Identification of novel umami molecules <i>via</i> QSAR models and molecular docking

Xiu, Hongxia; Liu, Huan; Yang, Huihui; Ren, Haibin; Luo, Bin; Wang, Zhipeng; Shao, Hong; Wang, Fengzhong; Zhang, Jingjian; Wang, Yutang

doi:10.1039/d2fo00544a

Cited by 6 publications

(7 citation statements)

References 35 publications

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“…For example, Goel et al designed a framework comprising QSAR models and molecular docking for identifying possible sweeteners from natural molecules . Xiu et al developed an in silico pipeline to identify novel umami-tasting molecules in batches from SWEET-DB and BitterDB databases via principal component analysis, QSAR modeling, molecular docking, and electronic tongue analysis . They identified 18 novel umami molecules using the pipeline via an electronic tongue analysis …”

Section: Screening and Designing Of Flavor Molecules Based On Computa...mentioning

confidence: 99%

Data-Driven Elucidation of Flavor Chemistry

Kou

Shi

Gao

et al. 2023

J. Agric. Food Chem.

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Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.

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Section: Screening and Designing Of Flavor Molecules Based On Computa...mentioning

confidence: 99%

Data-Driven Elucidation of Flavor Chemistry

Kou

Shi

Gao

et al. 2023

J. Agric. Food Chem.

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“…37 However, hydrophobic amino acids do not fully determine the overall taste effect. Molecules with bitter and sweet taste characteristics may be umami molecules, 13 and therefore, the three peptides may still produce the umami taste.…”

Section: Virtual Screening Analysis Of Umami Peptidesmentioning

confidence: 99%

“…Currently, the validation of umami peptides is mainly based on sensory analysis through a series of complex separations and purifications, which has certain limitations, such as cost. 13,14 Alternatively, the use of computerized algorithms to predict peptide activity is becoming increasingly popular. 15 Efficient and rapid virtual screening is often used to assist in the screening of fresh flavor peptides.…”

Section: Introductionmentioning

confidence: 99%

“…Such studies can help us understand the types of umami peptides and their umami effects in soybean paste to improve the quality of soybean paste products and increase their market size. Currently, the validation of umami peptides is mainly based on sensory analysis through a series of complex separations and purifications, which has certain limitations, such as cost. , Alternatively, the use of computerized algorithms to predict peptide activity is becoming increasingly popular . Efficient and rapid virtual screening is often used to assist in the screening of fresh flavor peptides. , For instance, Gu et al screened six umami peptide candidates (CN, SM, CRD, GESMTDGF, MS, and DGC) from porcine type I collagen using virtual screening and other methods .…”

Section: Introductionmentioning

confidence: 99%

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Identification, Characterization, and Receptor Binding Mechanism of New Umami Peptides from Traditional Fermented Soybean Paste (Dajiang)

Cao,

An,

et al. 2023

J. Agric. Food Chem.

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Dajiang, a traditional Chinese condiment, is made from fermented soybeans. It is highly popular among consumers as a result of its delicious umami flavor, which mainly originates from umami peptides. To examine the mechanism of umami taste in Dajiang, we selected Dajiang samples with strong umami taste and subjected them to purification and identification analysis using ethanol precipitation, gel chromatography, reversed-phase high-performance liquid chromatography, and ultraperformance liquid chromatography–tandem mass spectrometry. Subsequently, on the basis of toxicity and umami prediction analysis, we screened, synthesized, and characterized three novel bean umami peptides in Dajiang: TLGGPTTL, 758.4174 Da; GALEQILQ, 870.4811 Da; and HSISDLQ, 911.4713 Da. Their sensory threshold values were 0.25, 0.40, and 0.17 mmol/L, respectively. Furthermore, molecular docking results showed that hydrogen-bonding and hydrophobic interactions are important interaction forces in the binding of umami peptide to taste receptors. Ser147 and Glu148 of the T1R3 taste receptor are important amino acid residues for binding of the three umami peptides. This study uncovers the mechanism of umami-peptide-driven flavor in fermented soybean products.

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“…13 Quantitative structure-activity relationship (QSAR) refers to the study of the quantitative variation pattern between the structural characteristics of a class of compounds and their biological effects by mathematical and statistical methods, and the establishment of reasonable mathematical models, which are widely used in activity prediction, screening and design of new compounds. [14][15][16] Among these, 3D-QSAR can not only examine the effects of different conformations or conformations of receptor-ligand interactions, but also includes richer physicochemical connotations. 17 The most widely used methods in 3D-QSAR are comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA).…”

Section: Introductionmentioning

confidence: 99%