On the human taste perception: Molecular-level understanding empowered by computational methods

Pallante, Lorenzo; Malavolta, Marta; Grasso, Gianvito; Korfiati, Aigli; Mavroudi, Seferina; Mavkov, Bojan; Καλογεράς, Αθανάσιος; Alexakos, Christos; Martos, Vanessa; Amoroso, Daria; Benedetto, G; Piga, Dario; Theofilatos, Konstantinos; Deriu, Marco Agostino

doi:10.1016/j.tifs.2021.07.013

Cited by 23 publications

(21 citation statements)

References 119 publications

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“…Of the 25 known bitter taste receptors, only 21 had at least one known bitter ligand. Accordingly, the homology models of 21 bitter taste receptors hTas2r1, hTas2r3, hTas2r4, hTas2r5, hTas2r7, hTas2r8, hTas2r9, hTas2r10, hTas2r13, hTas2r14, hTas2r16, hTas2r38, hTas2r39, hTas2r40, hTas2r41, hTas2r43, hTas2r44, hTas2r46, hTas2r47, hTas2r49, and hTas2r50 using the β2 adrenergic receptor (PDB ID: 3SN65) as a template were obtained from BitterDB. , The medaka fish taste receptor T1r2a-T1r3 (PDB ID: 5X2M, resolution in X-ray diffraction: 2.21 Å) was considered to be a more realistic template, resulting in a more accurate screening result. Therefore, it was used as a template for hTAS1R2-hTAS1R3 in this study.…”

Section: Methodsmentioning

confidence: 99%

“…Computational methods such as molecular modeling, molecular docking, and structure-or ligand-based virtual screening have been used extensively on key tastants identified. 13 For example, in silico studies have been used to uncover hop-derived compounds as a very particular class of bitter compounds. 14 Several molecular modeling approaches have been applied to the binding site and activation mechanism of maltitol and lactitol for the human sweet taste receptor.…”

Section: ■ Introductionmentioning

confidence: 99%

“…A more general solution involving calculation methods has been proposed. Computational methods such as molecular modeling, molecular docking, and structure- or ligand-based virtual screening have been used extensively on key tastants identified . For example, in silico studies have been used to uncover hop-derived compounds as a very particular class of bitter compounds .…”

Section: Introductionmentioning

confidence: 99%

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Discovery of N-l-Lactoyl-l-Trp as a Bitterness Masker via Structure-Based Virtual Screening and a Sensory Approach

Zhao

Zhou

Cui

et al. 2023

J. Agric. Food Chem.

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N-Lactoyl-amino acid derivatives (N-Lac-AAs) are of increasing interest as potential taste-active compounds. The complexity and diversity of N-Lac-AAs pose a significant challenge to the effective discovery of taste-active N-Lac-AAs. Therefore, a structure-based virtual screening was used to identify taste-active N-Lac-AAs. Virtual screening results showed that N-lactoylhydrophobic amino acids had a higher affinity for taste receptors, specifically N-L-Lac-L-Trp. And then, N-L-Lac-L-Trp was synthesized in yields of 22.3% by enzymatic synthesis in the presence of L-lactate and L-Trp, and its chemical structure was confirmed by MS/MS and one-dimensional (1D) and two-dimensional (2D) NMR. Sensory evaluation revealed that N-L-Lac-L-Trp had a significant taste-masking effect on quinine, D-salicin, caffeine, and L-Trp, particularly L-Trp and caffeine. N-L-Lac-L-Trp had a better masking effect on the higher concentration of bitter compounds. It reduced the bitterness of caffeine (500 mg/L) and L-Trp (1000 mg/L) by approximately 20 and 26%, respectively. The result of the ligand−receptor interaction and a quantum mechanical analysis showed that N-L-Lac-L-Trp increased the binding affinity to the bitter receptor mainly through hydrogen bonding and lowering the electrostatic potential.

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Section: Methodsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discovery of N-l-Lactoyl-l-Trp as a Bitterness Masker via Structure-Based Virtual Screening and a Sensory Approach

Zhao

Zhou

Cui

et al. 2023

J. Agric. Food Chem.

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“…Machine Learning methods have proven to play a key role in the development of prediction tools and digital support systems in a variety of application areas, including nutrition and agri-food research 35 – 42 . In this context, here, we developed a novel machine-learning-driven umami taste predictor, named VirtuousUmami, to identify umami/non-umami compounds based on the SMILES representation.…”

Section: Discussionmentioning

confidence: 99%

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach

Pallante

Korfiati²,

Androutsos³

et al. 2022

Sci Rep

Self Cite

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The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.

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“…We have known for a long time that many amino acids that form stereoisomers taste different, as do the glucose enantiomers L and D and mannose anomers taste different, [20][21][22][23] .…”

Section: Molecular Structure and Organoleptic Properties Of Sensory S...mentioning

confidence: 99%

Phenomenology of perception, from stimuli to their interpretation

Riccardi¹

2022

World J. Adv. Res. Rev.

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Receptors systems are our window on the world, the tools that relate us to the outside world and allow us to orient ourselves in all the activities that are fundamental for our survival. Thanks to the receptor systems we have the awareness and consciousness of human beings and have acquired the knowledge of the world in which we live. Receptors have a fundamental role to play in the birth of culture and the science of peoples. In addition to having an informative function of environmental conditions, the receptors perform an important protective and defensive activity for the living. In fact, they have the ability to make us recognize useful substances from harmful ones based on their organoleptic characteristics, color, smell, flavor, favoring the consumption of the first, preventing that of the second. They also form a second protective barrier at the level of the cell membrane by selecting the entry of unnecessary and potentially harmful substances and preventing their absorption through selective cellular channels. By means of channel proteins and ion exchange proteins (for example sodium-potassium), they maintain the optimal concentration of solutes in the cell. Finally, they guide the intra and interspecific relations between all living beings, from plants to man. We owe our existence to the reception systems. In this article we are going to examine the receptor systems describing the importance they have had in the evolution of the living and we are going to critically discuss the most recent theories proposed by quantum mechanics to explain their functioning.

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On the human taste perception: Molecular-level understanding empowered by computational methods

Cited by 23 publications

References 119 publications

Discovery of N-l-Lactoyl-l-Trp as a Bitterness Masker via Structure-Based Virtual Screening and a Sensory Approach

Discovery of N-l-Lactoyl-l-Trp as a Bitterness Masker via Structure-Based Virtual Screening and a Sensory Approach

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach

Phenomenology of perception, from stimuli to their interpretation

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