دورية أكاديمية
Enhancing plant-based cheese formulation through molecular docking and dynamic simulation of tocopherol and retinol complexes with zein, soy and almond proteins via SVM-machine learning integration.
| العنوان: | Enhancing plant-based cheese formulation through molecular docking and dynamic simulation of tocopherol and retinol complexes with zein, soy and almond proteins via SVM-machine learning integration. |
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| المؤلفون: | Yakoubi S; Faculty of Life and Environmental Sciences, University of Tsukuba, Ibaraki 305-8572, Japan; Alliance for Research on the Mediterranean North Africa (ARENA), University of Tsukuba, Ibaraki, Japan; University of Tunis El Manar, 1068 Tunis, Tunisia. Electronic address: sanayakoubi3@gmail.com. |
| المصدر: | Food chemistry [Food Chem] 2024 Sep 15; Vol. 452, pp. 139520. Date of Electronic Publication: 2024 May 03. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 7702639 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-7072 (Electronic) Linking ISSN: 03088146 NLM ISO Abbreviation: Food Chem Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Barking : Elsevier Applied Science Publishers Original Publication: Barking, Eng., Applied Science Publishers. |
| مواضيع طبية MeSH: | Molecular Docking Simulation* , Plant Proteins*/chemistry , Plant Proteins*/metabolism , Cheese*/analysis , Prunus dulcis*/chemistry , Vitamin A*/chemistry , Vitamin A*/metabolism , Tocopherols*/chemistry , Tocopherols*/metabolism , Zein*/chemistry , Zein*/metabolism, Molecular Dynamics Simulation ; Machine Learning ; Glycine max/chemistry ; Glycine max/metabolism ; Support Vector Machine |
| مستخلص: | The current study addresses the growing demand for sustainable plant-based cheese alternatives by employing molecular docking and deep learning algorithms to optimize protein-ligand interactions. Focusing on key proteins (zein, soy, and almond protein) along with tocopherol and retinol, the goal was to improve texture, nutritional value, and flavor characteristics via dynamic simulations. The findings demonstrated that the docking analysis presented high accuracy in predicting conformational changes. Flexible docking algorithms provided insights into dynamic interactions, while analysis of energetics revealed variations in binding strengths. Tocopherol exhibited stronger affinity (-5.8Kcal/mol) to zein compared to retinol (-4.1Kcal/mol). Molecular dynamics simulations offered comprehensive insights into stability and behavior over time. The integration of machine learning algorithms improved the classification and the prediction accuracy, achieving a rate of 71.59%. This study underscores the significance of molecular understanding in driving innovation in the plant-based cheese industry, facilitating the development of sustainable alternatives to traditional dairy products. Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare. (Copyright © 2024. Published by Elsevier Ltd.) |
| فهرسة مساهمة: | Keywords: Autodock Vina; Cheese texturization; Food innovation; Machine learning; Molecular dynamics simulations; Plant-based cheese; Protein-ligand interactions |
| المشرفين على المادة: | 0 (Plant Proteins) 11103-57-4 (Vitamin A) R0ZB2556P8 (Tocopherols) 9010-66-6 (Zein) |
| تواريخ الأحداث: | Date Created: 20240509 Date Completed: 20240530 Latest Revision: 20240530 |
| رمز التحديث: | 20240531 |
| DOI: | 10.1016/j.foodchem.2024.139520 |
| PMID: | 38723573 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1873-7072 |
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| DOI: | 10.1016/j.foodchem.2024.139520 |