Literature DB >> 27979110

Sweetness prediction of natural compounds.

Jean-Baptiste Chéron1, Iuri Casciuc1, Jérôme Golebiowski1, Serge Antonczak1, Sébastien Fiorucci2.   

Abstract

Based on the most exhaustive database of sweeteners with known sweetness values, a new quantitative structure-activity relationship model for sweetness prediction has been set up. Analysis of the physico-chemical properties of sweeteners in the database indicates that the structure of most potent sweeteners combines a hydrophobic scaffold functionalized by a limited number of hydrogen bond sites (less than 4 hydrogen bond donors and 10 acceptors), with a moderate molecular weight ranging from 350 to 450g·mol-1. Prediction of sweetness, bitterness and toxicity properties of the largest database of natural compounds have been performed. In silico screening reveals that the majority of the predicted natural intense sweeteners comprise saponin or stevioside scaffolds. The model highlights that their sweetness potency is comparable to known natural sweeteners. The identified compounds provide a rational basis to initiate the design and chemosensory analysis of new low-calorie sweeteners.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Chemical space; Natural compounds; Structure-activity relationship; Sweeteners

Mesh:

Substances:

Year:  2016        PMID: 27979110     DOI: 10.1016/j.foodchem.2016.10.145

Source DB:  PubMed          Journal:  Food Chem        ISSN: 0308-8146            Impact factor:   7.514


  3 in total

1.  A QSTR-Based Expert System to Predict Sweetness of Molecules.

Authors:  Cristian Rojas; Roberto Todeschini; Davide Ballabio; Andrea Mauri; Viviana Consonni; Piercosimo Tripaldi; Francesca Grisoni
Journal:  Front Chem       Date:  2017-07-25       Impact factor: 5.221

2.  e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods.

Authors:  Suqing Zheng; Mengying Jiang; Chengwei Zhao; Rui Zhu; Zhicheng Hu; Yong Xu; Fu Lin
Journal:  Front Chem       Date:  2018-03-29       Impact factor: 5.221

3.  PhytoMolecularTasteDB: An integrative database on the "molecular taste" of Indian medicinal plants.

Authors:  Dorin Dragos; Marilena Gilca
Journal:  Data Brief       Date:  2018-04-21
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.