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<p>The pyrrolysyl-tRNA synthetase (PylRS) is widely used to incorporate noncanonical amino acids (ncAAs) into proteins. However, the&#xa0;yields of&#xa0;most ncAA-containing protein &#xa0;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 (<i>k</i><sub>cat</sub>/<i>K</i><sub>m</sub><sup>tRNA</sup>). Transplanting these mutations into 7 PylRS-derived synthetases significantly improves the&#xa0;yields of proteins containing 6 types of ncAAs. This paper presents improved PylRS variants and a machine learning framework for optimizing the enzyme activity.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月19日

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  • 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,
  • Haoran Yu

摘要

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.