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Assessing Molecular Docking Tools to Guide the Design of Polymeric Materials Formulations: A Case Study of Canola and Soybean Protein

After more than 40 years of biopolymer development, the current research is still based on conventional laboratory techniques, which require a large number of experiments. Therefore, finding new research methods are required to accelerate and power the future of biopolymeric development. In this study, promising biopolymer–additive ranking was described using an integrated computer-aided molecular design platform. In this perspective, a set of 21 different additives with plant canola and soy proteins were initially examined by predicting the molecular interactions scores and mode of molecule interactions within the binding site using AutoDock Vina, Molecular Operating Environment (MOE), and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA). The findings of the investigated additives highlighted differences in their binding energy, binding sites, pockets, types, and distance of bonds formed that play crucial roles in protein–additive interactions. Therefore, the molecular docking approach can be used to rank the optimal additive among a set of candidates by predicting their binding affinities. Furthermore, specific molecular-level insights behind protein–additives interactions were provided to explain the ranking results. The highlighted results can provide a set of guidelines for the design of high-performance polymeric materials at the molecular level. As a result, we suggest that the implementation of molecular modeling can serve as a fast and straightforward tool in protein-based bioplastics design, where the correct ranking of additives among sets of candidates is often emphasized. Moreover, these approaches may open new ways for the discovery of new additives and serve as a starting point for more in-depth investigations into this area.

Publication date: 05/09/2022

Author: Frage Abookleesh

Reference: doi: 10.3390/polym14173690

MDPI (polymers)


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870292.