Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network

Bo, Weichen; Qin, Dongya; Zheng, Xin; Wang, Yue; Ding, Botian; Li, Yinghong; Liang, Guizhao

doi:10.1016/j.foodres.2022.110974

Cited by 24 publications

(20 citation statements)

References 41 publications

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“…The top 2 indicators (MinEStateIndex and VSA_Estate6) were related to the van der Waals surface (Figure C,D). This was consistent with the index proposed by Bo in exploring the bitter/sweet structure–activity relationship . In the importance of the D–E amino acid matrix for umami prediction, D_E_Count was the most important indicator, followed by E_frist_index, E_count, D_first_idnex, D_E_true, and D_count.…”

Section: Resultssupporting

confidence: 83%

“…This was consistent with the index proposed by Bo in exploring the bitter/sweet structure−activity relationship. 9 In the importance of the D−E amino acid matrix for umami prediction, D_E_Count was the most important indicator, followed by E_frist_index, E_count, D_first_idnex, D_E_true, and D_count. In the bitter taste, E_frist_index was the most important, followed by D_E_Count, E_count, D_first_index, D_count, and D_E_true.…”

Section: Structural Analysis Of Key Molecular Fingerprints and Descri...mentioning

confidence: 88%

“…Molecular descriptors are used to quantitatively describe the physical and chemical information of molecules. For example, an artificial neural network multilayer perceptron applying molecular fingerprints as input data 9 and an umami prediction model applying the molecular descriptors as input data 10 were constructed. To provide a good overview, the information on the published taste prediction models over the past decade was summarized (Tables S1).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning

Cui

Zhang

Zhou

et al. 2023

J. Agric. Food Chem.

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Taste peptides, as an important component of protein-rich foodstuffs, potentiate the nutrition and taste of food. Thereinto, umami-and bitter-taste peptides have been ex tensively reported, while their taste mechanisms remain unclear. Meanwhile, the identification of taste peptides is still a time-consuming and costly task. In this study, 489 peptides with umami/ bitter taste from TPDB (http://tastepeptides-meta.com/) were collected and used to train the classification models based on docking analysis, molecular descriptors (MDs), and molecular fingerprints (FPs). A consensus model, taste peptide docking machine (TPDM), was generated based on five learning algorithms (linear regression, random forest, gaussian naive bayes, gradient boosting tree, and stochastic gradient descent) and four molecular representation schemes. Model interpretive analysis showed that MDs (VSA_EState, MinEstateIndex, MolLogP) and FPs (598, 322, 952) had the greatest impact on the umami/bitter prediction of peptides. Based on the consensus docking results, we obtained the key recognition modes of umami/bitter receptors (T1Rs/T2Rs):(1) residues 107S-109S, 148S-154T, 247F-249A mainly form hydrogen bonding contacts and (2) residues 153A-158L, 163L, 181Q,

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Section: Resultssupporting

confidence: 83%

Section: Structural Analysis Of Key Molecular Fingerprints and Descri...mentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning

Cui

Zhang

Zhou

et al. 2023

J. Agric. Food Chem.

View full text Add to dashboard Cite

show abstract

“…At this point, the performance of the GNN declined. Nonetheless, the accuracy of flavor multiclassification is a challenging task, and our accuracy rate of 80% represented a significant improvement. In addition, the large number of peptides in the molecular set will also affect the accuracy of the models, and the taste classification of peptides and small molecules should be separated in the future.…”

Section: Resultsmentioning

confidence: 99%

Building a Kokumi Database and Machine Learning-Based Prediction: A Systematic Computational Study on Kokumi Analysis

He,

Liu,

et al. 2024

J. Chem. Inf. Model.

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Kokumi is a subtle sensation characterized by a sense of fullness, continuity, and thickness. Traditional methods of taste discovery and analysis, including those of kokumi, have been labor-intensive and costly, thus necessitating the emergence of computational methods as critical strategies in molecular taste analysis and prediction. In this study, we undertook a comprehensive analysis, prediction, and screening of the kokumi compounds. We categorized 285 kokumi compounds from a previously unreleased kokumi database into five groups based on their molecular characteristics. Moreover, we predicted kokumi/ non-kokumi and multi-flavor compositions using six structure− taste relationship models: MLP-E3FP, MLP-PLIF, MLP-RDKFP, SVM-RDKFP, RF-RDKFP, and WeaveGNN feature of Atoms and Bonds. These six predictors exhibited diverse performance levels across two different models. For kokumi/non-kokumi prediction, the WeaveGNN model showed an exceptional predictive AUC value (0.94), outperforming the other models (0.87, 0.90, 0.89, 0.92, and 0.78). For multi-flavor prediction, the MLP-E3FP model demonstrated a higher predictive AUC and MCC value (0.94 and 0.74) than the others (0.73 and 0.33; 0.92 and 0.70; 0.95 and 0.73; 0.94 and 0.64; and 0.88 and 0.69). This data highlights the model's proficiency in accurately predicting kokumi molecules. As a result, we sourced kokumi active compounds through a highthroughput screening of over 100 million molecules, further refined by toxicity and similarity screening. Lastly, we launched a web platform, KokumiPD (https://www.kokumipd.com/), offering a comprehensive kokumi database and online prediction services for users.

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“…Initial studies mostly focused on building sweet/ non-sweet and bitter/non-bitter classification models using standard molecular descriptors from cheminformatics tools, wherein tastant databases constituted the positive set and random molecules were selected to create the negative set [5,6,7,8,9]. Later, efforts were made to address the sweet-bitter dichotomy by considering both the taste qualities [10,11,12,13]. A few studies also reported predictive models to quantify the taste intensity of molecules using properties like relative sweetness with respect to sucrose [14,15,16].…”

Section: Introductionmentioning

confidence: 99%

Classification of tastants: A deep learning based approach

Dutta,

Jain,

Gupta

et al. 2023

Molecular Informatics

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Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules— the three basic tastes whose sensation is mediated by G protein‐coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups. A deep neural network model based on molecular descriptors and a graph neural network model were trained for taste prediction. The class imbalance due to fewer umami molecules was tackled using special sampling techniques. Both models show comparable performance during evaluation, but the graph‐based model can learn task‐specific representations from the molecular structure without requiring handcrafted features. We further explain the deep neural network predictions using Shapley additive explanations. Finally, we demonstrated the applicability of the models by screening bitter, sweet, and umami molecules from a large food database. This study develops an in‐silico approach to classify molecules based on their taste by leveraging the recent progress in deep learning, which can serve as a powerful tool for tastant design.

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Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network

Cited by 24 publications

References 41 publications

Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning

Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning

Building a Kokumi Database and Machine Learning-Based Prediction: A Systematic Computational Study on Kokumi Analysis

Classification of tastants: A deep learning based approach

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