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99 results for “Antimicrobial peptides”

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zenodo48/100

Machine learning designs non-hemolytic antimicrobial peptides

The upload contains additional primary data associated with the publication, including raw data in the original file format whenever possible. Data content: HRMS, HPLC-MS, CD, MD, TEM

opencc-by-4.0Jul 2021View details →

zenodo44/100

AMPSphere : the worldwide survey of prokaryotic antimicrobial peptides

AMPSphere v.2022-03: the worldwide survey of prokaryotic antimicrobial peptides   INTRODUCTION AMPSphere is a comprehensive catalog of antimicrobial peptides predicted using Macrel (DOI: <a href="https://peerj.com/articles/10555/">10.7717/peerj.10555</a>) from 63,410 public metagenomes, <a href="http://progenomes.embl.de/">ProGenomes v2.2 database</a> (82,400 high-quality microbial genomes), and c.a. 4k non-whitelisted microbial genomes from NCBI. Currently, AMPSphere is available as a web resource at <a href="https://ampsphere.big-data-biology.org/">https://ampsphere.big-data-biology.org/</a>.   GENERATION Peptides were predicted using <a href="https://www.big-data-biology.org/software/macrel/">Macrel</a>. Singleton peptides were removed, except those with a direct hit to <a href="http://dramp.cpu-bioinfor.org/">DRAMP database</a>. Redundant peptides were coded using a reduced alphabet and hierarchically clustered using CD-HIT (version 4.6) at 100%, 85%, and 75% of amino acid identity (and 90% of overlap of the shorter peptide). The obtained clusters were numbered by decreasing size (number of peptides). Each level of clustering was called a SPHERE. Redundant nucleotide sequences for the gene variants of different AMPs also were included in this version of AMPSphere.   STATISTICS AMPSphere v.2022-03 contains 863,498 sequences (avg length: 36 amino acids, range 8-98). DRAMP database was used to find confirmed sequences with strict homology to reference. This approach showed that 2,488 peptides were previously confirmed in our dataset.   IDENTIFIERS Peptides are named in the form >AMP10.XXX_XXX where XXX_XXX is a unique numerical identifier (starting at zero). Numbers were assigned in order of increasing number of copies. So that the lower the number, the greater number of copies of that peptide were present in the input data. Annotations were also provided as separated fields in the fasta file, containing their: - SPHERE families at level III (corresponding to hierarchically obtained clusters using 100-85-75% of identity with a minimum overlap of 90% of the shorter gene). Example of header: <pre><code class="language-bash">>AMP10.000_000 | SPHERE-III.001_493 </code></pre> VERSION DETAILS Version 2022-03 includes: - quality assessment of documented AMPs, - metadata associated with the genes, - a better taxonomic identification of AMP sources using GTDB. WARNING: Due to a different procedure of AMP sorting, now some entries and families may have changed their accessions.   FILES WITHIN THIS VERSION README.md This file.   AMPsphere_v.2022-03.fna.xz Multi-fasta with AMPSphere gene sequences (nucleotide).   AMPsphere_v.2022-03.faa.gz Multi-fasta with AMPSphere peptide sequences (amino acid).   SPHERE_v.2022-03.levels_assessment.tsv.gz TSV table relating AMP name and the hierarchically obtained clusters per level. Columns: - AMP accession - evaluation vs. representative - SPHERE_fam level I - SPHERE_fam level II - SPHERE_fam level III Levels of each SPHERE family: I: contains clusters obtained with 100% of identity cut-off and 90% of overlap of the shorter sequence; II: contains clusters obtained with the unclustered sequences and the representatives from level I at 85% of identity and 90% of overlap of the sorter sequence; III: contains clusters obtained with the unclustered sequences and the representatives from level II at 75% of identity and 90% of overlap of the sorter sequence; `evaluation vs. representatives` shows the percent of identity the sequence has in an alignment against the cluster representative, and also the overlap in percent. Example:     * -- This means: this sequence is a cluster representative. OR something like this:     77.50%,1:40:1:40 -- This means: alignment identity against the                         representative of the cluster equals 77.5% and the                         alignment start and end position for the query (1 and                         40, respectively), and target (1 and 40, respectively).   