Engineering Hydroxylase Activity, Selectivity, and Stability for a Scalable Concise Synthesis of a Key Intermediate to Belzutifan

DiRocco D.A., Zhong Y., Le D.N., McCann S.D., Hethcox J.C., Kim J., Kolev J.N., Kosjek B., Dalby S.M., McMullen J.P., Gangam R., Morris W.J.

Kim J., Zhang V., Abe K., Qin Y., DiRocco D.A., McMullen J.P., Sun A., Gangam R., Chow M., Pitts-McCoy A., Malkani A.S.

Mattern K., Grosser S.T.

Hethcox C., Johnson H.C., Kim J., Wang X., Cheng L., Cao Y., Tan M., DiRocco D.A., Ji Y.

Zwick C.R., Renata H.

Kissman E.N., Neugebauer M.E., Sumida K.H., Swenson C.V., Sambold N.A., Marchand J.A., Millar D.C., Chang M.C.

Biocatalytic C–H activation has the potential to merge enzymatic and synthetic strategies for bond formation. Fe II /αKG-dependent halogenases are particularly distinguished for their ability both to control selective C–H activation as well as to direct group transfer of a bound anion along a reaction axis separate from oxygen rebound, enabling the development of new transformations. In this context, we elucidate the basis for the selectivity of enzymes that perform selective halogenation to yield 4-Cl-lysine (BesD), 5-Cl-lysine (HalB), and 4-Cl-ornithine (HalD), allowing us to probe how site-selectivity and chain length selectivity are achieved. We now report the crystal structure of the HalB and HalD, revealing the key role of the substrate-binding lid in positioning the substrate for C 4 vs C 5 chlorination and recognition of lysine vs ornithine. Targeted engineering of the substrate-binding lid further demonstrates that these selectivities can be altered or switched, showcasing the potential to develop halogenases for biocatalytic applications.

Gomez C.A., Mondal D., Du Q., Chan N., Lewis J.C.

Amatuni A., Shuster A., Abegg D., Adibekian A., Renata H.

Papadopoulou A., Meyer F., Buller R.M.

Fe(II)/α-ketoglutarate-dependent dioxygenases (α-KGDs) are widespread enzymes in aerobic biology and serve a remarkable array of biological functions, including roles in collagen biosynthesis, plant and animal development, transcriptional regulation, nucleic acid modification, and secondary metabolite biosynthesis. This functional diversity is reflected in the enzymes' catalytic flexibility as α-KGDs can catalyze an intriguing set of synthetically valuable reactions, such as hydroxylations, halogenations, and desaturations, capturing the interest of scientists across disciplines. Mechanistically, all α-KGDs are understood to follow a similar activation pathway to generate a substrate radical, yet how individual members of the enzyme family direct this key intermediate toward the different reaction outcomes remains elusive, triggering structural, computational, spectroscopic, kinetic, and enzyme engineering studies. In this Perspective, we will highlight how first enzyme and substrate engineering examples suggest that the chemical reaction pathway within α-KGDs can be intentionally tailored using rational design principles. We will delineate the structural and mechanistic investigations of the reprogrammed enzymes and how they begin to inform about the enzymes' structure-function relationships that determine chemoselectivity. Application of this knowledge in future enzyme and substrate engineering campaigns will lead to the development of powerful C-H activation catalysts for chemical synthesis.

Tao H., Mori T., Chen H., Lyu S., Nonoyama A., Lee S., Abe I.

