scholarly journals Putative Antimicrobial Peptides of the Posterior Salivary Glands from the Cephalopod Octopus vulgaris Revealed by Exploring a Composite Protein Database

Antibiotics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 757 ◽  
Author(s):  
Daniela Almeida ◽  
Dany Domínguez-Pérez ◽  
Ana Matos ◽  
Guillermin Agüero-Chapin ◽  
Hugo Osório ◽  
...  
Keyword(s):  
Antimicrobial Peptides ◽  
Salivary Glands ◽  
Protein Identification ◽  
Inflammatory Responses ◽  
Shotgun Proteomics ◽  
Octopus Vulgaris ◽  
Protein Database ◽  
Venom Protein ◽  
Protein Toxin ◽  
Proteomics Approach

Cephalopods, successful predators, can use a mixture of substances to subdue their prey, becoming interesting sources of bioactive compounds. In addition to neurotoxins and enzymes, the presence of antimicrobial compounds has been reported. Recently, the transcriptome and the whole proteome of the Octopus vulgaris salivary apparatus were released, but the role of some compounds—e.g., histones, antimicrobial peptides (AMPs), and toxins—remains unclear. Herein, we profiled the proteome of the posterior salivary glands (PSGs) of O. vulgaris using two sample preparation protocols combined with a shotgun-proteomics approach. Protein identification was performed against a composite database comprising data from the UniProtKB, all transcriptomes available from the cephalopods’ PSGs, and a comprehensive non-redundant AMPs database. Out of the 10,075 proteins clustered in 1868 protein groups, 90 clusters corresponded to venom protein toxin families. Additionally, we detected putative AMPs clustered with histones previously found as abundant proteins in the saliva of O. vulgaris. Some of these histones, such as H2A and H2B, are involved in systemic inflammatory responses and their antimicrobial effects have been demonstrated. These results not only confirm the production of enzymes and toxins by the O. vulgaris PSGs but also suggest their involvement in the first line of defense against microbes.

scholarly journals A comprehensive and scalable database search system for metaproteomics

2016 ◽  
Author(s):  
Sandip Chatterjee ◽  
Gregory S. Stupp ◽  
Sung Kyu (Robin) Park ◽  
Jean-Christophe Ducom ◽  
John R. Yates ◽  
...  
Keyword(s):  
Search Engine ◽  
Protein Identification ◽  
High Throughput Sequencing ◽  
Shotgun Proteomics ◽  
Identification Accuracy ◽  
Sequencing Data ◽  
Protein Database ◽  
Healthy Human ◽  
Genomic Libraries ◽  
Sequence Databases

AbstractBackgroundMass spectrometry-based shotgun proteomics experiments rely on accurate matching of experimental spectra against a database of protein sequences. Existing computational analysis methods are limited in the size of their sequence databases, which severely restricts the proteomic sequencing depth and functional analysis of highly complex samples. The growing amount of public high-throughput sequencing data will only exacerbate this problem. We designed a broadly applicable metaproteomic analysis method (ComPIL) that addresses protein database size limitations.ResultsOur approach to overcome this significant limitation in metaproteomics was to design a scalable set of sequence databases assembled for optimal library querying speeds. ComPIL was integrated with a modified version of the search engine ProLuCID (termed “Blazmass”) to permit rapid matching of experimental spectra. Proof-of-principle analysis of human HEK293 lysate with a ComPIL database derived from high-quality genomic libraries was able to detect nearly all of the same peptides as a search with a human database (~500x fewer peptides in the database), with a small reduction in sensitivity. We were also able to detect proteins from the adenovirus used to immortalize these cells. We applied our method to a set of healthy human gut microbiome proteomic samples and showed a substantial increase in the number of identified peptides and proteins compared to previous metaproteomic analyses, while retaining a high degree of protein identification accuracy, and allowing for a more in-depth characterization of the functional landscape of the samples.ConclusionsThe combination of ComPIL with Blazmass allows proteomic searches to be performed with database sizes much larger than previously possible. These large database searches can be applied to complex meta-samples with unknown composition or proteomic samples where unexpected proteins may be identified. The protein database, proteomics search engine, and the proteomic data files for the 5 microbiome samples characterized and discussed herein are open source and available for use and additional analysis.

scholarly journals Data Employed in the Construction of a Composite Protein Database for Proteogenomic Analyses of Cephalopods Salivary Apparatus

Data ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 110
Author(s):  
Daniela Almeida ◽  
Dany Domínguez-Pérez ◽  
Ana Matos ◽  
Guillermin Agüero-Chapin ◽  
Yuselis Castaño ◽  
...  
Keyword(s):  
Salivary Glands ◽  
De Novo Assembly ◽  
De Novo ◽  
Original Data ◽  
Data Repository ◽  
Feeding Strategies ◽  
Octopus Vulgaris ◽  
Protein Database ◽  
Database Construction ◽  
Proteome Discoverer