AMPSphere_v.2022-03.quality_assessment.tsv.gz TSV table containing the results of each quality test (by sequence). Columns: - AMP ID - Antifam - RNAcode - Metaproteomes - Metatranscriptomes - Coordinates Results are one of 'Passed', 'Failed', or 'Not tested'. <a href="https://www.ebi.ac.uk/research/bateman/software/antifam-tool-identify-spurious-proteins">Antifam </a>results show if the sequence matches ('Fail') or does not match ('Pass') to Antifams, a set of well-known spurious ORFs. <a href="https://github.com/ViennaRNA/RNAcode">RNAcode</a> relies on gene diversity, therefore, families with less than 3 different gene sequences could not be tested and were marked as such. The direct match of 50% of our peptide to transcripts (in at least 2 different samples) or peptides from meta-omics studies sampled from different environments assigned the peptide as passing the metatranscriptomes and metaproteomes tests, respectively. Finally, the coordinates test check if the start of the small ORF happens with at least one stop codon upstream, this ensures that the gene is not a fragment from a larger protein.   AMPSphere_v.2022-03.general_geneinfo.tsv.gz TSV table relating AMP, gene name, the microbial source, sample, environment, and geographical location. Columns: - gmsc (gene code access)     - amp     - sample (biosample) - source (microbial origin, GTDB taxonomy) - specI (species cluster according to ProGenomes v.2 classification) - is_metagenomic (False if comming from a high-quality microbial genome) - geographic_location - latitude - longitude - general_envo_name - environment_material   CONTACT You can contact us via our discussion group: <a href="https://groups.google.com/g/ampsphere-users">https://groups.google.com/g/ampsphere-users</a> AMPsphere main developers: - <a href="mailto:celio@big-data-biology.com?subject=AMPSphere%20v.2022-03&body=Dear%20Celio%2C%20%0A%0ARegarding%20AMPSphere%20v.2022-03.">Célio Dias Santos Júnior</a> - <a href="mailto:yiqian@big-data-biology.org?subject=AMPSphere%20v2022-03&body=Dear%20Yiqian%2C%20%0A%0ARegarding%20AMPSphere%20v2022-03.%0A">Yiqian Duan</a> - <a href="mailto:hui@big-data-biology.org?subject=AMPSphere%20v2022-03&body=Dear%20Hui%2C%20%0A%0ARegarding%20AMPSphere%20v.2022-03.">Hui Chong</a> - <a href="mailto:luispedro@big-data-biology.com?subject=AMPSphere%20v.2022-03&body=Dear%20Luis%2C%20%0A%0ARegarding%20AMPSphere%20v.2022-03.">Luis Pedro Coelho</a> COPYRIGHT NOTICE AMPSphere v.2022-03 - the worldwide survey of prokaryotic antimicrobial peptides. This work is a joint effort of Big Data Biology group from the Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI) - Fudan University, Shanghai, China, and the Structural and Computational Biology Unit (Heidelberg) - European Molecular Biology Laboratory (EMBL). Copyright (C) 2019-2022 The Authors    AMPSphere IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES    OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.    IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,    DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR    OTHERWISE,ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE    USE OR OTHER DEALINGS IN THE SOFTWARE.    This database is free; you can redistribute it and/or modify it    as you wish, under the terms of the CC BY 4.0 license.    You are allowed to:    Share — copy and redistribute the material in any medium or format    Adapt — remix, transform, and build upon the material for any purpose,            even commercially.    You may also obtain a copy of the CC BY 4.0 license here:        https://creativecommons.org/licenses/by/4.0/ REFERENCES CITED - Macrel: Santos-Júnior CD, Pan S, Zhao X, Coelho LP. 2020. Macrel: antimicrobial peptide screening in genomes and metagenomes. PeerJ 8:e10555. https://doi.org/10.7717/peerj.10555 - ProGenomes: Mende DR, Letunic I, Maistrenko OM et al. 2020. proGenomes2: an improved database for accurate and consistent habitat, taxonomic and functional annotations of prokaryotic genomes.  Nucleic Acids Research 48(D1): D621–D625. https://doi.org/10.1093/nar/gkz1002 - DRAMP: Kang X, Dong F, Shi C et al. 2019. DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci Data 6, 148. https://doi.org/10.1038/s41597-019-0154-y - ANTIFAM: Eberhardt RY, Haft DH, Punta M, Martin M, O’Donovan C, BatemanA. 2012. AntiFam: a tool to help identify spurious ORFs in protein annotation. Database, Bas003. - RNAcode: Washietl S, Findeiss S, Müller SA, Kalkhof S, von Bergen M, Hofacker IL, Stadler PF, Goldman N. 2011. RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data. RNA 17(4):578-94.

