State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China.
Yunnan Key Laboratory of Potato Biology, The CAAS-YNNU-YINMORE Joint Academy of Potato Sciences, Yunnan Normal University, Kunming, Yunnan 650500, China.
Food Chem. 2024 Jun 15;443:138556. doi: 10.1016/j.foodchem.2024.138556. Epub 2024 Jan 23.
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.
土豆是全球最重要的农作物之一，为快速增长的人口提供食物。除了提供能量、纤维、维生素和矿物质外，土豆贮藏蛋白因其高必需氨基酸（EAA）指数而被认为是最有价值的非动物蛋白来源之一。然而，由于块茎蛋白含量低且对土豆贮藏蛋白的了解有限，限制了它们在食品工业中的广泛应用。在这里，我们使用基于深度学习的蛋白质设计工具进行了概念验证研究，以表征主要的土豆贮藏蛋白——马铃薯蛋白的生物学和化学特性。然后，我们利用这些知识在马铃薯蛋白表面设计多个半胱氨酸，以构建由二硫键连接的聚合物，从而显著提高了土豆粉面团的粘性和营养价值。我们的研究表明，基于深度学习的蛋白质设计策略可有效用于对新型食物来源的蛋白质进行特性分析和设计。
