A Translation-Independent Directed Evolution Strategy to Engineer Aminoacyl-tRNA Synthetases.

Using directed evolution, aminoacyl-tRNA synthetases (aaRSs) have been engineered to incorporate numerous noncanonical amino acids (ncAAs). Until now, the selection of such novel aaRS mutants has relied on the expression of a selectable reporter protein. However, such translation-dependent selections are incompatible with exotic monomers that are suboptimal substrates for the ribosome. A two-step solution is needed to overcome this limitation: (A) engineering an aaRS to charge the exotic monomer, without ribosomal translation; (B) subsequent engineering of the ribosome to accept the resulting acyl-tRNA for translation. Here, we report a platform for aaRS engineering that directly selects tRNA-acylation without ribosomal translation (START). In START, each distinct aaRS mutant is correlated to a cognate tRNA containing a unique sequence barcode. Acylation by an active aaRS mutant protects the corresponding barcode-containing tRNAs from oxidative treatment designed to damage the 3'-terminus of the uncharged tRNAs. Sequencing of these surviving barcode-containing tRNAs is then used to reveal the identity of the aaRS mutants that acylated the correlated tRNA sequences. The efficacy of START was demonstrated by identifying novel mutants of the Methanomethylophilus alvus pyrrolysyl-tRNA synthetase from a naïve library that enables incorporation of ncAAs into proteins in living cells.

A map of the rubisco biochemical landscape.

Rubisco is the primary CO2 fixing enzyme of the biosphere yet has slow kinetics. The roles of evolution and chemical mechanism in constraining the sequence landscape of rubisco remain debated. In order to map sequence to function, we developed a massively parallel assay for rubisco using an engineered E. coli where enzyme function is coupled to growth. By assaying >99% of single amino acid mutants across CO2 concentrations, we inferred enzyme velocity and CO2 affinity for thousands of substitutions. We identified many highly conserved positions that tolerate mutation and rare mutations that improve CO2 affinity. These data suggest that non-trivial kinetic improvements are readily accessible and provide a comprehensive sequence-to-function mapping for enzyme engineering efforts.

Protein design using structure-based residue preferences.

Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at individual residues-without accounting for mutation interactions-explain much and sometimes virtually all of the combinatorial mutation effects across 8 datasets (R2 ~ 78-98%). Hence, few observations (~100 times the number of mutated residues) enable accurate prediction of held-out variant effects (Pearson r > 0.80). We hypothesized that the local structural contexts around a residue could be sufficient to predict mutation preferences, and develop an unsupervised approach termed CoVES (Combinatorial Variant Effects from Structure). Our results suggest that CoVES outperforms not just model-free methods but also similarly to complex models for creating functional and diverse protein variants. CoVES offers an effective alternative to complicated models for identifying functional protein mutations.