Proteomic characterization of pilot scale hot-water extracts from the industrial carrageenan red seaweed Eucheuma denticulatum

Gregersen, Simon; Pertseva, Margarita; Marcatili, Paolo; Jacobsen, Charlotte; García‐Moreno, Pedro J.; Hansen, Egon Bech; Overgaard, Michael Toft

doi:10.1016/j.algal.2021.102619

Cited by 11 publications

(10 citation statements)

References 18 publications

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“…Data was searched against a manually curated version of the full protein database for Solanum tuberosum (tax:4113) from UniProt (Consortium et al, 2021) where redundant fragments were removed, as previously described (García-Moreno, Gregersen, et al, 2020). Standard settings were applied, using a 5% false discovery rate (FDR) on both peptide and protein level (Gregersen et al, 2022), including reverse sequences for FDR control, and including common contaminants.…”

Section: Methodsmentioning

confidence: 99%

“…To determine the protein-level distribution of peptides in PPHs, the method of unspecific, length-normalized relative intensity, I L rel , was used (Gregersen et al, 2021, 2022). In short, the relative molar abundance of an identified protein (group) was estimated as: where I n is the intensity of protein n (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Although a higher FDR (5%) was applied for MaxQuant analysis, this level was previously shown to be suitable for non-specific digests due to the significantly increased combinatorial search space (Gregersen et al, 2022). In general, a lower number of peptides were identified in PPHs produced using Neut, although more than 10,000 peptides were identified in all PPHs (Table 4).…”

Section: 4peptide Identification and Mean Peptide Propertiesmentioning

confidence: 99%

“…Using our previously published method for relative protein abundance estimation using lengthnormalized, relative MS1 intensity (Gregersen et al, 2021(Gregersen et al, , 2022, it is possible to obtain insight on which proteins are abundantly represented in a hydrolysate and thereby also on protein-level enrichment and selectivity. By summing intensities for all peptides originating from the proteins, the potential peptide-level bias is alleviated to a large degree.…”

Section: 6protease and E/s Ratio Governs Differential Protein Family ...mentioning

confidence: 99%

“…This is the prerequire assumption used in the iBAQ approach for relative protein quantification (Schwanhüusser et al, 2011). A similar approach was recently developed for non-conventional bottom-up proteomics data, where a specific protease has not been applied in the sample preparation and downstream data analysis (Gregersen et al, 2021(Gregersen et al, , 2022, thereby making the approach suitable for the MS data obtained in this study. Based on relative abundance by IL rel , protein-level abundances were pooled into major protein families/classes found in potato (Fig.…”

Section: 6protease and E/s Ratio Governs Differential Protein Family ...mentioning

confidence: 99%

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Targeted hydrolysis of native potato protein: A novel route for obtaining hydrolysates with improved interfacial properties

Gregersen

Jafarpour

Yeşiltaş

et al. 2022

Preprint

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Peptides and protein hydrolysates are promising alternatives to substitute chemical additives as functional food ingredients. In this study, we present a novel approach for producing a potato protein hydrolysate with improved emulsifying and foaming properties by data-driven, targeted hydrolysis. Based on previous studies, we selected 15 emulsifier peptides derived from abundant potato proteins, which were clustered based on sequence identity. Through in silico analysis, we determined that from a range of industrial proteases (Neutrase (Neut), Alcalase (Alc), Flavorzyme (Flav) and Trypsin (Tryp)), Tryp was found more likely to release peptides resembling the target peptides. After applying all proteases individually, hydrolysates were assayed for in vitro emulsifying and foaming properties. No direct correlation between degree of hydrolysis and interfacial properties was found. Tryp produced a hydrolysate (DH=5.4%) with the highest (P<0.05) emulsifying and foaming abilities, good stabilities, and high aqueous solubility. Using LC-MS/MS, we identified >10,000 peptides in each hydrolysate. Through peptide mapping, we show that random overlapping with known peptide emulsifiers is not sufficient to quantitatively describe hydrolysate functionality. While Neut hydrolysates had the highest proportion of peptides with target overlap, they showed inferior interfacial activity. In contrast, Tryp was able to release specifically targeted peptides, explaining the high surface activity observed. While modest yields and residual unhydrolyzed protein indicate room for process improvement, this work shows that data-driven, targeted hydrolysis is a viable, interdisciplinary approach to facilitate hydrolysis design for production of functional hydrolysates from alternative protein sources.

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Section: Methodsmentioning

confidence: 99%