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

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

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 ( k cat / K m tRNA ). 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.