外文摘要：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.
外文关键词：deep learning;potato;Patatin;Protein design;Disulfide bond
作者：Luo, Xuming;Cao, Lijuan;Yu, Langhua;Gao, Meng;Ai, Ju;Gao, Dongli;Zhang, Xiaopeng;Lucas, William John;Huang, Sanwen;Xu, Jianfei;Shang, Yi
作者单位：Chinese Acad Agr Sci;Univ Calif Davis;Chinese Acad Trop Agr Sci;Yunnan Normal Univ
期刊名称：FOOD CHEMISTRY
期刊影响因子：0.0
出版年份：2024
出版刊次：443
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台