencc-by-4.0May 2024View details →

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