Machine Learning-Driven Classification of Bioactive Peptides and Their Molecular Basis for Sour Taste Perception

Bioactive peptides derived from dairy proteins are increasingly recognized for their dual capacity to enhance sensory attributes and provide health-promoting functions. In fermented dairy products such as yogurt, proteolysis releases a diverse array of peptides, including sour-taste peptides that can refine flavor profiles and reduce the need for added acids or synthetic flavorings. However, systematic discovery and characterization of such peptides remain limited by the time and cost of traditional screening methods. Here, we developed an integrated computational-experimental workflow to identify yogurt-derived sour peptides with potential multifunctional applications. A curated data set of labeled sour and non-sour peptides was analyzed for amino acid composition, sequence length, and physicochemical properties, revealing distinct molecular signatures associated with sourness. Multiple machine-learning models, including Multi-Layer Perceptron, Random Forest, and XGBoost, were trained on diverse feature sets, with XGBoost consistently achieving the highest predictive performance (AUC >0.95). Four top-predicted candidates (DSEPV, EPVLL, QEPVL, and RYLGYLE) were synthesized and evaluated using an electronic tongue, confirming DSEPV as the most potent sour-taste peptide, while others exhibited balanced profiles incorporating umami and astringency. To explore potential bioactivities beyond taste, we assessed peptide interactions with the proton channel OTOP1, a protein involved in sour taste transduction and broader physiological processes, using molecular docking and 100 ns molecular dynamics simulations. Stable peptide-protein complexes were observed, with EPVLL showing the most favorable binding free energy, suggesting possible modulation of channel conformation. These findings establish a rapid, cost-effective strategy for discovering flavor-modifying peptides from dairy streams, offering a sustainable route to upcycle fermentation by-products and highlighting the potential for dual-purpose applications in both flavor optimization and functional food development.

Authors

Institutions

Publication Details

Journal
Journal of Dairy Science
Published
2026-04-01
DOI
https://doi.org/10.3168/jds.2026-28183
Primary Topic
Biochemical Analysis and Sensing Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Machine Learning-Driven Classification of Bioactive Peptides and Their Molecular Basis for Sour Taste Perception

Huizi Cui, Wannan Li, Weiwei Han, Yuxuan Wang et al.
Journal of Dairy Science
Biochemical Analysis and Sensing Techniques
article

Machine Learning-Driven Classification of Bioactive Peptides and Their Molecular Basis for Sour Taste Perception

Huizi Cui, Wannan Li, Weiwei Han, Yuxuan Wang, Wenfu Yan, Xueqi Fu, Cheng Cheng
article en

Abstract

Bioactive peptides derived from dairy proteins are increasingly recognized for their dual capacity to enhance sensory attributes and provide health-promoting functions. In fermented dairy products such as yogurt, proteolysis releases a diverse array of peptides, including sour-taste peptides that can refine flavor profiles and reduce the need for added acids or synthetic flavorings. However, systematic discovery and characterization of such peptides remain limited by the time and cost of traditional screening methods. Here, we developed an integrated computational-experimental workflow to identify yogurt-derived sour peptides with potential multifunctional applications. A curated data set of labeled sour and non-sour peptides was analyzed for amino acid composition, sequence length, and physicochemical properties, revealing distinct molecular signatures associated with sourness. Multiple machine-learning models, including Multi-Layer Perceptron, Random Forest, and XGBoost, were trained on diverse feature sets, with XGBoost consistently achieving the highest predictive performance (AUC >0.95). Four top-predicted candidates (DSEPV, EPVLL, QEPVL, and RYLGYLE) were synthesized and evaluated using an electronic tongue, confirming DSEPV as the most potent sour-taste peptide, while others exhibited balanced profiles incorporating umami and astringency. To explore potential bioactivities beyond taste, we assessed peptide interactions with the proton channel OTOP1, a protein involved in sour taste transduction and broader physiological processes, using molecular docking and 100 ns molecular dynamics simulations. Stable peptide-protein complexes were observed, with EPVLL showing the most favorable binding free energy, suggesting possible modulation of channel conformation. These findings establish a rapid, cost-effective strategy for discovering flavor-modifying peptides from dairy streams, offering a sustainable route to upcycle fermentation by-products and highlighting the potential for dual-purpose applications in both flavor optimization and functional food development.

Journal of Dairy Science
Ministry of Education (KN)
Zero hunger
Openalex Percentile: Top 23%
Biochemical Analysis and Sensing Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.