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      Mapping Coeliac Toxic Motifs in the Prolamin Seed Storage Proteins of Barley, Rye, and Oats Using a Curated Sequence Database

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          Abstract

          Wheat gluten, and related prolamin proteins in rye, barley and oats cause the immune-mediated gluten intolerance syndrome, coeliac disease. Foods labelled as gluten-free which can be safely consumed by coeliac patients, must not contain gluten above a level of 20 mg/Kg. Current immunoassay methods for detection of gluten can give conflicting results and may underestimate levels of gluten in foods. Mass spectrometry methods have great potential as an orthogonal method, but require curated protein sequence databases to support method development. The GluPro database has been updated to include avenin-like sequences from bread wheat ( n = 685; GluPro v1.1) and genes from the sequenced wheat genome ( n = 699; GluPro v 1.2) and Triticum turgidum ssp durum ( n = 210; GluPro v 2.1). Companion databases have been developed for prolamin sequences from barley ( n = 64; GluPro v 3.0), rye ( n = 41; GluPro v 4.0), and oats ( n = 27; GluPro v 5.0) and combined to provide a complete cereal prolamin database, GluPro v 6.1 comprising 1,041 sequences. Analysis of the coeliac toxic motifs in the curated sequences showed that they were absent from the minor avenin-like proteins in bread and durum wheat and barley, unlike the related avenin proteins from oats. A comparison of prolamin proteins from the different cereal species also showed α- and γ-gliadins in bread and durum wheat, and the sulphur poor prolamins in all cereals had the highest density of coeliac toxic motifs. Analysis of ion-mobility mass spectrometry data for bread wheat (cvs Chinese Spring and Hereward) showed an increased number of identifications when using the GluPro v1.0, 1.1 and 1.2 databases compared to the limited number of verified sequences bread wheat sequences in reviewed UniProt. This family of databases will provide a basis for proteomic profiling of gluten proteins from all the gluten containing cereals and support identification of specific peptide markers for use in development of new methods for gluten quantitation based on coeliac toxic motifs found in all relevant cereal species.

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          Clustal Omega for making accurate alignments of many protein sequences.

          Clustal Omega is a widely used package for carrying out multiple sequence alignment. Here, we describe some recent additions to the package and benchmark some alternative ways of making alignments. These benchmarks are based on protein structure comparisons or predictions and include a recently described method based on secondary structure prediction. In general, Clustal Omega is fast enough to make very large alignments and the accuracy of protein alignments is high when compared to alternative packages. The package is freely available as executables or source code from www.clustal.org or can be run on-line from a variety of sites, especially the EBI www.ebi.ac.uk.
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            Target-decoy search strategy for mass spectrometry-based proteomics.

            Accurate and precise methods for estimating incorrect peptide and protein identifications are crucial for effective large-scale proteome analyses by tandem mass spectrometry. The target-decoy search strategy has emerged as a simple, effective tool for generating such estimations. This strategy is based on the premise that obvious, necessarily incorrect "decoy" sequences added to the search space will correspond with incorrect search results that might otherwise be deemed to be correct. With this knowledge, it is possible not only to estimate how many incorrect results are in a final data set but also to use decoy hits to guide the design of filtering criteria that sensitively partition a data set into correct and incorrect identifications.
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              Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures.

              A novel database search algorithm is presented for the qualitative identification of proteins over a wide dynamic range, both in simple and complex biological samples. The algorithm has been designed for the analysis of data originating from data independent acquisitions, whereby multiple precursor ions are fragmented simultaneously. Measurements used by the algorithm include retention time, ion intensities, charge state, and accurate masses on both precursor and product ions from LC-MS data. The search algorithm uses an iterative process whereby each iteration incrementally increases the selectivity, specificity, and sensitivity of the overall strategy. Increased specificity is obtained by utilizing a subset database search approach, whereby for each subsequent stage of the search, only those peptides from securely identified proteins are queried. Tentative peptide and protein identifications are ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, the algorithm utilizes decoy database techniques for automatically determining the false positive identification rates. The search algorithm has been tested by comparing the search results from a four-protein mixture, the same four-protein mixture spiked into a complex biological background, and a variety of other "system" type protein digest mixtures. The method was validated independently by data dependent methods, while concurrently relying on replication and selectivity. Comparisons were also performed with other commercially and publicly available peptide fragmentation search algorithms. The presented results demonstrate the ability to correctly identify peptides and proteins from data independent acquisition strategies with high sensitivity and specificity. They also illustrate a more comprehensive analysis of the samples studied; providing approximately 20% more protein identifications, compared to a more conventional data directed approach using the same identification criteria, with a concurrent increase in both sequence coverage and the number of modified peptides.
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                Author and article information

                Contributors
                Journal
                Front Nutr
                Front Nutr
                Front. Nutr.
                Frontiers in Nutrition
                Frontiers Media S.A.
                2296-861X
                17 July 2020
                2020
                : 7
                : 87
                Affiliations
                [1] 1Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, Manchester Institute of Biotechnology, University of Manchester , Manchester, United Kingdom
                [2] 2Centre for Crop Genetic Improvement, Rothamsted Research , Harpenden, United Kingdom
                [3] 3Waters Corporation , Wilmslow, United Kingdom
                Author notes

                Edited by: Katharina Anne Scherf, Karlsruhe Institute of Technology (KIT), Germany

                Reviewed by: Stefania Masci, University of Tuscia, Italy; Michelle Lisa Colgrave, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia; Vera Muccilli, University of Catania, Italy

                *Correspondence: E. N. Clare Mills clare.mills@ 123456manchester.ac.uk

                This article was submitted to Nutrition and Food Science Technology, a section of the journal Frontiers in Nutrition

                Article
                10.3389/fnut.2020.00087
                7379453
                f6d81b72-2c0e-4433-975b-c4647b248fd8
                Copyright © 2020 Daly, Bromilow, Nitride, Shewry, Gethings and Mills.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 May 2019
                : 12 May 2020
                Page count
                Figures: 4, Tables: 4, Equations: 0, References: 93, Pages: 17, Words: 12431
                Categories
                Nutrition
                Original Research

                gluten,sequence database,barley,rye,oats,coeliac disease,wheat
                gluten, sequence database, barley, rye, oats, coeliac disease, wheat

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