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Novel anti‐cariogenic phosvitin‐phosphopeptides produced by hydrostatic pressure combined with enzymatic hydrolysis

2016· article· en· W4389024197 on OpenAlexaffabout
Heejoo Yoo

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChemistryPapainPhosvitinThermolysinEnzymatic hydrolysisHydrostatic pressureHydrolysisTrypsinChromatographyProteasesEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Purpose Phosvitin (PV) is known as a metal binding protein in egg yolk with unique amino acid composition (>55% serine) associated with a large proportion of phosphates. Many attempts to use PV as a potential source of anti‐microbial agent were made but it turns out be no feasible due to its insolubility and poor stability in aqueous media. Our preliminary data show the enzyme‐treated PV phosphopeptides (PV‐P) could be dissolved. On the other hand, the yield of PV‐P was extremely low due to high serine‐phosphate resistant to enzymatic hydrolysis. This study was to increase the yield of PV‐P by using an innovative processing platform technology of high hydrostatic pressure combined with enzymatic hydrolysis (HHP‐EH). Further study was to evaluate the iron‐chelating capacity of PV‐P. The main objective of this research is to produce PV‐P with iron binding capacity using HHP‐EH technology and study the possible correlation between iron binding capacity and antimicrobial activity of PV‐P against bacteria causing dental caries. Methods PV was isolated from egg yolk residues after IgY antibody extraction. PV‐P fractions (pH6.0) were produced by HHP‐EH with various proteases (Alcalase, Trypsin, Bromelain, Papain, Thermolysin, Elastase, Flavourzyme, Visozyme, and Savinase), in single, double or triple combinations, at E:S ratio of 1:50, under 100 MPa, at 37–50ºC for 12–24 h. The optimization of HHP‐EH was evaluated by TNBS method for degree of hydrolysis (DH) of PV. M w distribution of PV‐P was monitored by SDS‐PAGE, MALDI‐TOF and HPLC techniques. Iron‐chelating capacity of PV‐P fractions (> or <3 kDa) vs PV was measured by spectrophotometric detection of ferrous ions. Selected PV hydrolysates with highest iron chelating capacity were used in antimicrobial assays. Streptococcus sobrinus was grown in appropriate media at 37 ºC. PV, PV‐P and fractions were added to the cell suspension at different concentrations. A 100 μL portion was spread over Mitis‐Salivarius agar (Difco) plates. The number of colonies formed after incubation at 35 ºC for 24 h was measured to calculate the survival ratio. Samples were analyzed in triplicate and results were plotted in student t‐test. Results Triple combination of Alclase, Elastase and Flavourzyme showed the highest DH (89%) at E:S ratio of 1:50 in each enzyme, under 100 MPa, at 37ºC for 24 h. The results of SDS‐PAGE and MALDI‐TOF, HPLC showed that PV M w bands (48 and 37 kDa) were hydrolyzed into PV‐P at 30, 23, 17 kDa and <3 kDa. The highest iron‐chelating capacity was observed in PV‐P fraction (<3 kDa, 30%), compared to PV‐P fraction (>3 kDa, 13.5%), regardless of enzymatic and pressure treatment, indicating an efficient iron‐chelating capacity of <3 kDa PV‐P fraction. The antibacterial effect of PV‐P (<3 kDa fraction) on S. sobrinus strain showed a growth inhibition effect. Conclusion We optimized parameters of HHP‐EH processing to increase the yield of PV‐P. These short phosphopeptides (< 3 kDa) show high iron‐chelating capacity used in anti‐microbial agent. Support or Funding Information MITACS Graduate Internship Program and Canada Food Innovators Grant

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.205
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2016
Admission routes2
Has abstractyes

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