Non-heme iron and α-ketoglutarate-dependent (Fe/αKG) oxygenases catalyze various oxidative biotransformations. Due to their catalytic flexibility and high efficiency, Fe/αKG oxygenases have attracted keen attention for their application as biocatalysts. Here, we report the biochemical and structural characterizations of the unusually promiscuous and catalytically versatile Fe/αKG oxygenase SptF, involved in the biosynthesis of fungal meroterpenoid emervaridones. The in vitro analysis revealed that SptF catalyzes several continuous oxidation reactions, including hydroxylation, desaturation, epoxidation, and skeletal rearrangement. SptF exhibits extremely broad substrate specificity toward various meroterpenoids, and efficiently produced unique cyclopropane-ring-fused 5/3/5/5/6/6 and 5/3/6/6/6 scaffolds from terretonins. Moreover, SptF also hydroxylates steroids, including androsterone, testosterone, and progesterone, with different regiospecificities. Crystallographic and structure-based mutagenesis studies of SptF revealed the molecular basis of the enzyme reactions, and suggested that the malleability of the loop region contributes to the remarkable substrate promiscuity. SptF exhibits great potential as a promising biocatalyst for oxidation reactions. Non-heme iron and α-ketoglutarate-dependent (Fe/αKG) oxygenases have attracted attention for their application as biocatalysts due to their flexibility and high efficiency. Here, the authors show the biochemical and structural characterizations of the versatile Fe/αKG oxygenase SptF, involved in the biosynthesis of fungal meroterpenoid emervaridones.

Salehi Marzijarani N., Fine A.J., Dalby S.M., Gangam R., Poudyal S., Behre T., Ekkati A.R., Armstrong B.M., Shultz C.S., Dance Z.E., Stone K.

Peng F., Tan L., Chen L., Dalby S.M., DiRocco D.A., Duan J., Feng M., Gong G., Guo H., Hethcox J.C., Jin L., Johnson H.C., Kim J., Le D., Lin Y., et. al.

Jumper J., Evans R., Pritzel A., Green T., Figurnov M., Ronneberger O., Tunyasuvunakool K., Bates R., Žídek A., Potapenko A., Bridgland A., Meyer C., Kohl S.A., Ballard A.J., Cowie A., et. al.

AbstractProteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

Münch J., Püllmann P., Zhang W., Weissenborn M.J.

Meyer F., Frey R., Ligibel M., Sager E., Schroer K., Snajdrova R., Buller R.

Modification of aliphatic C-H bonds in a regio- and stereoselective manner can pose a formidable challenge as these are least reactive in organic chemistry. In this context, the use of non-heme iron and α-ketoglutarate-dependent dioxygenases (αKGDs) represents an interesting complementary tool as this enzyme family can catalyze a broad set of synthetically valuable reactions including hydroxylations, oxidations and desaturations. The consensus reaction mechanism of this enzyme family proceeds via the formation of a Fe(IV)-oxo complex capable of hydrogen atom transfer (HAT) from a sp3- hybridized substrate carbon center. The resulting substrate radical and Fe(III)-OH cofactor is considered to be the branch point toward the possible reaction outcomes which are determined by the enzyme’s active site architecture. To date, the modulation of the reaction fate in Fe/ α-ketoglutarate-dependent oxygenases via enzyme engineering has been mainly elusive. In this study, we therefore targeted to engineer the L-proline cis-4-hydroxylase SmP4H from Sinorhizobium meliloti for selective oxidative modification of the non-proteinogenic amino acid L-homo-phenylalanine (L-hPhe) to produce pharmacological relevant small molecule intermediates. Using structure-guided directed evolution, we improved the turnover number and apparent kcat of the hydroxylation reaction yielding the desired -hydroxylation product by approximately 10-fold and 20-fold, respectively. Notably, the introduction of only one new catalytic entity into the active site (W40Y), allowed us to re-program the natural hydroxylase to predominantly act as a desaturase, presumably through tyrosine’s capability to serve as a catalytic base in the reaction mechanism.

Reynolds E.S., Smith T.G., Rama Damodaran A., Bhagi-Damodaran A.

2-oxoglutarate-dependent non-heme iron hydroxylases offer a direct route to functionalizing C(sp3)–H bonds across a diverse range of substrates, making them prime candidates for chemoenzymatic synthetic strategies. We demonstrate the ability of a non-heme iron L-lysine dioxygenase to perform sequential oxidation and computationally explore structural elements that promote this reactivity.

Wohlgemuth R.

Sustainable processes by completing biocatalytic conversions and recovering products completely.

Vikram A., Mattern K.A., Grosser S.T.

A generalizable active learning framework enables accurate prediction of mass transfer coefficients (kLa), and iterative design of experiments to efficiently characterize new reactor configurations with minimal experimental trials.

Wilson R.H., Diaz D.J., Rama Damodaran A., Bhagi-Damodaran A.

