Plant-based biopolymer materials present an attractive alternative to synthetic materials for various applications, including biodegradable plastics, adhesives, coatings, textiles, and tissue engineering materials for pharmaceutical use. In particular, the wide availability of agricultural excess products makes them an energy-saving, low-cost, high-yield material source. However, a bottleneck remains in fast and effective processing of plant-derived materials. In this work, we develop novel corn-derived proteins based on zein, a major storage protein of corn that comprises up to 50% of all kernel protein content in corn. Zein is biocompatible and biodegradable, exhibits reduced immunogenicity in comparison to animal-derived materials ‚Äì essential for biomedical applications, and can be readily processed. In particular, incorporating tunable solubility into zein presents many additional commercial and biomedical opportunities to simplify processing of the material. In recent years, sequence-based solubility prediction software made it possible to perform large-scale predictions. Here, we develop a general paradigm for introducing solubility into protein sequences through a high-throughput smart-sequence generation method integrated with a novel machine learning-based approach to predict solubility. We compare our model with existing predictors and find that it performs favorably with existing models. Through this work, we extract molecular design principles for introducing solubility into zein protein, as a case study that can be extended to other agricultural byproduct materials. Our work leverages the renewable capacity of crop plants for feedstock production, and presents a novel approach to design crop plants via smarter genetic engineering in the future.

Tuning the processing capacity of plant-based biopolymers with a high-throughput machine learning approach

The Virtual Technical Meeting of the Society of Engineering Science 2020 is the first fully virtual meeting of the SES community to envision and connect during the hiatus created by Covid-19, providing a platform for the exchange of ideas, and to showcase the latest research in the field of engineering science.

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