This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: During the production of fermented products such as kefir, Parmesan, or mountain cheese, as well as in the production of protein powders, bitter-tasting peptides can form, which impair the taste and thus the acceptability of the products. A research team led by the Leibniz Institute for Food Systems Biology at the Technical University of Munich has now developed and successfully tested an AI-based method that can predict the bitterness of peptides.
It also makes it possible to design new, bitter-tasting peptides from scratch. This is another important step forward in taste and food research. Bitter-tasting peptides are produced during the enzymatic or chemical breakdown of proteins and pose a particular challenge in the production of fermented foods or protein hydrolysates.
At the same time, they can possess physiological properties and, for example, contribute to the regulation of hunger and satiety. "To make plant-based protein sources more attractive for food production and to use them more sustainably, we need to better understand which peptides taste bitter and what structural features characterize them. AI-based methods can also make an important contribution here," says Antonella Di Pizio, principal investigator of the current study published in npj Science of Food.
To develop such an AI-supported bioinformatics method, the team led by Antonella Di Pizio combined a protein language model—which the team had previously trained using approximately 500 known bitter-tasting peptides—with the BitterPep-GCN prediction model it had recently developed. This is a so-called graph convolutional network (GCN), a specialized form of artificial neural network used to analyze structured data. Based on this, the researchers first generated 161 new peptide sequences that had not yet been experimentally characterized.
They then identified candidates that, according to the model's predictions, were highly likely to taste bitter or non-bitter. They had the most promising of these peptides synthesized and then tasted by a trained sensory panel. In most cases, the AI predictions were confirmed: Of the 31 peptides tested, the test subjects correctly classified 25 as bitter or non-bitter.
In addition, the research team identified numerous previously unknown bitter- and non-bitter-tasting peptides. "Our results show that not only can the bitterness of peptides be predicted, but that our new AI-based method can also be used to specifically design new bitter-tasting peptides," says Alexandra Steuer, first author of the study and a doctoral student in Di Pizio's Molecular Modeling research group. "This brings us significantly closer to the goal of proactively controlling taste characteristics," adds Di Pizio.
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