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Ray Yue"],"technology_type":["Mass Spectrometry","Bottom-up proteomics","Data-independent acquisition"],"software":[""],"submitter_keywords":["Legumes","Food allergy","Allpret","Allergenicity assessment","Ige","Proteomics","Data independent acquisition","Mass spectrometry","Allergen extracts"],"full_dataset_link":["https://www.ebi.ac.uk/pride/archive/projects/PXD079632"],"tissue":["Plant Cell"],"sample_protocol":["All reagents for MS sample preparation and analyses were of MS grade. Dried legume seeds were frozen using liquid nitrogen and grinded into powder, before being suspended in 100 mM ammonium carbonate to obtain protein extracts. Legume protein extracts were then centrifuged at 16,000 – 20,000 g for 30 – 40 min at 4 – 10 °C. The resulting supernatants were filtered through 5 µm, 1.2 µm and 0.2 µm Minisart® NML Cellulose Acetate (hydrophilic) (Sartorius AG, Germany) or Polyethersulfone (PES) (Carl Roth, Germany) filters. Aliquoted (1 mL or 5 mL) extracts were stored at -80 °C. The extract aliquots were freeze-dried overnight in a lyophilizer (Alpha 2-4 LSCplus, Martin Christ Gefriertrocknungsanlagen, Germany) and stored at -20 °C until use.   Lyophilised legume extracts were resuspended at 5 mg/mL in PBS (137 mM NaCl, 3 mM KCl, 8 mM Na2HPO4, 1 mM KH2PO4, pH 7.2). In triplicate, legume extracts (10 µg of protein in 2 µL of 5 mg/mL extracts) were diluted in 16 µL 50 mM Tris/HCl pH 8.8, before being reduced with 2 µL TCEP and alkylated with 2 µL of CAA. Samples were incubated at RT for 20 min, heated in a dry heating block at 95 °C for 5 min, and sonicated in a BioRupter Pico sonicating water bath (Diagenode, Denville, NJ, US) on a 60-second on / 30-second off mode for 5 cycles at 8°C. Samples were digested with 40 µL of digestion buffer (10% acetonitrile [ACN] in 50 mM HEPES pH 8.5) and 200 ng of LysC (Fujifilm WAKO Pure Chemical Corporation, Osaka, Japan), and incubated at 750 rpm at 37 °C for 4 h. Digestion buffer of 140 µL and 100 ng Trypsin (Thermo ScientificTM) were then added and incubated at 750 rpm at 37 °C overnight. To clean samples, SOLAμ™ SPE-HRP 2 mg/1 mL 96-well plate (Thermo Scientific™) columns according to manufacturer instructions. Supernatants transferred into clean 1.5 mL LoBind tubes (Eppendorf SE) and dried in a Speedvac at 60 °C for 90 min. Dried samples were resuspended in 12 μL of Buffer A* (2% ACN, 1% TFA), vortexed, sonicated in a VWR Ultrasonic Cleaner USC300T (VWR International) sonicating water bath for 4 min and centrifuged at 18,000g for 10 min at RT. Peptide concentration was measured by loading 1 μL onto a DS-11 FX+ Series Nanodrop Spectrophotometer (DeNovix, Wilmington, DE, US) and analysed at A280 to obtain the peptide concentration. Samples were stored at -20°C until LC-MS/MS injection.  In triplicate injections, peptides (500 ng peptide per sample) were loaded onto an Acclaim™ PepMap™ 20 mm C18 trap column (Thermo Scientific™), connected in-line to a 15 cm C18 reverse-phase EasySpray ES904 analytical column (Thermo Scientific™) using 100% of Buffer A (0.1% FA) at 750 bar, using the EASY-nLCTM 1200 LC system (Thermo ScientificTM), and the column oven operating at 30 °C. Peptides were eluted over a 70 min gradient ranging from 10% to 60% of Buffer B (80% ACN, 0.1% FA) at 250 nL/min, and the Orbitrap ExplorisTM 480 mass spectrometer (Thermo ScientificTM) was run in data-independent acquisition (DIA) mode with field asymmetric ion mobility spectrometry (FAIMS) ProTM Interface (Thermo ScientificTM) with compensation voltage of -45 V. Full MS spectra were collected at a resolution of 120,000, with an automated gain control (AGC) target of 300% or maximum injection time set to ‘auto’ and a scan range of 400–1000 m/z. The MS2 spectra were obtained in DIA mode in the Orbitrap operating at a resolution of 60,000, with an AGC target 1000% or maximum injection time set to ‘auto’, a normalised HCD collision energy of 32. The isolation window was set to 6 m/z with a 1 m/z overlap and window placement on. Each DIA experiment covered a range of 200 m/z, resulting in three DIA experiments (400-600 m/z, 600-800 m/z and 800-1000m/z). Between the DIA experiments, a full MS scan was performed for MS1 quantification."],"repository":["Pride"],"quantification_method":["Not available"],"modification":[""],"data_protocol":["Raw