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

Autor: Lorenzo Pallante, Aigli Korfiati, Lampros Androutsos, Filip Stojceski, Agorakis Bompotas, Ioannis Giannikos, Christos Raftopoulos, Marta Malavolta, Gianvito Grasso, Seferina Mavroudi, Athanasios Kalogeras, Vanessa Martos, Daria Amoroso, Dario Piga, Konstantinos Theofilatos, Marco A. Deriu
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-022-25935-3
Popis: Abstract 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.
Databáze: Directory of Open Access Journals
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