Here we provide all datasets and details applied in the construction of a composite protein database required for the proteogenomic analyses of the article “Putative Antimicrobial Peptides of the Posterior Salivary Glands from the Cephalopod Octopus vulgaris Revealed by Exploring a Composite Protein Database”. All data, subdivided into six datasets, are deposited at the Mendeley Data repository as follows. Dataset_1 provides our composite database “All_Databases_5950827_sequences.fasta” derived from six smaller databases composed of (i) protein sequences retrieved from public databases related to cephalopods’ salivary glands, (ii) proteins identified with Proteome Discoverer software using our original data obtained by shotgun proteomic analyses of posterior salivary glands (PSGs) from three Octopus vulgaris specimens (provided as Dataset_2) and (iii) a non-redundant antimicrobial peptide (AMP) database. Dataset_3 includes the transcripts obtained by de novo assembly of 16 transcriptomes from cephalopods’ PSGs using CLC Genomics Workbench. Dataset_4 provides the proteins predicted by the TransDecoder tool from the de novo assembly of 16 transcriptomes of cephalopods’ PSGs. Further details about database construction, as well as the scripts and command lines used to construct them, are deposited within Dataset_5 and Dataset_6. The data provided in this article will assist in unravelling the role of cephalopods’ PSGs in feeding strategies, toxins and AMP production.

scholarly journals MODERN COMPUTATIONAL STRATEGIES FOR PROTEIN INFERENCE IN SHOTGUN PROTEOMIC

2021 ◽  
Vol 2 (336) ◽  
pp. 56-65
Author(s):  
Y. S. Golenko ◽  
A. A. Ismailova
Keyword(s):  
Protein Identification ◽  
Separation Efficiency ◽  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Optimization Methods ◽  
Tandem Mass ◽  
Experimental Sample ◽  
Label Free ◽  
Protein Database ◽  
Tandem Mass Spectra

Today, shotgun proteomics is a powerful approach to characterize proteomes in biological samples. Unlike the top-down proteomics strategy, shotgun proteomics is characterized by high separation efficiency and mass spectral sensitivity. At the same time, it places higher demands on the computational and statistical methods required for peptide identification, protein identification, and label-free quantification. The main purpose of shotgun proteomics is to identify the shape and amount of each protein by combining liquid chromatography with tandem mass spectrometry. The analysis and interpretation of experimental data is the final and most important stage in proteomics; they also generate a large number of problems that require complex computational solutions. One of the most important tasks, of course, is the identification of proteins present in the experimental sample. As a rule, this task is divided into two main components: the stage of assigning experimental tandem mass spectra to peptides obtained from the protein database, and the stage of comparing peptides with proteins and quantitative assessment of the reliability of the identified proteins. It is also worth considering that the assessment of the reliability of the data obtained can be a separate, no less important and complex task. In this article, we propose to consider protein identification only as a problem of statistical inference, and also describe a number of methods that can be used to solve it. We classify the existing approaches into (1) rule-based methods, (2) combinatorial optimization methods, and (3) probabilistic inference methods. Integer programming and Bayesian inference frameworks are used to represent methods. We also discuss the main problems of protein identification and suggest possible solutions to these problems.

Mass Spectrometry-Based Identification of Urinary Antimicrobial Peptides in Dairy Cows

2020 ◽  
Vol 27 (3) ◽  
pp. 225-235
Author(s):  
Ambika Sharma ◽  
Rajesh Nigam ◽  
Ashish Kumar ◽  
Simmi Singh
Keyword(s):  
Mass Spectrometry ◽  
Antimicrobial Peptides ◽  
Polyacrylamide Gel Electrophoresis ◽  
Radial Diffusion ◽  
Protein Database ◽  
Antimicrobial Effects ◽  
Cow Urine ◽  
Radial Diffusion Assay ◽  
Promising Source ◽  
Diffusion Assay

Background:: Urine is considered one of the biological fluids in which antimicrobial peptides are secreted or expressed. Cow urine has not been investigated for the presence of these peptides using MALDI-TOF-MS. Objective:: The aim of this study is to isolate, identify and assess the antimicrobial activity of urinary antimicrobial peptides from healthy normal cycling cows. Method:: We analyzed the urine sample using diafiltration, ion exchange chromatography, Reverse Phase High-Performance Liquid Chromatography (RP-HPLC), acid urea polyacrylamide gel electrophoresis (AU-PAGE) coupled with identification through Peptide Mass Fingerprinting (PMF) by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDITOF- MS). The in vitro antimicrobial effects of purified fractions were assessed using Radial Diffusion Assay (RDA) and microtitre broth dilution assay against Gram-positive and Gramnegative bacteria. Results: : Proteins corresponding to the peaks were identified using SWISSPROT protein database. This study revealed constitutive expression of β-Defensin-1 (DEFB1), β-Defensin-4A (DFB4A), Neutrophil Defensin-1 (DEF1), Neutrophil Defensin-3 (DEF3) in cow urine. The identified peptides are cationic antimicrobial peptides of the defensin family. The purified fractions exhibited antimicrobial effects in radial diffusion assay and MIC values in the range of 2.93-29.3 &*#181;M/L. Conclusion:: This study concludes that cow urine, previously unexplored with regard to antimicrobial peptides, would be a promising source of highly potent AMPs and an effective alternative to the resistant antibiotics.

