Designing catalysts that achieve the rates and selectivities of natural enzymes is a long-standing goal in protein chemistry. Here, we show that an ultrahigh-throughput droplet-based microfluidic screening platform can be used to improve a previously optimized artificial aldolase by an additional factor of 30 to give a >10 rate enhancement that rivals the efficiency of class I aldolases. The resulting enzyme catalyses a reversible aldol reaction with high stereoselectivity and tolerates a broad range of substrates. Biochemical and structural studies show that catalysis depends on a Lys-Tyr-Asn-Tyr tetrad that emerged adjacent to a computationally designed hydrophobic pocket during directed evolution. This constellation of residues is poised to activate the substrate by Schiff base formation, promote mechanistically important proton transfers and stabilize multiple transition states along a complex reaction coordinate. The emergence of such a sophisticated catalytic centre shows that there is nothing magical about the catalytic activities or mechanisms of naturally occurring enzymes, or the evolutionary process that gave rise to them.

Evolutionary advances are often fueled by unanticipated innovation. Directed evolution of a computationally designed enzyme suggests that dramatic molecular changes can also drive the optimization of primitive protein active sites. The specific activity of an artificial retro-aldolase was boosted >4,400 fold by random mutagenesis and screening, affording catalytic efficiencies approaching those of natural enzymes. However, structural and mechanistic studies reveal that the engineered catalytic apparatus, consisting of a reactive lysine and an ordered water molecule, was unexpectedly abandoned in favor of a new lysine residue in a substrate binding pocket created during the optimization process. Structures of the initial in silico design, a mechanistically promiscuous intermediate, and one of the most evolved variants highlight the importance of loop mobility and supporting functional groups in the emergence of the new catalytic center. Such internal competition between alternative reactive sites may have characterized the early evolution of many natural enzymes.

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From their early roots in natural products, peptides now represent an expanding class of novel drugs. Their modular structures make them ideal candidates for pooled library screening approaches. Key technologies for library generation and screening, such as SICLOPPS, phage display and mRNA display, give unparalleled access to tight binding peptides. Through combination with genetic code reprogramming and chemical modifications, access to more natural product-like libraries, spanning non-canonical peptide space, is readily achievable. Recent advances in these fields enable introduction of diverse non-standard motifs, such as cyclisation and backbone methylations. Peptide discovery platforms now allow robust access to potent, highly functionalised peptides against virtually any protein of interest, with typical binding constants in the nanomolar range. Application of these optimised platforms in a drug discovery setting has the potential to significantly accelerate identification of new leads.

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The ability to programme new modes of catalysis into proteins would allow the development of enzyme families with functions beyond those found in nature. To this end, genetic code expansion methodology holds particular promise, as it allows the site-selective introduction of new functional elements into proteins as non-canonical amino acid side chains. [1][2][3][4] Here, we exploit an expanded genetic code to develop a photoenzyme that operates via triplet energy transfer catalysis, a versatile mode of reactivity in organic synthesis that is currently not accessible to biocatalysis. [5][6][7][8][9][10][11][12] Installation of a genetically encoded photosensitiser into the beta-propeller scaffold of DA_20_00 13 converts a de novo Diels-Alderase into a photoenzyme for [2+2]cycloadditions (EnT1.0). Subsequent development and implementation of a platform for photoenzyme evolution afforded an efficient and enantioselective enzyme (EnT1.3, up to 99% e.e.) that can promote selective cycloadditions that have proven challenging to achieve with small molecule catalysts. EnT1.3 performs >300 turnovers and, in contrast to small molecule photocatalysts, can operate effectively under aerobic conditions. A 1.7 Å resolution X-ray crystal structure of an EnT1.3-product complex shows how multiple functional components work in synergy to promote efficient and selective photocatalysis. This study opens the door to a wealth of new excited-state chemistry in protein active sites and establishes the framework for developing a new generation of evolvable photocatalysts with efficiencies and specificities akin to natural enzymes.

De novo biocatalysts with non-natural functionality are accessible by computational enzyme design. The catalytic activities obtained for the initial designs are usually low, but can be optimized significantly by directed evolution. Nevertheless, rate accelerations approaching the level of natural enzymes can only be achieved over many rounds of tedious and time-consuming laboratory evolution. In this work, we show that microfluidic-based screening using fluorescence-activated droplet sorting (FADS) is ideally suited for efficient optimization of designed enzymes with low starting activity, essentially straight out of the computer. We chose the designed retro-aldolase RA95.0, which had been previously evolved by conventional microtiter plate screening, as an example and reoptimized it using the microfluidic-based assay. Our results show that FADS is sufficiently sensitive to detect enzyme activities as low as kcat/Km = 0.5 M(-1)s(-1) The ultra-high throughput of this system makes screening of large mutant libraries possible in which clusters of up to five residues are randomized simultaneously. Thus, combinations of beneficial mutations can be identified directly, leading to large jumps in catalytic activity of up to 80-fold within a single round of evolution. By exploring several evolutionary trajectories in parallel, we identify alternative active site arrangements that exhibit comparably enhanced efficiency but opposite enantioselectivity.

De novo biocatalysts with non-natural functionality are accessible by computational enzyme design. The catalytic activities obtained for the initial designs are usually low, but can be optimized significantly by directed evolution. Nevertheless, rate accelerations approaching the level of natural enzymes can only be achieved over many rounds of tedious and time-consuming laboratory evolution. In this work, we show that microfluidic-based screening using fluorescence-activated droplet sorting (FADS) is ideally suited for efficient optimization of designed enzymes with low starting activity, essentially straight out of the computer. We chose the designed retro-aldolase RA95.0, which had been previously evolved by conventional microtiter plate screening, as an example and reoptimized it using the microfluidic-based assay. Our results show that FADS is sufficiently sensitive to detect enzyme activities as low as k cat /K m = 0.5 M -1 s -1 . The ultra-high throughput of this system makes screening of large mutant libraries possible in which clusters of up to five residues are randomized simultaneously. Thus, combinations of beneficial mutations can be identified directly, leading to large jumps in catalytic activity of up to 80-fold within a single round of evolution. By exploring several evolutionary trajectories in parallel, we identify alternative active site arrangements that exhibit comparably enhanced efficiency but opposite enantioselectivity.