An important 2′-OH group for an RNA–protein interaction

We have investigated the role of 2′-OH groups in the specific interaction between the acceptor stem of Escherichia coli tRNACys and cysteine-tRNA synthetase. This interaction provides for the high aminoacylation specificity observed for cysteine-tRNA synthetase. A synthetic RNA microhelix that recapitulates the sequence of the acceptor stem was used as a substrate and variants containing systematic replacement of the 2′-OH by 2′-deoxy or 2′-O-methyl groups were tested. Except for position U73, all substitutions had little effect on aminoacylation. Interestingly, the deoxy substitution at position U73 had no effect on aminoacylation, but the 2′-O-methyl substitution decreased aminoacylation by 10-fold and addition of the even bulkier 2′-O-propyl group decreased aminoacylation by another 2-fold. The lack of an effect by the deoxy substitution suggests that the hydrogen bonding potential of the 2′-OH at position U73 is unimportant for aminoacylation. The decrease in activity upon alkyl substitution suggests that the 2′-OH group instead provides a monitor of the steric environment during the RNA–synthetase interaction. The steric role was confirmed in the context of a reconstituted tRNA and is consistent with the observation that the U73 base is the single most important determinant for aminoacylation and therefore is a site that is likely to be in close contact with cysteine-tRNA synthetase. A steric role is supported by an NMR-based structural model of the acceptor stem, together with biochemical studies of a closely related microhelix. This role suggests that the U73 binding site for cysteine-tRNA synthetase is sterically optimized to accommodate a 2′-OH group in the backbone, but that the hydroxyl group itself is not involved in specific hydrogen bonding interactions.

Computational method to reduce the search space for directed protein evolution

We introduce a computational method to optimize the in vitro evolution of proteins. Simulating evolution with a simple model that statistically describes the fitness landscape, we find that beneficial mutations tend to occur at amino acid positions that are tolerant to substitutions, in the limit of small libraries and low mutation rates. We transform this observation into a design strategy by applying mean-field theory to a structure-based computational model to calculate each residue's structural tolerance. Thermostabilizing and activity-increasing mutations accumulated during the experimental directed evolution of subtilisin E and T4 lysozyme are strongly directed to sites identified by using this computational approach. This method can be used to predict positions where mutations are likely to lead to improvement of specific protein properties.