Deep learning-based characterization and redesign of major potato tuber storage protein

文献类型： 外文期刊

第一作者： Luo, Xuming

作者： Luo, Xuming;Zhang, Xiaopeng;Huang, Sanwen;Cao, Lijuan;Yu, Langhua;Gao, Meng;Ai, Ju;Gao, Dongli;Shang, Yi;Zhang, Xiaopeng;Lucas, William John;Huang, Sanwen;Xu, Jianfei

作者机构：

关键词： Potato; Patatin; Deep learning; Protein design; Disulfide bond

期刊名称：FOOD CHEMISTRY （ 影响因子：8.8； 五年影响因子：8.6 ）

ISSN： 0308-8146

年卷期： 2024 年 443 卷

页码：

收录情况： SCI

摘要： Potato is one of the most important crops worldwide, to feed a fast-growing population. In addition to providing energy, fiber, vitamins, and minerals, potato storage proteins are considered as one of the most valuable sources of non -animal proteins due to their high essential amino acid (EAA) index. However, low tuber protein content and limited knowledge about potato storage proteins restrict their widespread utilization in the food industry. Here, we report a proof -of -concept study, using deep learning -based protein design tools, to characterize the biological and chemical characteristics of patatins, the major potato storage proteins. This knowledge was then employed to design multiple cysteines on the patatin surface to build polymers linked by disulfide bonds, which significantly improved viscidity and nutrient of potato flour dough. Our study shows that deep learning -based protein design strategies are efficient to characterize and to create novel proteins for future food sources.

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