8brw

Publication Abstract from PubMed

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 AAs with noncanonical backbones, since an appropriate selective pressure has not been discovered. 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. 18 predicted variants were chosen for experimental testing; 10 exhibited detectable activity for beta-Met adenylation. Top predicted hits were characterized experimentally in detail. Dissociation constants, catalytic rates, and Michaelis constants for both alpha-Met and beta-Met were measured. The best mutant retained a preference for alpha-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 alpha-Met and beta-Met, indicating that the predicted, active conformation of beta-Met in the active site was retained. This article is protected by copyright. All rights reserved.

Redesigning methionyl-tRNA synthetase for beta-methionine activity with adaptive landscape flattening and experiments.,Opuu V, Nigro G, Lazennec-Schurdevin C, Mechulam Y, Schmitt E, Simonson T Protein Sci. 2023 Jul 30:e4738. doi: 10.1002/pro.4738. PMID:37518893^[1]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.