Effective engineering of a ketoreductase for the biocatalytic synthesis of an ipatasertib precursor.

Semi-rational enzyme engineering is a powerful method to develop industrial biocatalysts. Profiting from advances in molecular biology and bioinformatics, semi-rational approaches can effectively accelerate enzyme engineering campaigns. Here, we present the optimization of a ketoreductase from Sporidiobolus salmonicolor for the chemo-enzymatic synthesis of ipatasertib, a potent protein kinase B inhibitor. Harnessing the power of mutational scanning and structure-guided rational design, we created a 10-amino acid substituted variant exhibiting a 64-fold higher apparent kcat and improved robustness under process conditions compared to the wild-type enzyme. In addition, the benefit of algorithm-aided enzyme engineering was studied to derive correlations in protein sequence-function data, and it was found that the applied Gaussian processes allowed us to reduce enzyme library size. The final scalable and high performing biocatalytic process yielded the alcohol intermediate with ≥ 98% conversion and a diastereomeric excess of 99.7% (R,R-trans) from 100 g L-1 ketone after 30 h. Modelling and kinetic studies shed light on the mechanistic factors governing the improved reaction outcome, with mutations T134V, A238K, M242W and Q245S exerting the most beneficial effect on reduction activity towards the target ketone.

Improving Enzyme Fitness with Machine Learning.

The combinatorial composition of proteins has triggered the application of machine learning in enzyme engineering. By predicting how protein sequence encodes function, researchers aim to leverage machine learning models to select a reduced number of optimized sequences for laboratory measurement with the aim to lower costs and shorten timelines of enzyme engineering campaigns. In this review, we will highlight successful algorithm-aided protein engineering examples, including work carried out within the NCCR Catalysis. In this context, we will discuss the underlying computational methods developed to improve enzyme properties such as enantioselectivity, regioselectivity, activity, and stability. Considering the rapid maturing of computational techniques, we expect that their continued application in enzyme engineering campaigns will be key to deliver additional powerful biocatalysts for sustainable chemical synthesis.

LibGENiE - A bioinformatic pipeline for the design of information-enriched enzyme libraries.

Enzymes are potent catalysts with high specificity and selectivity. To leverage nature's synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, the many possible ways a protein can be altered forces researchers to carefully balance between the exhaustiveness of an enzyme screening campaign and the required resources. Consequently, the optimal engineering strategy is often defined on a case-by-case basis. Strikingly, while predicting mutations that lead to an improved target function is challenging, here we show that the prediction and exclusion of deleterious mutations is a much more straightforward task as analyzed for an engineered carbonic acid anhydrase, a transaminase, a squalene-hopene cyclase and a Kemp eliminase. Combining such a pre-selection of allowed residues with advanced gene synthesis methods opens a path toward an efficient and generalizable library construction approach for protein engineering. To give researchers easy access to this methodology, we provide the website LibGENiE containing the bioinformatic tools for the library design workflow.

Algorithm-aided engineering of aliphatic halogenase WelO5* for the asymmetric late-stage functionalization of soraphens.

Late-stage functionalization of natural products offers an elegant route to create novel entities in a relevant biological target space. In this context, enzymes capable of halogenating sp3 carbons with high stereo- and regiocontrol under benign conditions have attracted particular attention. Enabled by a combination of smart library design and machine learning, we engineer the iron/α-ketoglutarate dependent halogenase WelO5* for the late-stage functionalization of the complex and chemically difficult to derivatize macrolides soraphen A and C, potent anti-fungal agents. While the wild type enzyme WelO5* does not accept the macrolide substrates, our engineering strategy leads to active halogenase variants and improves upon their apparent kcat and total turnover number by more than 90-fold and 300-fold, respectively. Notably, our machine-learning guided engineering approach is capable of predicting more active variants and allows us to switch the regio-selectivity of the halogenases facilitating the targeted analysis of the derivatized macrolides' structure-function activity in biological assays.

Asymmetric Cation-Olefin Monocyclization by Engineered Squalene-Hopene Cyclases.

Squalene-hopene cyclases (SHCs) have great potential for the industrial synthesis of enantiopure cyclic terpenoids. A limitation of SHC catalysis has been the enzymes' strict (S)-enantioselectivity at the stereocenter formed after the first cyclization step. To gain enantio-complementary access to valuable monocyclic terpenoids, an SHC-wild-type library including 18 novel homologs was set up. A previously not described SHC (AciSHC) was found to synthesize small amounts of monocyclic (R)-γ-dihydroionone from (E/Z)-geranylacetone. Using enzyme and process optimization, the conversion to the desired product was increased to 79 %. Notably, analyzed AciSHC variants could finely differentiate between the geometric geranylacetone isomers: While the (Z)-isomer yielded the desired monocyclic (R)-γ-dihydroionone (>99 % ee), the (E)-isomer was converted to the (S,S)-bicyclic ether (>95 % ee). Applying the knowledge gained from the observed stereodivergent and enantioselective transformations to an additional SHC-substrate pair, access to the complementary (S)-γ-dihydroionone (>99.9 % ee) could be obtained.

Production of the Carboxylate Reductase from Nocardia otitidiscaviarum in a Soluble, Active Form for in†vitro Applications.

Accessing aldehydes from carboxylate moieties is often a challenging task. In this regard, carboxylate reductases (CARs) are promising catalysts provided by nature that are able to accomplish this task in just one step, avoiding over-reduction to the alcohol product. However, the heterologous expression of CARs can be quite difficult due to the excessive formation of insoluble protein, thus hindering further characterization and application of the enzyme. Here, the heterologous production of the carboxylate reductase from Nocardia otitidiscaviarum (NoCAR) was optimized by a combination of i) optimized cultivation conditions, ii) post-translational modification with a phosphopantetheinyl transferase and iii) selection of an appropriate expression strain. Especially, the selection of Escherichia coli tuner cells as host had a strong effect on the final 110-fold increase in the specific activity of NoCAR. This highly active NoCAR was used to reduce sodium benzoate to benzaldehyde, and it was successfully assembled with an in vitro regeneration of ATP and NADPH, being capable of reducing about 30 mM sodium benzoate with high selectivity in only 2 h of reaction.