opencc-by-4.0May 2022View details →

zenodo40/100

Human antimicrobial peptide inactivation mechanism of enveloped viruses

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>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).Setup and conditions for the experiments are described in the experimental section of the published (open access) manuscript.

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

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

opencc-by-4.0Dec 2023View details →

zenodo40/100

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

Data underlying the figures in the publication “A Multiplexed Cell-Free Assay to Screen for Antimicrobial Peptides in Double Emulsion Droplets”, published in Angew. Chem. Int. Ed., 2022, e202114632. <a href="https://onlinelibrary.wiley.com/doi/10.1002/anie.202114632">https://onlinelibrary.wiley.com/doi/10.1002/anie.202114632</a>   Table of contents: 1. Figure 1b: Bright-field image of the double emulsions droplets produced on the microfluidic chip (scale bar 40 μm). 2. Figure 1c: Source video of the image in Figure 1c. 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 μm). 3. Figure 2a: Excel file containing the experimental data for Figure 2a. 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). 4. Figure 2c: Excel files containing the experimental data for Figure 2c. 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), α-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). 5. Figure 2d: Excel file containing the experimental data for Figure 2d. 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. 6. Figures 2e and 2f: FACS data for Figures 2e and 2f. 7. Figure 3a: Excel file containing the experimental data for Figure 3a.   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). 8. Figure 3c: Excel file containing the experimental data for Figure 3c.   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 (“PI positive”). Constitutively expressed sfGFP proteins normally efficiently retained in intact bacterial cells (“GFPpositive”) but lost in suitably permeabilized cells. Error bars indicate standard deviation (n=10000). 9. Figure SI_2: Excel files containing the experimental data for Supplementary Figure 2. 10. Figure SI_3: Excel file containing the experimental data for Supplementary Figure 3. 11. Figure SI_4a: Excel files containing the experimental data for Supplementary Figure 4a. 12. Figure SI_4b: Excel files containing the experimental data for Supplementary Figure 4b. 13. Figure SI_5: Excel files containing the experimental data for Supplementary Figure 5. 14. Figure SI_6: Excel files containing the experimental data for Supplementary Figure 6.

opencc-by-4.0Feb 2022View details →

zenodo40/100

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

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. Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution in vitro to that of single sequence AMPs, using the ESKAPE pathogen Pseudomonas aeruginosa (P. aeruginosa) 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 form biofilms. P. aeruginosa 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. 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 in vitro, providing an alternative to classic antibiotics worth pursuing.

opencc-by-4.0May 2024View details →

zenodo40/100

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

opencc-by-4.0Aug 2023View details →

zenodo40/100

pH-responsive aminolipid nanocarriers for antimicrobial peptide delivery

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.   Abstract: 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.

opencc-by-4.0Jun 2021View details →

dryad36/100

The lower airways microbiota and antimicrobial peptides indicate dysbiosis in sarcoidosis

