Redesigning methionyl-tRNA synthetase for β-methionine activity with adaptive landscape flattening and experiments

Abstract

Amino acids (AAs) with a noncanonical backbone would be a valuable tool for protein engineering, enabling new structural motifs and building blocks. To incorporate them into an expanded genetic code, the first, key step is to obtain an appropriate aminoacyl-tRNA synthetase (aaRS). Currently, directed evolution is not available to optimize such AAs, since an appropriate selective pressure is not available. Computational protein design (CPD) is an alternative. We used a new CPD method to redesign MetRS and increase its activity towards {beta}-Met, which has an extra backbone methylene. The new method considered a few active site positions for design and used a Monte Carlo exploration of the corresponding sequence space. During the exploration, a bias energy was adaptively learned, such that the free energy landscape of the apo enzyme was flattened. Enzyme variants could then be sampled, in the presence of the ligand and the bias energy, according to their {beta}-Met binding affinities. Eleven predicted variants were chosen for experimental testing; all exhibited detectable activity for {beta}-Met adenylation. Top predicted hits were characterized experimentally in detail. Dissociation constants, catalytic rates, and Michaelis constants for both -Met and {beta}-Met were measured. The best mutant retained a preference for -Met over {beta}-Met; however, the preference was reduced, compared to the wildtype, by a factor of 29. For this mutant, high resolution crystal structures were obtained in complex with both -Met and {beta}-Met, indicating that the predicted, active conformation of {beta}-Met in the active site was retained. Author summaryAmino acids (AAs) with a noncanonical backbone would be valuable for protein engineering, enabling new structural motifs. To incorporate them into an expanded genetic code, the key step is to obtain an appropriate aminoacyl-tRNA synthetase (aaRS). Currently, directed evolution is not available to optimize such AAs. Computational protein design is an alternative. We used a new method to redesign MetRS and increase its activity towards {beta}-Met, which has an extra backbone methylene. The method considered a few active site positions for design and used a Monte Carlo exploration of sequence space, during which a bias energy was adaptively learned, such that the free energy landscape of the apo enzyme was flattened. Enzyme variants could then be sampled, in the presence of the ligand and the bias energy, according to their {beta}-Met binding affinities. Eleven predicted variants were chosen for experimental testing; all exhibited detectable {beta}-Met adenylation activity. Top hits were characterized experimentally in detail. The best mutant had its preference for -Met over {beta}-Met reduced by a factor of 29. Crystal structures indicated that the predicted, active conformation of {beta}-Met in the active site was retained.