Mapping Taste-Relevant Food Peptidomes by Means of Sequential Window Acquisition of All Theoretical Fragment Ion–Mass Spectrometry

Abstract

During the last few years, key taste-active compounds have been isolated and identified by means of a combination of a time-and lab-consuming successive fractionation and sensory characterization. Because the peptidome of fermented, protein-rich food is very complex, new strategies are necessary to accelerate the identification of taste-active peptides. In this study, two advanced mass spectrometric approaches were developed to comprehensively map the bitter tasting peptidome of fermented foods by data-independent acquisition (DIA) using sequential window acquisition of all theoretical fragment ion−mass spectrometry (SWATH−MS) and an in silico-assisted triple quadrupole (QQQ)-based targeted proteomics approach, separately. Application of both techniques on two fresh cheese samples as well as on crude medium-pressure liquid chromatography fractions exhibiting intense bitter taste, followed by filtering the hydrophobic target peptides (Q value of ≥1200 cal/mol) showing a signal-to-noise ratio of ≥10 and a fold change of ≥3 when comparing the less bitter to the more bitter cheese sample, revealed the candidate bitter peptides, which were then validated by means of synthetic reference peptides and human sensory evaluation. The bitter peptides were then quantitated in the fresh cheese samples as well as in a series of dairy products by means of QQQ−MS and SWATH−MS, separately. Although the QQQ−MS method showed 2−80-fold lower limits of quantitation (LOQ), the SWATH−MS method could be shown for the first time to enable the comprehensive quantitation of all sensorially relevant key bitter peptides with LOQs far below the bitter taste recognition concentration of each peptide.

Application of the UHPLC-DIA-HRMS Method for Determination of Cheese Peptides

“…In this study, we have also found several peptides, that have been previously reported to show bioactivity [6,22]: VPITPT (αs2-CN f117-122), MPFPKYPVEPF (β-CN f109-119), EPVLGPVRGPFP (β-CN f195-206), DKIHPF (β-CN f47-52), YPFPGPIPN (β-CN f60-68), TPVVVPPFLQPE (β-CN f80-91), VPGEIVE (β-CN f8-14), VPSERYL (αs1-CN f86-92), VLGPVRGPFP (β-CN f197-206) and YPFPGPI (β-CN f60-66).…”

“…In this study, we have also found several peptides, that have been previously reported to show bioactivity [6,22] Across all samples analysed with either DIA-or DDA-based approaches, peptides from αs1-CN and β-CN comprised the majority of all detected peptides in the top 150 most abundant peptides.…”

Isolation, identification and sensory evaluation of kokumi peptides from by‐products of enzyme‐modified butter

“…The Q value rule was proposed to represent the average hydrophobicity of peptides, so as to predict the taste characteristics through the Q value 25 . This method is still popular for predicting the bitterness of peptides 49,50 . The Q value of the synthesized taste‐active peptides is calculated as shown in Table 3.…”

“…25 This method is still popular for predicting the bitterness of peptides. 49,50 The Q value of the synthesized tasteactive peptides is calculated as shown in Table 3. The Q values of all six peptides were less than 1400 cal mol −1 , which means that there may be no bitter taste in the six target peptides.…”

An aqueous extract of the brown alga Eisenia bicyclis extends lifespan in a sex-specific manner by interfering with the Tor-FoxO axis

“…The untargeted data acquisition was performed using the TripleToF 6600 mass spectrometer (Sciex, Darmstadt, Germany) coupled to a Exion LC system (Sciex) operating in the ESI negative (ESI-) and ESI positive (ESI+) ionization mode as described earlier [ 72 , 73 ]. Algae samples, QC and blank samples were analyzed using the data-independent acquisition (DIA) mode SWATH, by initially performing a ToF-MS scan with an accumulation time of 200 ms (m/z 100 - 1200), followed by 16 fixed Q1 isolation windows with an accumulation time of 81 ms in the m/z range from 100 to 1200 with 1 Da overlap.…”