Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China; Shanghai National Centre for Applied Mathematics (SJTU Center), MOE-LSC, Shanghai Jiao Tong University, Shanghai, China
Pan Tan
Shanghai Artificial Intelligence Laboratory, Shanghai, China
Yun (Kenneth) Kang
Changchun GeneScience Pharmaceuticals Co., Ltd., Jilin, China
Yongzhen Yan
Changchun GeneScience Pharmaceuticals Co., Ltd., Jilin, China
Yi Zong
Changchun GeneScience Pharmaceuticals Co., Ltd., Jilin, China
Shuang Li
Changchun GeneScience Pharmaceuticals Co., Ltd., Jilin, China
Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China; Shanghai National Centre for Applied Mathematics (SJTU Center), MOE-LSC, Shanghai Jiao Tong University, Shanghai, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Harbin Medical University, Harbin, China
School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, China; Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China; Shanghai National Centre for Applied Mathematics (SJTU Center), MOE-LSC, Shanghai Jiao Tong University, Shanghai, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China; Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China
Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance, and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.
