Identification of umami peptides based on virtual screening and molecular docking from Atlantic cod (<i>Gadus morhua</i>)

Wang, Yuanyuan; Luan, Junjia; Tang, Xuhua; Zhu, Wenhui; Xu, Yongxia; Bu, Ying; Lǐ, Jiànróng; Cui, Fangchao; Li, xuepeng

doi:10.1039/d2fo03776a

Cited by 37 publications

(20 citation statements)

References 45 publications

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“…To further verify the effectiveness and robustness of the model, we collected 91 wet-experiment verified umami peptide sequences reported in the latest literature [ 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ]. These empirical umami peptide sequences constituted the dataset UMP-VERIFIED, which was then used to test state-of-the-art methods, including UMPred-FRL [ 11 ] and iUP-BERT [ 12 ] for comparison to iUmami-DRLF.…”

Section: Resultsmentioning

confidence: 99%

“…The following supporting information can be downloaded at: , Figures S1–S3: iUmami-DRLF web server interface; Table S1: Results of 10-fold cross-validation and independent testing about SMOTE; Table S2: Prediction-probability results of the three models and 91 wet-experiment validated umami peptide sequences [ 5 , 6 , 11 , 56 , 59 , 60 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ].…”

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confidence: 99%

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A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features

Jiang

et al. 2023

Foods

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Umami peptides enhance the umami taste of food and have good food processing properties, nutritional value, and numerous potential applications. Wet testing for the identification of umami peptides is a time-consuming and expensive process. Here, we report the iUmami-DRLF that uses a logistic regression (LR) method solely based on the deep learning pre-trained neural network feature extraction method, unified representation (UniRep based on multiplicative LSTM), for feature extraction from the peptide sequences. The findings demonstrate that deep learning representation learning significantly enhanced the capability of models in identifying umami peptides and predictive precision solely based on peptide sequence information. The newly validated taste sequences were also used to test the iUmami-DRLF and other predictors, and the result indicates that the iUmami-DRLF has better robustness and accuracy and remains valid at higher probability thresholds. The iUmami-DRLF method can aid further studies on enhancing the umami flavor of food for satisfying the need for an umami-flavored diet.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features

Jiang

et al. 2023

Foods

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“…Toxicity and water solubility of peptides are important issues in peptide development because they are critical for their applicability in future functional food and drug development. 33 In this study, Innovagen and ADMETlab 2.0 servers were used to predict the water solubility and toxicity of the identified peptides. The predicted results showed that none of these peptides were toxic and 12 of them had good water solubility (Table 2).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Therefore, it is essential to find a safer alternative to MSG. Umami peptides are naturally present in a variety of foods and have great nutritional value, which conforms to the development trend of “natural, nutritious, and safe” food . Therefore, umami peptides have become a research hotspot in the field of food umami in recent years .…”

Section: Introductionmentioning

confidence: 89%

Identification of Novel Umami Peptides in Termitornyces albuminosus (Berk) Heim Soup by In Silico Analyses Combined with Sensory Evaluation: Discovering Potential Mechanism of Umami Taste Formation with Molecular Perspective

Yu,

Zhao,

et al. 2023

J. Agric. Food Chem.

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In this study, 24 peptides were identified in Termitornyces albuminosus (Berk) Heim soup, 12 of which were predicted to possess an umami taste based on the BIOPEP-UWM or Umami-MRNN databases. Among these 12 peptides, four peptides (i.e., QNDF, QGGDF, EPVTLT, and EVNYDFGGK) exhibited the lowest affinity energy with the umami receptor type 1 member 1 (T1R1) subunit. Molecular docking and molecular dynamics simulation further confirmed the strong binding of these four umami peptides to the umami receptor T1R1/T1R3, with the EVNYDFGGK forming the most stable complex. After synthesizing the four peptides, their umami taste was validated through sensory and electronic tongue analyses with recognition thresholds ranging from 0.0938 to 0.3750 mmol/L. Notably, the EVNYDFGGK peptide displayed the strongest umami taste (recognition threshold, 0.0938 mmol/L). This study may contribute to the industrial development of T. albuminosus by providing a new understanding of the mechanism of its umami formation.

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“…The active residues mainly included His145, Ser146, Ser147, Glu148, Gly168, Ala169, Ser170, Met171, Val187, Try218, Ser276, Val277, Glu301, Ala302, Try303, Leu304, Gln356, and His387. Several key residues have been reported to exist in VFTD, including Glu301, Ala302, Ser146, His145, and His387, where umami peptides from Trachinotus ovatus, Chouguiyu, and Atlantic cod (Gadus morhua) were derived. These amino residues are all depicted in Figure G.…”

Section: Resultsmentioning

confidence: 99%

Identification and Verification of Novel Umami Peptides Isolated from Hybrid Sturgeon Meat (Acipenser baerii × Acipenser schrenckii)

Wang,

Han,

Zhao

et al. 2023

J. Agric. Food Chem.

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To explore the umami mechanism in sturgeon meat, five peptides (ERRY, VRGPR, LKYPLE, VKKVFK, and YVVFKD) were isolated and identified by ultrafiltration, gel filtration chromatography, and UPLC-QTOF-MS/MS. The omission test confirmed that the five umami peptides contributed to the umami taste of sturgeon meat. Also, the peptides had the double effective role of enhancing both umami and saltiness. The threshold of ERRY was only 0.031, which exceeded most umami peptides in the last 3 years. Molecular docking results showed that five peptides could easily bind to Gly167, Ser170, and Try218 residues in T1R3 through hydrogen bonds and electrostatic interactions. Furthermore, molecular dynamics simulations indicated that hydrogen bonds and hydrophobic interactions were the main intermolecular interaction forces. This study could contribute to revealing the umami taste mechanism of sturgeon meat and provide new insights for effective screening of short umami peptides.

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Identification of umami peptides based on virtual screening and molecular docking from Atlantic cod (Gadus morhua)

Abstract: Umami peptides have currently become the research focus in the food umami science field and the key direction for umami agent development. This is because umami peptides have good processing...

Cited by 37 publications

References 45 publications

A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features

A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features

Identification of Novel Umami Peptides in Termitornyces albuminosus (Berk) Heim Soup by In Silico Analyses Combined with Sensory Evaluation: Discovering Potential Mechanism of Umami Taste Formation with Molecular Perspective

Identification and Verification of Novel Umami Peptides Isolated from Hybrid Sturgeon Meat (Acipenser baerii × Acipenser schrenckii)

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