Bacterial retrons consist of a reverse transcriptase (RT) and a contiguous non-coding RNA (ncRNA) gene. One third of annotated retrons carry additional open reading frames (ORFs), the contribution and significance of which in retron biology remains to be determined. In this study we developed a computational pipeline for the systematic prediction of genes specifically associated with retron RTs based on a previously reported large dataset representative of the diversity of prokaryotic RTs. We found that retrons generally comprise a tripartite system composed of the ncRNA, the RT and an additional protein or RT-fused domain with diverse enzymatic functions. These retron systems are highly modular, and their components have coevolved to different extents. Based on the additional module, we classified retrons into 13 types, some of which include additional variants. Our findings provide a basis for future studies on the biological function of retrons and for expanding their biotechnological applications.

De novo enzymes capable of efficiently catalysis of a non-natural reaction are obtained through minimalist design plus computationally-focused variant library screening.

Glycosidases are phylogenetically widely distributed enzymes that are crucial for the cleavage of glycosidic bonds. Here, we present the exceptional properties of a putative ancestor of bacterial and eukaryotic family-1 glycosidases. The ancestral protein shares the TIM-barrel fold with its modern descendants but displays large regions with greatly enhanced conformational flexibility. Yet, the barrel core remains comparatively rigid and the ancestral glycosidase activity is stable, with an optimum temperature within the experimental range for thermophilic family-1 glycosidases. None of the ∼5500 reported crystallographic structures of ∼1400 modern glycosidases show a bound porphyrin. Remarkably, the ancestral glycosidase binds heme tightly and stoichiometrically at a well-defined buried site. Heme binding rigidifies this TIM-barrel and allosterically enhances catalysis. Our work demonstrates the capability of ancestral protein reconstructions to reveal valuable but unexpected biomolecular features when sampling distant sequence space. The potential of the ancestral glycosidase as a scaffold for custom catalysis and biosensor engineering is discussed.

<div> <div> <div> <p>Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains a sluggish process, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib (funclib-weizmann.ac.il) is a novel automated computational procedure which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of a stability metric. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this a particularly challenging system to optimize. Yet, experimental screening of a very small number of active-site, multi-point variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (~2·104 M-1 s-1 and ~102 s-1) that compare well with modern natural enzymes, and that approach the catalysis levels for the best Kemp eliminases derived from extensive screening. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ~2 kcal·mol-1 and indicate that the improvements in activity are linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by EVB-based computational predictions of catalytic activity, as a generalized approach for computational enzyme design. </p> </div> </div> </div>

The routine generation of enzymes with completely new active sites is a major unsolved problem in protein engineering. Advances in this field have thus far been modest, perhaps due, at least in part, to the widespread use of modern natural proteins as scaffolds for de novo engineering. Most modern proteins are highly evolved and specialized and, consequently, difficult to repurpose for completely new functionalities. Conceivably, resurrected ancestral proteins with the biophysical properties that promote evolvability, such as high stability and conformational diversity, could provide better scaffolds for de novo enzyme generation. Kemp elimination, a non-natural reaction that provides a simple model of proton abstraction from carbon, has been extensively used as a benchmark in de novo enzyme engineering. Here, we present an engineered ancestral β-lactamase with a new active site that is capable of efficiently catalyzing Kemp elimination. The engineering of our Kemp eliminase involved minimalist design based on a single function-generating mutation, inclusion of an extra polypeptide segment at a position close to the de novo active site, and sharply focused, low-throughput library screening. Nevertheless, its catalytic parameters (kcat/KM~2·105 M−1 s−1, kcat~635 s−1) compare favorably with the average modern natural enzyme and match the best proton-abstraction de novo Kemp eliminases that are reported in the literature. The general implications of our results for de novo enzyme engineering are discussed.