Classification-based machine learning approaches to predict the taste of molecules: A review

Identification of novel umami peptides from oyster hydrolysate and the mechanisms underlying their taste characteristics using machine learning

Excessive sodium consumption poses considerable health risks, prompting the exploration of umami peptides as potential alternative for reducing sodium intake. This research investigated umami peptides (PQFAPEED, EEHPVLLTEA and DQAIPNKPEE) using machine learning, determining sensory thresholds to be 0.24, 0.30 and 0.29 mg/mL. Molecular docking studies revealed hydrogen bonds and hydrophobic interactions are vital for their binding to umami receptors T1R1/T1R3, with key residues identified as Val714, Leu852, Gln853 and Glu855. Combination of DQAIPNKPEE with 3 mg/mL sodium chloride (NaCl) closely mimicked the salinity perception of 5 mg/mL NaCl. Additionally, DQAIPNKPEE and PQFAPEED were recognised as salt-enhancing peptides, with Ala283, Glu284, Glu286, Arg294, Arg330 and Arg583 identified as critical amino acid residues of human transmembrane channel-like 4 (TMC4). These peptides substitute chloride ions to activate TMC4, resulting in sensation of saltiness. This study highlights efficacy of machine learning in rapid identification of umami peptides from oysters and taste receptors interactions.

Database, prediction, and antibacterial research of astringency based on large language models

Astringency, a sensory experience causing mouth dryness, significantly impacts the taste of foods such as wine and tea, and astringent molecules may exhibit antibacterial properties. Traditional methods for predicting astringency are costly, and the connection between astringency and antibacterial activity remains largely unexplored. In this study, we present a pioneering computational approach that includes: (1) the creation of the first comprehensive astringency database comprising 238 molecules; (2) the development of a Ligand-Based Prediction (LBP) framework that combines large language models, deep learning, and traditional machine learning for enhanced molecular and peptide prediction; (3) an astringency predictor achieving 0.95 accuracy and 0.90 AUC, validated through electronic tongue measurements; (4) antibacterial predictors for molecules and peptides with accuracies of 0.92 and 0.88, respectively, revealing that 51 % of astringent molecules possess antibacterial properties; (5) accessibility of these predictors via the AstringentPD and ABPD web servers. This work not only enhances the understanding of taste-related molecules but also elucidates the relationship between astringency and antibacterial properties, setting the stage for future explorations in food science and medicinal applications.

FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data

Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.Scientific Contribution FlavorMiner is an advanced machine learning (ML)-based tool designed to predict molecular flavor features with high accuracy and efficiency, addressing the complexity of food metabolomics. By leveraging robust algorithmic combinations paired with mathematical representations FlavorMiner achieves high predictive performance. Applied to cocoa metabolomics, FlavorMiner demonstrated its capacity to extract meaningful insights, showcasing its versatility for flavor analysis across diverse food products. This study underscores the transformative potential of ML in accelerating flavor biochemistry research, offering a scalable solution for the food and beverage industry.

Flavor Engineering: A comprehensive review of biological foundations, AI integration, industrial development, and socio-cultural dynamics

This state-of-the-art review comprehensively explores flavor development, spanning biological foundations, analytical methodologies, and the socio-cultural impact. It incorporates an industrial perspective and examines the role of artificial intelligence (AI) in flavor science. Initiating with the biological intricacies of flavor, the review delves into the interplay of taste, aroma, and texture rooted in sensory experiences. Advances in mathematical modeling and analytical techniques open avenues for interdisciplinary collaboration and technological innovation, addressing variations in flavor perception. The impact of flavor extends beyond gustatory experiences, influencing economics, society, nutrition, health, and technological innovation. This collective understanding deepens insight into the dynamic interplay between olfactory and flavor elements within cultural landscapes, emphasizing how sensory experiences are woven into human culture and heritage. The evolution of food flavor analysis, encompassing sensory analysis, instrumental analysis, a combination of both, and the integration of artificial intelligence techniques, signifies dynamic progression and, promising advancements in precision, efficiency, and innovation within the flavor industry. This comprehensive review involved analyzing key aspects within flavor engineering and related sectors. Articles and book chapters on these topics were collected using metadata analysis. The data for this analysis was extracted from major online databases, including Scopus, Web of Science, and ScienceDirect.

Advancements in production, assessment, and food applications of salty and saltiness-enhancing peptides: A review

Salt is important for food flavor, but excessive sodium intake leads to adverse health consequences. Thus, salty and saltiness-enhancing peptides are developed for sodium-reduction products. This review elucidates saltiness perception process and analyses correlation between the peptide structure and saltiness-enhancing ability. These peptides interact with taste receptors to produce saltiness perception, including ENaC, TRPV1, and TMC4. This review also outlines preparation, isolation, purification, characterization, screening, and assessment techniques of these peptides and discusses their potential applications. These peptides are from various sources and produced through enzymatic hydrolysis, microbial fermentation, or Millard reaction and then separated, purified, identified, and screened. Sensory evaluation, electronic tongue, bioelectronic tongue, and cell and animal models are the primary saltiness assessment approaches. These peptides can be used in sodium-reduction food products to produce "clean label" items, and the peptides with biological activity can also serve as functional ingredients, making them very promising for food industry.