scholarly journals Shotgun Proteomics Analysis of Saliva and Salivary Gland Tissue from the Common Octopus Octopus vulgaris

2018 ◽  
Vol 17 (11) ◽  
pp. 3866-3876 ◽  
Author(s):  
Legana C. H. W. Fingerhut ◽  
Jan M. Strugnell ◽  
Pierre Faou ◽  
Álvaro Roura Labiaga ◽  
Jia Zhang ◽  
...  
Keyword(s):  
Salivary Gland ◽  
Shotgun Proteomics ◽  
Octopus Vulgaris ◽  
Proteomics Analysis ◽  
Salivary Gland Tissue ◽  
Gland Tissue ◽  
The Common ◽  
Common Octopus

scholarly journals TOXIFY: a deep learning approach to classify animal venom proteins

2019 ◽  
Author(s):  
T Jeffrey Cole ◽  
Michael S Brewer
Keyword(s):  
Protein Sequence ◽  
Software Package ◽  
Shotgun Proteomics ◽  
Learning Approach ◽  
The Novel ◽  
Venom Protein ◽  
Order Of Magnitude ◽  
Gated Recurrent Units ◽  
Venom Proteins ◽  
Generation Sequencing

In the era of Next-Generation Sequencing and shotgun proteomics, the sequences of animal toxigenic proteins are being generated at rates exceeding the pace of traditional means for empirical toxicity verification. To facilitate the automation of toxin identification from protein sequences, we trained Recurrent Neural Networks with Gated Recurrent Units on publicly available datasets. The resulting models are available via the novel software package TOXIFY, allowing users to infer the probability of a given protein sequence being a venom protein. TOXIFY is more than 20X faster and uses over an order of magnitude less memory than previously published methods. Additionally, TOXIFY is more accurate, precise, and sensitive at classifying venom proteins. Availability: https://www.github.com/tijeco/toxify

scholarly journals Mass Spectrometry Based-Proteomic Analysis of Anisakis spp.: A Preliminary Study towards a New Diagnostic Tool

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 693 ◽  
Author(s):  
Valeria Marzano ◽  
Stefania Pane ◽  
Gianluca Foglietta ◽  
Stefano Levi Mortera ◽  
Pamela Vernocchi ◽  
...  
Keyword(s):  
Diagnostic Tool ◽  
Protein Extraction ◽  
Shotgun Proteomics ◽  
Diagnostic Tools ◽  
Routine Practice ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Proteomics Approach ◽  
Anisakis Spp ◽  
Tof Ms

Anisakiasis is nowadays a well-known infection, mainly caused by the accidental ingestion of Anisakis larvae, following the consumption of raw or undercooked fishes and cephalopods. Due to the similarity of symptoms with those of common gastrointestinal disorders, this infection is often underestimated, and the need for new specific diagnostic tools is becoming crucial. Given the remarkable impact that MALDI–TOF MS biotyping had in the last decade in clinical routine practice for the recognition of bacterial and fungi strains, a similar scenario could be foreseen for the identification of parasites, such as nematodes. In this work, a MALDI–TOF MS profiling of Anisakis proteome was pursued with a view to constructing a first spectral library for the diagnosis of Anisakis infections. At the same time, a shotgun proteomics approach by LC–ESI–MS/MS was performed on the two main fractions obtained from protein extraction, to evaluate the protein species enriched by the protocol. A set of MALDI–TOF MS signals associated with proteins originating in the ribosomal fraction of the nematode extract was selected as a potential diagnostic tool for the identification of Anisakis spp.

Optimizing Shotgun Proteomics Analysis for a Confident Protein Identification and Quantitation in Orphan Plant Species: The Case of Holm Oak (Quercus ilex)

2020 ◽  
pp. 157-168
Author(s):  
Isabel Gómez-Gálvez ◽  
Rosa Sánchez-Lucas ◽  
Bonoso San-Eufrasio ◽  
Luis Enrique Rodríguez de Francisco ◽  
Ana M. Maldonado-Alconada ◽  
...  
Keyword(s):  
Plant Species ◽  
Protein Identification ◽  
Quercus Ilex ◽  
Shotgun Proteomics ◽  
Holm Oak ◽  
Proteomics Analysis ◽  
Protein Identification And Quantitation
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