data were processed with Spectronaut® 20 (Biognosys), and the spectra were searched against the following taxonomies at the species level unless otherwise stated: peanut Arachis hypogaea (ID 3818: 102,020 entries), soybean Glycine max (ID 3847: 85,130 entries), pea Pisum sativum (ID 3888: 66,517 entries), chickpea Cicer arietinum (ID 3827: 31,239 entries), white bean Phaseolus vulgaris (ID 3885: 32,796 entries), sweet blue lupine Lupinus angustifolius (ID 3871: 31,389 entries), lentil Fabeae family (ID 163743: 107,132 entries), field bean Vicia faba (ID 3906: 36,660 entries), fenugreek Trifolieae tribe (ID 163742: 311,473 entries), alfalfa Medicago sativa (ID 3879: 94,640 entries), red clover Trifolium pratense (ID 57,577: 60,478 entries), and white clover Trifolium genus (ID 3899: 215,542 entries). The following parameters for protein identification searches were adopted: trypsin with at least 2 missed cleavages with 7 to 52 peptide length, charge states of +2, +3, and +4 accepted. Cysteine carbamidomethyl was set as a fixed modification, while oxidation (M), hydroxyproline, and acetyl on protein N-termini were set for variable modifications. All results were filtered at 1% false discovery rate, and protein quantitation was performed at the MS1 level. The data was normalised by retention time-dependent local regression model (Callister et al., 2006), and protein groups (PGs) were inferred by IDPicker. In this study, PGs are defined as one or more UniProt protein accessions that share identified peptide evidence and cannot be unambiguously distinguished by unique peptides, and therefore are grouped and quantified together.   Protein quantification pivot reports were exported from Spectronaut, and PGs were quantified using the intensity-based absolute quantification (iBAQ) approach. The average iBAQ value for each protein group was calculated as the sum of all identified values divided by the number of theoretical fully specific peptides without missed cleavages, as calculated inSpectronaut. Relative abundances were calculated by dividing each PG’s average iBAQ value by the summed iBAQ value. For additional data, refer to the main publication."],"omics_type":["Proteomics"],"labhead":["Katrine Lindholm Bøgh"],"instrument_platform":[""],"submission_type":["PARTIAL"],"labhead_affiliation":["Research Group for Food Allergy, National Food Institute, Technical University of Denmark"],"species":["Trifolium Pratense","Trigonella Foenum-graecum","Cicer Arietinum (chickpea) (garbanzo)","Phaseolus Vulgaris (kidney Bean) (french Bean)","Pisum Sativum (garden Pea)","Vicia Faba","Lupinus Angustifolius","Medicago Sativa (alfalfa)","Trifolium Repens","Lens Culinaris","Glycine Max","Arachis Hypogaea (peanut)"],"publication":["10.3389/FALGY.2026.1938923"],"submitter_mail":["sryu@food.dtu.dk"],"submitter_affiliation":["Technical University of Denmark"],"submitter_country":["Denmark"],"additional_accession":[]},"is_claimable":false,"name":"The ALLPreT panel of legume allergen preparations: Optimization of total protein extracts and characterization of allergen composition, IgE-binding- and functional cross-linking properties, for the development of an ‘Allergenicity Prediction Toolbox for novel foods’","description":"A protein-naming framework was established to determine the relative abundance of both allergens and non-allergenic proteins in 13 different legume seed extracts (roasted peanut, unroasted peanut, soybean, sweet blue lupine, winter pea, beluga lentils, chickpea, field bean, white kidney bean, fenugreek, alfalfa, red clover, and white clover). Lyophilized extracts were resuspended with PBS and enzymatically digested. Digested and cleaned legume extracts were analysed by data-independent acquisition LC-MS/MS, and processed in Spectronaut against the UniProt taxonomy for the respective legumes. A curated allergen database was initially created for legumes where information on allergens was available from publicly-available allergen databases, integrating existing allergen databases and 90% BLAST. Subsequently, a protein family naming framework to identify additional proteins belonging to allergenic protein families, enabling the relative abundance assessment of allergens and non-allergenic proteins for each 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