AbstractHighly selective C−H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure‐based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure‐based self‐supervised machine learning framework, MutComputeX, with classical molecular dynamics simulations to down select mutations for rational design of a non‐heme iron‐dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before‐hand. Our rationally designed single mutants purified with up to 2‐fold higher expression yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40 % improvement in the TTN (218±3) as compared to WT LDO (TTN=160±2). Overall, this work offers a low‐barrier approach for those seeking to synergize machine learning algorithms with pre‐existing protein engineering strategies.

Reisenbauer J.C., Sicinski K.M., Arnold F.H.

Biocatalysis has the potential to address the need for more sustainable organic synthesis routes. Protein engineering can tune enzymes to perform in cascade reactions and for efficient synthesis of enantiomerically enriched compounds, using both natural and new-to-nature reaction pathways. This review highlights recent achievements in biocatalysis, especially the development of novel enzymatic syntheses to access versatile small molecule intermediates and complex biomolecules. Biocatalytic strategies for the degradation of persistent pollutants and approaches for biomass valorization are also discussed. The transition of chemical synthesis to a greener future will be accelerated by implementing enzymes and engineering them for high performance and new activities.

Sun C., Zeng R., Chen T., Yang Y., Song Y., Li Q., Cheng J., Liu B.

Hydroxylation reaction is a significant source of structural diversity in natural products (NPs), playing a crucial role in improving the bioactivity, solubility, and stability of natural product molecules. This review summarizes the latest research progress in the field of natural product hydroxylation, focusing on several key hydroxylases involved in the biosynthesis of NPs, including cytochrome P450 monooxygenases, α-ketoglutarate-dependent hydroxylases, and flavin-dependent monooxygenases. These enzymes achieve selective hydroxylation modification of various NPs, such as terpenoids, flavonoids, and steroids, through different catalytic mechanisms. This review systematically summarizes the recent advances on the hydroxylation of NPs, such as amino acids, steroids, terpenoids, lipids, and phenylpropanoids, demonstrating the potential of synthetic biology strategies in constructing artificial biosynthetic pathways and producing hydroxylated natural product derivatives. Through metabolic engineering, enzyme engineering, genetic engineering, and synthetic biology combined with artificial intelligence-assisted technologies, a series of engineered strains have been successfully constructed for the efficient production of hydroxylated NPs and their derivatives, achieving efficient synthesis of hydroxylated NPs. This has provided new avenues for drug development, functional food, and biomaterial production and has also offered new ideas for the industrial production of these compounds. In the future, integrating artificial synthetic pathway design, enzyme directed evolution, dynamic regulation, and artificial intelligence technology is expected to further expand the application of enzyme-catalyzed hydroxylation reactions in the green synthesis of complex NPs, promoting research on natural product hydroxylation to new heights.

King B.R., Sumida K.H., Caruso J.L., Baker D., Zalatan J.

AbstractDeep learning tools for enzyme design are rapidly emerging, and there is a critical need to evaluate their effectiveness in engineering workflows. Here we show that the deep learning‐based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially relevant non‐native functions. This superfamily has diverse catalytic functions and could provide a rich new source of biocatalysts for synthesis and industrial processes. Through systematic comparisons of directed evolution trajectories for a non‐native, remote C(sp3)−H hydroxylation reaction, we demonstrate that the stabilized redesign can be evolved more efficiently than the wild‐type enzyme. After three rounds of directed evolution, we obtained a 6‐fold activity increase from the wild‐type parent and an 80‐fold increase from the stabilized variant. To generate the initial stabilized variant, we identified multiple structural and sequence constraints to preserve catalytic function. We applied these criteria to produce stabilized, catalytically active variants of a second Fe(II)/αKG enzyme, suggesting that the approach is generalizable to additional members of the Fe(II)/αKG superfamily. ProteinMPNN is user‐friendly and widely accessible, and our results provide a framework for the routine implementation of deep learning‐based protein stabilization tools in directed evolution workflows for novel biocatalysts.

King B.R., Sumida K.H., Caruso J.L., Baker D., Zalatan J.

