Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids

Qunfeng Zhang, Ling Jiang, Yadan Niu, Yujie Li, Wanyi Chen, Jingxi Cheng, Haote Ding, Binbin Chen, Ke Liu, Jiawen Cao, Junli Wang, Shilin Ye, Lirong Yang, Jianping Wu, Gang Xu, Jianping Lin and Haoran Yu ()
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Qunfeng Zhang: Zhejiang University
Ling Jiang: Zhejiang University
Yadan Niu: Zhejiang University
Yujie Li: Zhejiang University
Wanyi Chen: Zhejiang University
Jingxi Cheng: Zhejiang University
Haote Ding: Zhejiang University
Binbin Chen: Zhejiang University
Ke Liu: Zhejiang University
Jiawen Cao: Zhejiang University
Junli Wang: Zhejiang University
Shilin Ye: Zhejiang University
Lirong Yang: Zhejiang University
Jianping Wu: Zhejiang University
Gang Xu: Zhejiang University
Jianping Lin: Zhejiang University
Haoran Yu: Zhejiang University

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract The pyrrolysyl-tRNA synthetase (PylRS) is widely used to incorporate noncanonical amino acids (ncAAs) into proteins. However, the yields of most ncAA-containing protein remain low due to the limited activity of PylRS variants. Here, we apply machine learning to engineer the tRNA-binding domain of PylRS. The FFT-PLSR model is first applied to explore pairwise combinations of 12 single mutations, generating a variant Com1-IFRS with an 11-fold increase in stop codon suppression (SCS) efficiency. Deep learning models ESM-1v, Mutcompute, and ProRefiner are then used to identify additional mutation sites. Applying FFT-PLSR on these sites yields a variant Com2-IFRS showing a 30.8-fold increase in SCS efficiency, and up to 7.8-fold improvement in the catalytic efficiency (kcat/KmtRNA). Transplanting these mutations into 7 PylRS-derived synthetases significantly improves the yields of proteins containing 6 types of ncAAs. This paper presents improved PylRS variants and a machine learning framework for optimizing the enzyme activity.

Date: 2025
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DOI: 10.1038/s41467-025-61952-2

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