Statistical modeling of in vitro pepsin specificity

Suwareh, Ousmane; Causeur, David; Jardin, Julien; Briard‐Bion, Valérie; Feunteun, Steven Le; Pezennec, Stéphane; Nau, Françoise

doi:10.1016/j.foodchem.2021.130098

Cited by 18 publications

(11 citation statements)

References 39 publications

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“…These data were used for the prediction of the peptide release in β-LG with demasked peptide bonds [5]. The efforts are currently ongoing to analyze the secondary specificity of pepsin and to model the peptide release kinetics [6,7]. For trypsin, mostly primary substrate specificity was taken into consideration for the proteolysis modeling [2,8], although the preferred and undesirable amino acid residues at various positions have also been established and considered [9].…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Peptide Release during Proteolysis of β-Lactoglobulin by Trypsin with Consideration of Peptide Bond Demasking

Vorob’ev

2023

IJMS

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Prospects for predicting the fragmentation of polypeptide chains during their enzymatic hydrolysis using proteolysis models are considered. The opening of the protein substrate during proteolysis and the exposure of its internal peptide bonds for a successful enzymatic attack, the so-called demasking process, were taken into account. The two-step proteolysis model was used, including the parameters of demasking and the rate constants of hydrolysis of enzyme-specific peptide bonds. Herein, we have presented an algorithm for calculating the concentrations of intermediate and final peptide fragments depending on the time of hydrolysis or the degree of hydrolysis. The intermediate peptide fragments with two or one internal specific peptide bond were considered. The fragmentation of β-lactoglobulin (β-LG) by trypsin was predicted, and the calculated concentration curves for peptide fragments were compared with the experimental dependences of the concentrations on the degree of hydrolysis. Numerical parameters were proposed that characterize the concentration curves for intermediate and final peptide fragments, and they were used to compare the calculated and experimental dependences. The predicted distribution of the peptide fragments corresponded to the experimental data on the peptide release during the proteolysis of β-LG by trypsin.

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Section: Introductionmentioning

confidence: 99%

Modeling of the Peptide Release during Proteolysis of β-Lactoglobulin by Trypsin with Consideration of Peptide Bond Demasking

Vorob’ev

2023

IJMS

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“…These data were used for the prediction of the peptide release in -LG with demasked peptide bonds [5]. Efforts are currently ongoing to analyze the secondary specificity of pepsin and model the peptide release kinetics [ 6,7]. For trypsin, mostly primary substrate specificity was taken into consideration for the proteolysis modelling [1,8], although preferred and undesirable amino acid residues at various positions have also been established and taken into account [9].…”

Section: Introductionmentioning

confidence: 99%

“…The characteristic time of hydrolysis t0 for the most rapidly hydrolyzed bond (j=8) was 2.14 min 5. Selectivity for j=69 6. Selectivity for j=101.…”

mentioning

confidence: 99%

Modeling of the Peptide Release During Proteolysis of B-Lactoglobulin by Trypsin

Vorob’ev¹

2023

Preprint

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Prospects for predicting the fragmentation of polypeptide chains during their enzymatic hydrolysis using proteolysis models are considered. The opening of the protein substrate during proteolysis and the exposure of its internal peptide bonds for a successful enzymatic attack, the so-called demasking process, were taken into account. The two-step model of proteolysis was used, including the parameters of peptide bond demasking and hydrolysis rate constants for various peptide bonds. Herein, we presented an algorithm for calculating the concentrations of peptide fragments depending on the hydrolysis time or the degree of hydrolysis. The intermediate peptide fragments with two or one internal specific bonds were considered. The fragmentation of β-lactoglobulin (b-LG) with trypsin was predicted, and the calculated concentration curves for peptide fragments were compared with the experimental dependences of concentrations on the degree of hydrolysis. Numerical parameters characterizing the kinetic curves were proposed for the intermediate and final peptide fragments, and they were used to compare the calculated and experimental dependences. The predicted distribution of peptide fragments corresponded to the experimental data on the peptide release during proteolysis of b-LG by trypsin.

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“…8 Molecular docking results suggested that the C-terminal of LDLQR was combined with ASP443 and ASP542 residues of α-glucosidase, which could be the essential reason for its α-glucosidase inhibition activity. 8 Since Leu is one of the main target hydrolysis sites of pepsin, 29 LDLQR would be broken down in the stomach and the inhibitory activity will be weakened, which would challenge its efficacy in vivo. Herein, NU-1000 was synthesized and applied to encapsulate LDLQR for controlled release in the small intestine.…”

Section: Introductionmentioning

confidence: 99%

Acid-Resistant Mesoporous Metal–Organic Frameworks as Carriers for Targeted Hypoglycemic Peptide Delivery: Peptide Encapsulation, Release, and Bioactivity

Liu

et al. 2022

ACS Appl. Mater. Interfaces

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Oral administration of bioactive peptides with αglucosidase inhibitory activities is a promising strategy for diabetes mellitus. The wheat germ peptide Leu−Asp−Leu−Gln−Arg (LDLQR) has been previously proven to inhibit the activity of α-glucosidase efficiently. However, it is still difficult to transport the peptide to the intestine completely due to the harsh condition of the stomach. Herein, an acid-resistant zirconium-based metal− organic framework, NU-1000, was used to immobilize LDLQR with a high encapsulation capacity (92.72%) and encapsulation efficiency (44.08%) in only 10 min. The in vitro release results showed that the acid-stable NU-1000 not only effectively protected LDLQR from degradation in the presence of stomach acid and pepsin effectively but also ensured the release of encapsulated LDLQR under simulated intestinal conditions. Furthermore, LDLQR@NU-1000 could slow down the elevated blood sugar caused by maltose in mice and the area under blood sugar curve decreased by almost 20% when compared with the control group. The inflammatory factor (IL-1β, IL-6) in vivo and cell growth in vitro were almost the same between NU-1000 treatment and normal control groups. This study indicates NU-1000 is a promising vehicle for targeted peptide-based bioactive delivery to the small intestine.

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Statistical modeling of in vitro pepsin specificity

Cited by 18 publications

References 39 publications

Modeling of the Peptide Release during Proteolysis of β-Lactoglobulin by Trypsin with Consideration of Peptide Bond Demasking

Modeling of the Peptide Release during Proteolysis of β-Lactoglobulin by Trypsin with Consideration of Peptide Bond Demasking

Modeling of the Peptide Release During Proteolysis of B-Lactoglobulin by Trypsin

Acid-Resistant Mesoporous Metal–Organic Frameworks as Carriers for Targeted Hypoglycemic Peptide Delivery: Peptide Encapsulation, Release, and Bioactivity

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