NMR-guided directed evolution.

Directed evolution is a powerful tool for improving existing properties and imparting completely new functionalities to proteins1-4. Nonetheless, its potential in even small proteins is inherently limited by the astronomical number of possible amino acid sequences. Sampling the complete sequence space of a 100-residue protein would require testing of 20100 combinations, which is beyond any existing experimental approach. In practice, selective modification of relatively few residues is sufficient for efficient improvement, functional enhancement and repurposing of existing proteins5. Moreover, computational methods have been developed to predict the locations and, in certain cases, identities of potentially productive mutations6-9. Importantly, all current approaches for prediction of hot spots and productive mutations rely heavily on structural information and/or bioinformatics, which is not always available for proteins of interest. Moreover, they offer a limited ability to identify beneficial mutations far from the active site, even though such changes may markedly improve the catalytic properties of an enzyme10. Machine learning methods have recently showed promise in predicting productive mutations11, but they frequently require large, high-quality training datasets, which are difficult to obtain in directed evolution experiments. Here we show that mutagenic hot spots in enzymes can be identified using NMR spectroscopy. In a proof-of-concept study, we converted myoglobin, a non-enzymatic oxygen storage protein, into a highly efficient Kemp eliminase using only three mutations. The observed levels of catalytic efficiency exceed those of proteins designed using current approaches and are similar with those of natural enzymes for the reactions that they are evolved to catalyse. Given the simplicity of this experimental approach, which requires no a priori structural or bioinformatic knowledge, we expect it to be widely applicable and to enable the full potential of directed enzyme evolution.

Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes.

Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.

Catalytic Nanoassemblies Formed by Short Peptides Promote Highly Enantioselective Transfer Hydrogenation.

Self-assembly enables formation of incredibly diverse supramolecular structures with practically important functions from simple and inexpensive building blocks. Here, we show how a semirational, bottom-up approach to create emerging properties can be extended to a design of highly enantioselective catalytic nanoassemblies. The designed peptides comprising as few as two amino acid residues spontaneously self-assemble in the presence of metal ions to form supramolecular, vesicle-like nanoassemblies that promote transfer hydrogenation of ketones in an aqueous phase with excellent conversion rates and enantioselectivities (>90% ee).

Kemp Eliminases of the AlleyCat Family Possess High Substrate Promiscuity.

Minimalist enzymes designed to catalyze model reactions provide useful starting points for creating catalysts for practically important chemical transformations. We have shown that Kemp eliminases of the AlleyCat family facilitate conversion of leflunomide (an immunosupressor pro-drug) to its active form teriflunomide with outstanding rate enhancement (nearly four orders of magnitude) and catalytic proficiency (more than seven orders of magnitude) without any additional optimization. This remarkable activity is achieved by properly positioning the substrate in close proximity to the catalytic glutamate with very high pKa.