Is the structural diversity of tripeptides sufficient for developing functional food additives with satisfactory multiple bioactivities?

Wang, Jianhui; Liu, Yong-Le; Ning, Jingheng; Yu, Jiangnan; Li, Xianghong; Wang, Faxiang

doi:10.1016/j.molstruc.2013.03.004

Cited by 18 publications

(7 citation statements)

References 90 publications

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“…It has been shown that several potent ACE inhibitory or antioxidant short peptides (2 to 3 amino acid residues) were also bitter. 23,40,48,82 In contrast, in another study, 24 no direct correlation was seen between ACE inhibitory properties and bitterness of di-or tripeptides. The number of possible dipeptide combinations (20 2 ¼ 400) is lower than that of larger peptides, increasing the structural diversity of larger peptides.…”

mentioning

confidence: 79%

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

Nongonierma

Fitzgerald

2016

RSC Adv.

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mentioning

confidence: 79%

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

Nongonierma

Fitzgerald

2016

RSC Adv.

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“…When compared to dipeptides, tripeptides were reported to exhibit higher levels of antioxidant activity [ 14 ]. Besides, tripeptides had much larger structural diversity than dipeptides, which is a good property for developing multifunctional food additives [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

Deng

Long

Tang

et al. 2019

IJMS

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Due to their beneficial effects on human health, antioxidant peptides have attracted much attention from researchers. However, the structure-activity relationships of antioxidant peptides have not been fully understood. In this paper, quantitative structure-activity relationships (QSAR) models were built on two datasets, i.e., the ferric thiocyanate (FTC) dataset and ferric-reducing antioxidant power (FRAP) dataset, containing 214 and 172 unique antioxidant tripeptides, respectively. Sixteen amino acid descriptors were used and model population analysis (MPA) was then applied to improve the QSAR models for better prediction performance. The results showed that, by applying MPA, the cross-validated coefficient of determination (Q2) was increased from 0.6170 to 0.7471 for the FTC dataset and from 0.4878 to 0.6088 for the FRAP dataset, respectively. These findings indicate that the integration of different amino acid descriptors provide additional information for model building and MPA can efficiently extract the information for better prediction performance.

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“…The QlAIP set was recently complied by Wang et al.,30 which was a collection of antihypertensive peptides in varied sequence lengths, consisting of 23 dipeptides, 23 tripeptides, 13 tetrappetides, 12 pentapeptides, 15 hexapeptides and 32 longer peptides. The antihypertensive potency of these peptides was measured in vivo by the decrease (mm Hg) in systolic blood pressure (SBP) of hypertensive rats (SHRs) administered with corresponding peptides.…”

Section: Methodsmentioning

confidence: 99%

Integrating Computational Modeling and Experimental Assay to Discover New Potent ACE‐Inhibitory Peptides

Ren

Wang

Chen

et al. 2013

Molecular Informatics

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Human angiotensin-I-converting enzyme (ACE) is an important target of antihypertensive therapy, which possesses a bulky, hydrophobic pocket that is physicochemically compatible with a wide variety of peptide substrates and small-molecule ligands. Rational design of potent ACE inhibitors has long been an attractive topic in the chemical, biological and medical communities. In the present study, an integrative protocol is described to optimize and modify peptides bound with ACE based on their complex three-dimensional structures. The protocol combines a number of sophisticated computational methods including molecular dynamics (MD) simulation, quantitative structure-activity relationship (QSAR), molecular mechanics/Poisson-Boltzmann surface area (MM-PB/SA) and quantum mechanics/molecular mechanics (QM/MM) to discover new potent ACE-inhibitory peptides. With this strategy hundreds of potential peptides are generated virtually, from which several promising candidates are synthesized and assayed in vitro using a standard Fmoc -protected amino acid solid phase synthesis and spectrophotometric method, respectively. Six peptides are found to have potency against ACE, four of which (LVY, VLKP, MLPVY and LKIPLY) show satisfactory inhibitory capability (pIC50 =5.84, 5.27, 5.40 and 5.57, respectively). Subsequently, the complex structures of tripeptide LVY and hexapeptide LKIPLY with ACE are computationally modeled and their binding free energies are estimated as - 34.2 and - 57.8 kcal/mol, respectively, by using a rigorous QM/MM scheme. Intensive steric collisions are observed around the C-terminus of peptide ligands in the bound state. Based upon the findings the C-terminal residues of the two peptides are modified to small, hydrophobic amino acids Val and Ala, resulting in mutated peptides LVA and LKIPVA, respectively. Consequently, a significant improvement in ACE-inhibitory activity is observed for the LKIPVA mutant (pIC50 increases from 5.57 to 6.07), whereas only a modest activity change was associated with the LVA mutant (pIC50 decreases from 5.84 to 5.80).

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Is the structural diversity of tripeptides sufficient for developing functional food additives with satisfactory multiple bioactivities?

Cited by 18 publications

References 90 publications

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

Integrating Computational Modeling and Experimental Assay to Discover New Potent ACE‐Inhibitory Peptides

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