[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125661-en":3,"doc-seo-125661-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125661,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine Learning aided Molecular Modelling of Taste to Identify Food Fingerprints","Taste emerges from coordinated biological interactions triggered by food molecules binding to specific protein receptors. Mechanistic molecular modelling combined with machine learning can reveal the molecular, sub-cellular and cellular links governing how food constituents translate into perceived taste. This work uses digital AI-driven tools to build virtual molecular fingerprints of coffee from chemical composition, evaluating binding affinity and specificity toward bitter TAS2Rs with atomistic resolution for predictive decision support in nutrition and food industry.","CHEMICAL ENGINEERING TRANSACTIONS VOL. 102, 2023  \nGuest Editors: Laura Piazza, Mauro Moresi, Francesco Donsì Copyright © 2023, AIDIC Servizi S.r.l.  \nThe Italian Association of Chemical Engineering [Online at www.cetjournal.it](Online at www.cetjournal.it)  \nISBN 979-12-81206-01-4; ISSN 2283-9216   \nMachine Learning aided Molecular Modelling of Taste to  \nIdentify Food Fingerprints  \nLorenzo Pallantea, Marco Cannariatoa, Fosca Vezzullib, Marta Malavoltac , Milena Lambrib, Marco A. Deriua,*  \naPolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy  \nbDepartment for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy cFaculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia marco.deriu@polito.it  \nNature has developed fascinating mechanisms for selecting and monitoring nutrients through refined systems for food intake and uptake. One of the most important is the sense of taste. Taste is an emergent property involving a complex network of multilevel biological interactions beginning with the activation of specific protein receptors as a consequence of interaction with food molecules. In this context, crucial information about the mechanisms underlying the functioning of taste can be obtained by using molecular mechanistic modelling and machine learning tools borrowed from the field of drug design and the study of structural biology and protein biophysics. The ultimate goal is to develop predictive models capable of studying the intricate connection of molecular, sub-cellular and cellular phenomena underlying the complex biological mechanisms that regulate the relationships between food constituents and perceived taste. Artificial intelligence-driven digital tools for taste prediction and the study of molecular features of the interaction between food molecules and taste receptors have been recently developed by our group. Such tools are the operating engines of the decision support tool developed during the VIRTUOUS project ([https://virtuoush2020.com](https://virtuoush2020.com)) .  \nIn this work, these tools were used to generate molecular fingerprints of coffee starting from its chemical composition. Through methods that integrate molecular modelling techniques and machine learning , molecules extracted from coffee were characterized in terms of binding affinity, specificity, and selectivity toward bitter receptors. The targeting ability of coffee-extracted molecules for human TAS2Rs was studied with an atomistic resolution to obtain a virtual fingerprint that links the molecular structure of food ingredients with their bitter profile. The study fits within the digital transition vision that leverages modelling and computational approaches to develop decision-supporting tools for developing solutions in the areas of nutrition, health and the modern food industry.  \n1. Introduction  \nTaste is a multi-layered sensory experience, encompassing the recognition of flavours , which stem from the combination of stimuli from the olfactory, gustatory, and trigeminal systems. The sense of taste is crucial in regulating food consumption as it enables the evaluation of a food's nutritional content and safety, thus preventing the ingestion of hazardous or poisonous substances. (Roper, 2017) . Sweet, umami, bitter, sour, and salty are the five basic taste sensations and each is linked to a specific bodily function. Despite the common association of bitter taste with unpleasant flavours and potentially harmful substances like spoiled food or toxins , not all bitter-tasting compounds, [e.g. coffee](e.g. coffee), unsweetened cocoa, and untreated olives, are harmful or unappetizing. From a molecular point of view, bitter taste receptors belong to the taste 2 receptor family (TAS2Rs) of the G protein-coupled receptors (GPCRs) (Chandrashekar et al. , 2000) . Their structure includes a concise N-terminal located outside of the cell, ","cbCais1utte9ma6f","https://ap.wps.com/l/cbCais1utte9ma6f","pdf",1317356,1,6,"English","en",105,"# Introduction\n## Taste as a multi-layer sensory experience\n## Molecular basis of bitter taste receptors (TAS2Rs)\n## Structural core and ligand recognition mechanisms","[{\"question\":\"How does the document connect taste with molecular modelling and machine learning?\",\"answer\":\"It links taste to receptor-level biological interactions and uses molecular mechanistic modelling plus machine learning to build predictive models connecting food molecular features to taste receptor responses.\"},{\"question\":\"What is the study’s application area?\",\"answer\":\"It focuses on developing digital decision-support tools for nutrition, health, and modern food industry by using computational approaches to interpret food–receptor interactions.\"},{\"question\":\"How were coffee samples handled to derive food fingerprints?\",\"answer\":\"Coffee molecules were extracted from chemical composition, characterized for binding affinity and selectivity toward bitter receptors, and assembled into a virtual fingerprint tied to the bitter profile for TAS2Rs using atomistic resolution.\"}]","Machine Learning aided Molecular Modelling of Taste to Identify Food Fingerprints | 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