2024年1月23日，Elsevier 旗下国际著名期刊《Food Chemistry》在线发表了云南师范大学生命科学学院、马铃薯科学研究院尚轶教授、农科院深圳基因研究所、中国热带农业科学院黄三文院士、中国农科院蔬菜与花卉研究所徐建飞副研究员合作的最新研究成果《Deep learning-based characterization and redesign of major potato tuber storage protein》。

Abstract

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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