AbstractDeep learning tools for enzyme design are rapidly emerging, and there is a critical need to evaluate their effectiveness in engineering workflows. Here we show that the deep learning‐based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially relevant non‐native functions. This superfamily has diverse catalytic functions and could provide a rich new source of biocatalysts for synthesis and industrial processes. Through systematic comparisons of directed evolution trajectories for a non‐native, remote C(sp3)−H hydroxylation reaction, we demonstrate that the stabilized redesign can be evolved more efficiently than the wild‐type enzyme. After three rounds of directed evolution, we obtained a 6‐fold activity increase from the wild‐type parent and an 80‐fold increase from the stabilized variant. To generate the initial stabilized variant, we identified multiple structural and sequence constraints to preserve catalytic function. We applied these criteria to produce stabilized, catalytically active variants of a second Fe(II)/αKG enzyme, suggesting that the approach is generalizable to additional members of the Fe(II)/αKG superfamily. ProteinMPNN is user‐friendly and widely accessible, and our results provide a framework for the routine implementation of deep learning‐based protein stabilization tools in directed evolution workflows for novel biocatalysts.

Xie C., An N., Zhou L., Shen X., Wang J., Yan Y., Sun X., Yuan Q.

Coumarins are a vast family of natural products with diverse biological activities. Cinnamyl-CoA ortho-hydroxylases (CCHs) catalyze the gateway and rate-limiting steps in coumarin biosynthesis. However, engineering CCHs is challenging due to the large size of the substrates and the vague structure-activity relationships. Herein, directed evolution and structure-guided engineering are performed to engineer a CCH (AtF6'H from Arabidopsis thaliana) using a fluorescence-based screening method, yielding the transplantable surface mutations and substrate-specific pocket mutations with improved activity. Structural analysis and molecular dynamics simulations elucidate the conformational changes that lead to increased catalytic efficiency. Applying appropriate variants with the optimized upstream biosynthetic pathways improves the titer of three simple coumarins by 5-22-fold. Further introducing glycosylation modules results in the production of four coumarin glucosides, among which the titer of aesculin increased by 15.7-fold and reached 3 g/L in scale-up fermentation. This work unleashed the potential of CCHs and established an Escherichia coli platform for coumarins production.

Xu H., Zhao J., Renata H.

AbstractAlpha‐ketoglutarate‐dependent dioxygenases (αKGDs) have recently emerged as useful biocatalysts for C−H oxidation and functionalization. In this work, we characterized a new αKGD from aculene biosynthesis, AneA, which displays broad promiscuity toward a number of substrates with different ring systems. Unexpectedly, AneA was found to be capable of both desaturation and hydroxylation and require an amino ester motif on its substrate for productive catalysis. Insights gathered from the functional characterization and substrate‐activity profiling of AneA enabled the development of a chemoenzymatic strategy toward several complex sesquiterpenoids.

Xu H., Zhao J., Renata H.

AbstractAlpha‐ketoglutarate‐dependent dioxygenases (αKGDs) have recently emerged as useful biocatalysts for C−H oxidation and functionalization. In this work, we characterized a new αKGD from aculene biosynthesis, AneA, which displays broad promiscuity toward a number of substrates with different ring systems. Unexpectedly, AneA was found to be capable of both desaturation and hydroxylation and require an amino ester motif on its substrate for productive catalysis. Insights gathered from the functional characterization and substrate‐activity profiling of AneA enabled the development of a chemoenzymatic strategy toward several complex sesquiterpenoids.

Kumar A., Gill K.S., Upadhyay D., Devliyal S.

Beri M., Gill K.S., Chattopadhyay S., Singh M.

Wenger E.S., Martinie R.J., Ushimaru R., Pollock C.J., Sil D., Li A., Hoang N., Palowitch G.M., Graham B.P., Schaperdoth I., Burke E.J., Maggiolo A.O., Chang W., Allen B.D., Krebs C., et. al.

Shaw M.H., Fryszkowska A., Alvizo O., Attadgie I., Borra-Garske M., Devine P.N., Duan D., Grosser S.T., Forstater J.H., Hughes G.J., Maloney K.M., Margelefsky E.L., Mattern K.A., Miller M.T., Nawrat C.C., et. al.