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

The pyrrolysyl-tRNA synthetase (PylRS)/tRNA_CUA pair is one of the most widely used systems for the incorporation of noncanonical amino acids (ncAAs) into proteins at specific positions. Although directed evolution of PylRS have enabled over 300 ncAAs to be incorporated into proteins, most of the ncAA-containing proteins are expressed in a limited yield due to low activities of PylRS variants. Here, we applied machine learning (ML) to engineer the tRNA-binding domain of PylRS with a fast Fourier transform-partial least square regression (FFT-PLSR) model and three zero-shot prediction ML models. FFT-PLSR was first applied to explore a sequence space composed of pairwise combinations of 12 single mutations, and the best variant, Com1-IFRS, showed an 11-fold increase in activity compared to IFRS, a PylRS variant. The deep learning models ESM-1v, Mutcompute, and ProRefiner were then used to identify new mutation sites impacting the activity of Com1-IFRS. FFT-PLSR was used again to identify a variant, Com2-IFRS, from a sequence space containing 11520 mutations, which showed a 30-fold increase in activity. Com2-IFRS also enhanced enzyme activity against 12 other ncAAs by up to 3944.8-fold. Transplantation of the evolved mutations into 7 other PylRS-derived synthetases improved yields of proteins containing six types of ncAAs, including derivatives of Phe, Tyr, Trp, Cys, His and Lys, by up to 1149.7-fold. Molecular dynamics simulations revealed that mutations reshaped the hydrogen bond network between tRNA and protein, which increased tRNA binding affinity, shortened the reaction distance between tRNA and ncAA, and even enhanced the dynamics correlation network. This paper offers new PylRS variants that increase the utility of the orthogonal translation system and provide a machine learning framework for identifying optimized multiple-point combinatorial mutations in a vast sequence space.