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

Journal: Nature communications
Published Date: Jul 19, 2025

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 (k/K). 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.

Authors

  • Qunfeng Zhang
    Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Ling Jiang
    College of Information Science and Technology, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, China.
  • Yadan Niu
    Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Yujie Li
    College of Orthopedics and Traumatology, Henan University of Chinese Medicine, Zhengzhou, China.
  • Wanyi Chen
    Department of Automation, Xiamen University, Xiamen, China.
  • Jingxi Cheng
    Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Haote Ding
    Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Binbin Chen
    Department of Pharmacy, Xiamen Xianyue Hospital, Xiamen, Fujian, 361012, China.
  • Ke Liu
    State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China.
  • Jiawen Cao
    Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Junli Wang
    Key Laboratory of Embedded System and Service Computing (Tongji University), Ministry of Education, Shanghai 201804, China.
  • Shilin Ye
    Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Lirong Yang
    Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Jianping Wu
    Department of Civil Engineering, Tsinghua University, Beijing, 100084, China.
  • Gang Xu
    University Hospitals of Leicester NHS Trust; UK.
  • Jianping Lin
    The School of Health, Fujian Medical University, Fuzhou, China.
  • Haoran Yu
    College of Veterinary Medicine, Northeast Agricultural University, Harbin 150030, China; Key Laboratory of the Provincial Education, Department of Heilongjiang for Common Animal Disease Prevention and Treatment, Northeast Agricultural University, Harbin 150030, China.

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