In-silico investigation of umami peptides with receptor T1R1/T1R3 for the discovering potential targets: A combined modeling approach

Wang, Wenli; Cui, Zhiyong; Ning, Menghua; Zhou, Tianxing; Liu, Yuan

doi:10.1016/j.biomaterials.2021.121338

Cited by 56 publications

(76 citation statements)

References 38 publications

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“…2e). Both of these observations are in accord with the current consensus that the hydrophobic residues dominate bitter peptides 41,59 , while negative residues dominate umami peptides 43,46,60,61 . We, however, also note the significant presence of hydrophilic residues in bitter peptides.…”

Section: A Physicochemical Properties Of the Database Peptidessupporting

confidence: 89%

“…Following the literature, we set a peptide with all hydrophobic residues as the baseline bitter peptide 10,41,59 and a peptide with all negatively charged residues as the baseline umami peptide 43,46,60,61 . While the compositions of longer umami peptides are known to be varied 43 , the importance of the presence of negatively charged acidic residues is generally well accepted in the community.…”

Section: F Baseline Patternsmentioning

confidence: 99%

“…Accordingly, defining the target variable for umami intensity proved to be difficult. To bypass this difficulty, the computational methods that have been generally used to predict and analyze umami peptides relied on structural analysis such as homology modeling and molecular docking [44][45][46][47][48] of possible umami peptides to umami taste receptors such as T1R1/T1R3 49 . Quite recently, physicochemical descriptors 50 and only sequence information 51 were used along with ML-based methods to classify umami and non-umami peptides.…”

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confidence: 99%

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Identifying sequential residue patterns in bitter and umami peptides

Dutta¹,

Bereau²,

Vilgis³

2022

Preprint

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The primary structures of peptides, originating from food proteins, affect their taste. Connecting primary structure to taste, however, is difficult because the size of the peptide sequence space increases exponentially with increasing peptide length, while experimentally-labeled data on peptides' tastes remain scarce. We propose a method that coarse-grains the sequence space to reduce its size and systematically identifies the most common coarse-grained residue patterns found in known bitter and umami peptides. We select the optimal patterns by performing extensive out-of-sample tests. The optimal patterns better represent the bitter and umami peptides when compared against baseline peptides, bitter peptides with all hydrophobic residues and umami peptides with all negatively charged residues, and peptides with randomly-chosen residues. Our method complements quantitative structure-activity relationship methods by offering generic, coarse-grained bitter and umami residue patterns that can aid in locating short bitter or umami segments in a protein and in designing new umami peptides. I. INTRODUCTIONSpecial compounds trigger specific tastes: sodium chloride (salty), sugars (sweet), acids, phenols, and alkaloids (bitter), glutamic acids and nucleotides (umami). Tastes are crucial because the gustatory system, the sensory system that helps in perceiving taste, often informs us about safe and harmful foods through their tastes 1 . Further, taste determines most of our food preferences 2 . For example, vegetables such as cabbage, cucumber, or spinach, often taste bitter since they contain plant alkaloidswhich can be toxic if consumed in large amounts and are known to have excessive bitter taste 2,3 -and, consequently, we avoid them.Bitter and umami represent two major taste modalities that peptides commonly have; the third one is sweet 4 . Interestingly, while salty, sour, sweet, and bitter were recognized as basic tastes early on, umami was recognized as the fifth basic taste only around the beginning of this century when umami taste receptors were identified [5][6][7] . As a result, the study of umami peptides is a more recent endeavor compared to, for example, the study of bitter peptides 8,9 .Bitter peptides are often found in fermented foods 10,11 and protein hydrolysates 3 , while umami peptides are found in savory foods such as parmesan cheese, fermented soy sauce, and seaweed 8 . As we tend to avoid bitter foods and seek savory ones, classifying foods based on the taste responses they evoke and modulate, and finding the physicochemical reasons causing those responses are indispensable steps in designing new nutritional and palatable foods. The growing number of curated databases, and analysis tools, of bitter-and umamia)

