Pro-PRIME: A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins
| Autor: | Tan, Pan, Li, Mingchen, Yu, Yuanxi, Jiang, Fan, Zheng, Lirong, Wu, Banghao, Sun, Xinyu, Kang, Liqi, Song, Jie, Zhang, Liang, Xiong, Yi, Ouyang, Wanli, Hu, Zhiqiang, Fan, Guisheng, Pei, Yufeng, Hong, Liang |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Designing protein mutants of both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce Pro-PRIME, a deep learning zero-shot model, which can suggest protein mutants of improved stability and activity without any prior experimental mutagenesis data. By leveraging temperature-guided language modelling, Pro-PRIME demonstrated superior predictive power compared to current state-of-the-art models on the public mutagenesis dataset over 33 proteins. Furthermore, we carried out wet experiments to test Pro-PRIME on five distinct proteins to engineer certain physicochemical properties, including thermal stability, rates of RNA polymerization and DNA cleavage, hydrolase activity, antigen-antibody binding affinity, or even the nonnatural properties, e.g., the ability to polymerize non-natural nucleic acid or resilience to extreme alkaline conditions. Surprisingly, about 40% AI-designed mutants show better performance than the one before mutation for all five proteins studied and for all properties targeted for engineering. Hence, Pro-PRIME demonstrates the general applicability in protein engineering. Comment: arXiv admin note: text overlap with arXiv:2304.03780 |
| Databáze: | arXiv |
| Externí odkaz: |
|