Rationale: The role of the pulmonary microbiome in sarcoidosis is unknown. Objectives: The objectives of the current study was to: 1) 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. Methods: 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). Measurements and Main Results: Aspergillus 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 a-diversity and AMPs. Conclusions: The pulmonary fungal and bacterial microbiota in sarcoidosis differed from controls, with lower antimicrobial peptides levels in sarcoidosis.

opencc-zeroMar 2022View details →

dryad36/100

Genetic and transcriptomic datasets characterizing gene drive mosquitoes expressing antimicrobial peptides that retard Plasmodium sporogonic development

Gene drives hold promise for the genetic control of malaria vectors. The development of vector population modification strategies hinges on the availability of effector mechanisms impeding parasite development in transgenic mosquitoes. We augmented a midgut gene of the malaria mosquito Anopheles gambiae to secrete two exogenous antimicrobial peptides, Magainin 2 and Melittin. This small genetic modification, capable of efficient non-autonomous gene drive, hampers oocyst development in both Plasmodium falciparum and Plasmodium berghei. It delays the release of infectious sporozoites while it simultaneously reduces the lifespan of homozygous female transgenic mosquitoes. Modeling the spread of this modification using a large-scale agent-based model of malaria epidemiology reveals that it can break the cycle of disease transmission across a range of transmission intensities.

opencc-zeroAug 2022View details →

zenodo36/100

Fig. 1 in Antimicrobial activity of the crude peptide extracts from Blackfin sea catfish Arius jella Day, 1877

Fig. 1 — Photograph of experimental species Arius jella

opencc-by-4.0Aug 2023View details →

zenodo36/100

Data for HydrAMP - a deep generative model for antimicrobial peptide discovery

<ul> <li>data- training data for peptides < 25 AA (16.8 MB)</li> <li>models - checkpoints of HydrAMP, PepCVAE, and Basic models for every training epoch (466 MB)</li> <li>results - dumped generation results for every model. Required for running comparison notebooks (832 MB)</li> <li>wheels - custom TensorFlow packages (1 GB)</li> </ul>

openmit-licenseDec 2022View details →

zenodo36/100

Antimicrobial peptides modulate lung injury by altering the intestinal microbiota

This repository contains the OTU tables for "Antimicrobial peptides modulate pulmonary inflammation by altering the intestinal microbiota" by Abdelgawad and Nicola et al., an analysis of the role of antimicrobial peptide in the gut-lung axis during hyperoxia-induced lung injury. This work was supported by the National Heart, Lung, and Blood Institute of the U.S. National Institutes of Health, K NIH: K08 HL151907 (KW), K08 HL141652 (CL), K08 DK120871 (AO); the Kaul Pediatric Research Institute at Children’s of Alabama (KW), and the Microbiome Center at UAB (KW). The funding agencies had no role in the design, conduct, and analysis of the study or in the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. All data in this repository is the intellectual property of the authors and may be utilized for academic publication only with prior written permission.

opencc-by-4.0Dec 2022View details →

zenodo36/100

Amphiphilic Cyclic Antimicrobial Peptides Derived from the [W4R4] Scaffold: Structural Studies and Correlation with Activity

Data for Publication “Amphiphilic Cyclic Antimicrobial Peptides Derived from the [W4R4] Scaffold: Structural Studies and Correlation with Activity.” The file archive "WR_peptides_MD.zip" contains the starting structures of peptides, molecular topologies of peptides, representative MD trajectories of peptides in water and the DOPC/DOPG bilayer, as well as MD simulation parameters. See the README.txt file in this archive for details.

opencc-by-4.0Jul 2023View details →

ClinicalTrials.gov36/100

The Estrogen Impact on Overactive Bladder Syndrome: Female Pelvic Floor Microbiomes and Antimicrobial Peptides

ClinicalTrials.gov study NCT02524769. IPD Sharing: NO. Countries: 1. Publications: 17.

closedIPD-NOFeb 2026View details →

dryad36/100