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Section: A Physicochemical Properties Of the Database Peptidessupporting

confidence: 89%

Section: F Baseline Patternsmentioning

confidence: 99%

mentioning

confidence: 99%

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Identifying sequential residue patterns in bitter and umami peptides

Dutta¹,

Bereau²,

Vilgis³

2022

Preprint

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“…Bioactive compounds from natural foods for the treatment of hypertension have received great attention due to their low toxicity, especially the ACE inhibitory peptides, and numerous studies have shown that peptides derived from different food sources such as pork, beef, bovine milk, egg, rice and rapeseed have an antihypertensive effect on the animal model. 16–18 Most ACE inhibitory peptides or hydrolysates reduce the blood pressure by reducing the activity of RAS in vivo , such as peptides from walnut protein, 19 corn germ, 20 and tilapia skin gelatin. 21 However, further investigations suggested that ACE inhibitory peptides reduced blood pressure by other mechanisms, such as the upregulation of angiotensin-converting enzyme 2 (ACE2), 22 the improvement of oxidative stress, 23 the upregulation of endothelial nitric oxide, 24 and the downregulation of AT1R.…”

Section: Introductionmentioning

confidence: 99%

The effects of angiotensin I-converting enzyme inhibitory peptide VGINYW and the hydrolysate of α-lactalbumin on blood pressure, oxidative stress and gut microbiota of spontaneously hypertensive rats

Xie

Shen

et al. 2022

Food Funct.

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“…Residues of D and E were also generally found in the sequence of umami peptides, dominating the dipeptides and tripeptides with umami taste. 31,32 Among the six types of proteins, myosin included the highest level of glutamic acid at 12.71% ± 0.47%, significantly (P < 0.01) more than other proteins, and parvalbumin was the most abundant in aspartic acid at 11.60 ± 1.62%…”

Section: Comparison Of Amino Acids In Proteinsmentioning

confidence: 99%

Fish proteins as potential precursors of taste‐active compounds: an in silico study

Xiao

et al. 2022

J Sci Food Agric

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BACKGROUND: Fish protein is a good source of amino acids and peptides with sensory properties. Theoretically, the type of protein affects the taste quality of the protein hydrolysates. To better use fish protein in the food ingredients industry, an in silico approach was adopted to evaluate the potential of fish protein to release taste-active compounds.RESULTS: Six types of protein from seven commercial fishes were screened from the Uniprot knowledge base. The results showed that a remarkable number of umami fragments presented in myosin and parvalbumin (PB), such as glutamic acid (Glu), aspartic acid (Asp), and Asp-and Glu-containing peptides, whereas sweet amino acids and bitter peptides (e.g., Proand Gly-containing peptides) were mainly found in collagen (CGI) in all fish samples. After the in silico proteolysis by papain, a difference in the profile of taste-active fragments was observed among the six types of proteins. Amino acids were the main hydrolysis products of these proteins, especially umami, sweet, and bitter amino acids, significantly contributing to the taste formation of protein hydrolysates. Besides, the myosin and CGI hydrolysates were abundant in taste active peptides both in types and quantities.CONCLUSION: Myosin is a promising protein source for producing umami fragments, and CGI seems to be a good precursor of sweet and bitter fragments. Different types of protein have an essential effect on the taste of protein hydrolysates.

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In-silico investigation of umami peptides with receptor T1R1/T1R3 for the discovering potential targets: A combined modeling approach

Cited by 56 publications

References 38 publications

Identifying sequential residue patterns in bitter and umami peptides

Identifying sequential residue patterns in bitter and umami peptides

The effects of angiotensin I-converting enzyme inhibitory peptide VGINYW and the hydrolysate of α-lactalbumin on blood pressure, oxidative stress and gut microbiota of spontaneously hypertensive rats

Fish proteins as potential precursors of taste‐active compounds: an in silico study

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