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.