2021
DOI: 10.1021/acssuschemeng.1c01202
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Computational Protein–Ligand Docking and Experimental Study of Bioplastic Films from Soybean Protein, Zein, and Natural Modifiers

Abstract: Plant-based proteins are emerging at the forefront of functional food trends, as well as sustainable components for various environmentally friendly and sustainable polymeric materials. This study focuses on the application of a combined computational and experimental approach in the design of plant protein-based films from soy protein and zein (corn protein). This work, for the first time, shows the application of a computational protein–ligand docking approach in the design of protein-based films by modeling… Show more

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Cited by 18 publications
(5 citation statements)
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“…Because these materials have the potential for food packaging applications, an understanding of their antimicrobial behavior will be of great importance. Authors will continue to develop new approaches to encode polymeric materials ,,,, and continue to apply advanced machine learning approaches for better characterization and design of new proteoposite materials …”
Section: Results and Discussionmentioning
confidence: 99%
“…Because these materials have the potential for food packaging applications, an understanding of their antimicrobial behavior will be of great importance. Authors will continue to develop new approaches to encode polymeric materials ,,,, and continue to apply advanced machine learning approaches for better characterization and design of new proteoposite materials …”
Section: Results and Discussionmentioning
confidence: 99%
“…AutoDock is a collection of automated docking tools specifically to forecast how small molecules bind to receptor proteins with known three-dimensional (3D) structures. To ascertain the potential conformation of both the ligand molecule and the receptor proteins, the Lamarckian genetic algorithm was employed using AutoDock Tools (version 1.5.6) [ 49 ]. The structures of glutamine, theanine, and ethylamine were acquired from the PubChem website ( https://pubchem.ncbi.nlm.nih.gov/ ) and were initially subjected to energy and geometry optimization prior to the docking process [ 50 ].…”
Section: Methodsmentioning
confidence: 99%
“…Additionally, biodegradable plastic substitutes typically contain multiple natural building blocks, and conventional simulation tools are not efficient to describe such complex systems. Instead, it is highly desirable to have a prediction model that can optimize multiple physicochemical properties of a biodegradable plastic substitute and automatically suggest ideal fabrication parameters 6 , 7 , largely accelerating the research and development processes.…”
Section: Mainmentioning
confidence: 99%