Taste and its receptors in human physiology: A comprehensive look

Increasing evidence shows that food has significance beyond traditional perception (providing nutrition and energy) in maintaining normal life activities. It is indicated that the sense of taste plays a crucial part in regulating human life activities. Taste is one of the basic physiological sensations in mammals, and it is the fundamental guarantee for them to perceive, select, and ingest nutrients in order to survive. With the advances in electrophysiology, molecular biology, and structural biology, studies on the intracellular and extracellular transduction mechanisms of taste have made great progress and gradually revealed the indispensable role of taste receptors in the regulation and maintenance of normal physiological activities. Up to now, how food regulates life activities through the taste pathway remains unclear. Thus, this review comprehensively and systematically summarizes the current study about the sense of taste, the function of taste receptors, the taste–structure relationship of gustatory molecules, the cross‐talking between distinctive tastes, and the role of the gut–organ axis in the realization of taste. Moreover, we also provide forward‐looking perspectives on taste research to afford a scientific basis for revealing the scientific connotation of taste receptors regulating body health.

Bitter flavors and bitter compounds in foods: identification, perception, and reduction techniques

Bitterness is one of the five basic tastes generally considered undesirable. The widespread presence of bitter compounds can negatively affect the palatability of foods. The classification and sensory evaluation of bitter compounds have been the focus in recent research. However, the rigorous identification of bitter tastes and further studies to effectively mask or remove them have not been thoroughly evaluated. The present paper focuses on identification of bitter compounds in foods, structural-based activation of bitter receptors, and strategies to reduce bitter compounds in foods. It also discusses the roles of metabolomics and virtual screening analysis in bitter taste. The identification of bitter compounds has seen greater success through metabolomics with multivariate statistical analysis compared to conventional chromatography, HPLC, LC-MS, and NMR techniques. However, to avoid false positives, sensory recognition should be combined. Bitter perception involves the structural activation of bitter taste receptors (TAS2Rs). Only 25 human TAS2Rs have been identified as responsible for recognizing numerous bitter compounds, showcasing their high structural diversity to bitter agonists. Thus, reducing bitterness can be achieved through several methods. Traditionally, the removal or degradation of bitter substances has been used for debittering, while the masking of bitterness presents a new effective approach to improving food flavor. Future research in food bitterness should focus on identifying unknown bitter compounds in food, elucidating the mechanisms of activation of different receptors, and developing debittering techniques based on the entire food matrix.

De novo antioxidant peptide design via machine learning and DFT studies

Antioxidant peptides (AOPs) are highly valued in food and pharmaceutical industries due to their significant role in human function. This study introduces a novel approach to identifying robust AOPs using a deep generative model based on sequence representation. Through filtration with a deep-learning classification model and subsequent clustering via the Butina cluster algorithm, twelve peptides (GP1-GP12) with potential antioxidant capacity were predicted. Density functional theory (DFT) calculations guided the selection of six peptides for synthesis and biological experiments. Molecular orbital representations revealed that the HOMO for these peptides is primarily localized on the indole segment, underscoring its pivotal role in antioxidant activity. All six synthesized peptides exhibited antioxidant activity in the DPPH assay, while the hydroxyl radical test showed suboptimal results. A hemolysis assay confirmed the non-hemolytic nature of the generated peptides. Additionally, an in silico investigation explored the potential inhibitory interaction between the peptides and the Keap1 protein. Analysis revealed that ligands GP3, GP4, and GP12 induced significant structural changes in proteins, affecting their stability and flexibility. These findings highlight the capability of machine learning approaches in generating novel antioxidant peptides.

Virtual screening and characteristics of novel umami peptides from porcine type I collagen

This study aimed to rapidly and precisely discover novel umami peptides from porcine type I collagen using virtual screening, sensory evaluation and molecular docking simulation. Porcine type I collagen was hydrolyzed in silico and six umami peptide candidates (CN, SM, CRD, GESMTDGF, MS, DGC) were shortlisted via umami taste, bioactivity, toxicity, allergenicity, solubility and stability predictions. The sensory evaluation confirmed that these peptides exhibited umami taste, with CRD, GESMTDGF and DGC displaying higher umami intensity and significant umami-enhancing effects in 0.35% sodium glutamate solution. Molecular docking predicted that Ser 276/384/385 of T1R1 and Asn68, Val277, Thr305, Ser306, Leu385 of T1R3 may also play critical roles in binding umami peptides. The umami taste of peptides may be perceived mainly through the formation of hydrogen bonds with the hydrophilic amino acids of T1R1/T1R3. This work provided a robust procedure and guidance to develop novel umami peptides from food byproducts.

A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction

Taste determination in small molecules is critical in food chemistry but traditional experimental methods can be time-consuming. Consequently, computational techniques have emerged as valuable tools for this task. In this study, we explore taste prediction using various molecular feature representations and assess the performance of different machine learning algorithms on a dataset comprising 2601 molecules. The results reveal that GNN-based models outperform other approaches in taste prediction. Moreover, consensus models that combine diverse molecular representations demonstrate improved performance. Among these, the molecular fingerprints + GNN consensus model emerges as the top performer, highlighting the complementary strengths of GNNs and molecular fingerprints. These findings have significant implications for food chemistry research and related fields. By leveraging these computational approaches, taste prediction can be expedited, leading to advancements in understanding the relationship between molecular structure and taste perception in various food components and related compounds.