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

Late-stage modification of bioactive compounds: Improving druggability through efficient molecular editing

Synthetic chemistry plays an indispensable role in drug discovery, contributing to hit compounds identification, lead compounds optimization, candidate drugs preparation, and so on. As Nobel Prize laureate James Black emphasized, "the most fruitful basis for the discovery of a new drug is to start with an old drug"1. Late-stage modification or functionalization of drugs, natural products and bioactive compounds have garnered significant interest due to its ability to introduce diverse elements into bioactive compounds promptly. Such modifications alter the chemical space and physiochemical properties of these compounds, ultimately influencing their potency and druggability. To enrich a toolbox of chemical modification methods for drug discovery, this review focuses on the incorporation of halogen, oxygen, and nitrogen-the ubiquitous elements in pharmacophore components of the marketed drugs-through late-stage modification in recent two decades, and discusses the state and challenges faced in these fields. We also emphasize that increasing cooperation between chemists and pharmacists may be conducive to the rapid discovery of new activities of the functionalized molecules. Ultimately, we hope this review would serve as a valuable resource, facilitating the application of late-stage modification in the construction of novel molecules and inspiring innovative concepts for designing and building new drugs.

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.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

From nature to industry: Harnessing enzymes for biocatalysis

Biocatalysis harnesses enzymes to make valuable products. This green technology is used in countless applications from bench scale to industrial production and allows practitioners to access complex organic molecules, often with fewer synthetic steps and reduced waste. The last decade has seen an explosion in the development of experimental and computational tools to tailor enzymatic properties, equipping enzyme engineers with the ability to create biocatalysts that perform reactions not present in nature. By using (chemo)-enzymatic synthesis routes or orchestrating intricate enzyme cascades, scientists can synthesize elaborate targets ranging from DNA and complex pharmaceuticals to starch made in vitro from CO2-derived methanol. In addition, new chemistries have emerged through the combination of biocatalysis with transition metal catalysis, photocatalysis, and electrocatalysis. This review highlights recent key developments, identifies current limitations, and provides a future prospect for this rapidly developing technology.

Machine Learning-Guided Protein Engineering

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design

Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization, and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This Perspective aims to showcase the current status of applications in pharmaceutical industry and also to discuss and celebrate the innovative approaches in protein science by highlighting their potential in selected recent developments and offering thoughts on future opportunities for biocatalysis. It also critically assesses the technology's limitations, unanswered questions, and unmet challenges.

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

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.

Non-Native Site-Selective Enzyme Catalysis

The ability to site-selectively modify equivalent functional groups in a molecule has the potential to streamline syntheses and increase product yields by lowering step counts. Enzymes catalyze site-selective transformations throughout primary and secondary metabolism, but leveraging this capability for non-native substrates and reactions requires a detailed understanding of the potential and limitations of enzyme catalysis and how these bounds can be extended by protein engineering. In this review, we discuss representative examples of site-selective enzyme catalysis involving functional group manipulation and C-H bond functionalization. We include illustrative examples of native catalysis, but our focus is on cases involving non-native substrates and reactions often using engineered enzymes. We then discuss the use of these enzymes for chemoenzymatic transformations and target-oriented synthesis and conclude with a survey of tools and techniques that could expand the scope of non-native site-selective enzyme catalysis.

Late-stage Functionalization for Improving Drug-like Molecular Properties

The development of late-stage functionalization (LSF) methodologies, particularly C-H functionalization, has revolutionized the field of organic synthesis. Over the past decade, medicinal chemists have begun to implement LSF strategies into their drug discovery programs, allowing for the drug discovery process to become more efficient. Most reported applications of late-stage C-H functionalization of drugs and drug-like molecules have been to rapidly diversify screening libraries to explore structure-activity relationships. However, there has been a growing trend toward the use of LSF methodologies as an efficient tool for improving drug-like molecular properties of promising drug candidates. In this review, we have comprehensively reviewed recent progress in this emerging area. Particular emphasis is placed on case studies where multiple LSF techniques were implemented to generate a library of novel analogues with improved drug-like properties. We have critically analyzed the current scope of LSF strategies to improve drug-like properties and commented on how we believe LSF can transform drug discovery in the future. Overall, we aim to provide a comprehensive survey of LSF techniques as tools for efficiently improving drug-like molecular properties, anticipating its continued uptake in drug discovery programs.

An update on late-stage functionalization in today's drug discovery

INTRODUCTION

Late-stage functionalization (LSF) allows for the introduction of new chemical groups toward the end of a synthetic sequence, which means new molecules can be rapidly accessed without laborious de novo chemical synthesis. Over the last decade, medicinal chemists have begun to implement LSF strategies into their drug discovery programs, affording benefits such as efficient access to diverse libraries to explore structure-activity relationships and the improvement of physicochemical and pharmacokinetic properties.

AREAS COVERED

An overview of the key advancements in LSF methodology development from 2019 to 2022 and their applicability to drug discovery is provided. In addition, several examples from both academia and industry where LSF methodologies have been applied by medicinal chemists to their drug discovery programs are presented.

EXPERT OPINION

Utilization of LSF by medicinal chemists is on the rise, both in academia and in industry. The maturation of the LSF field to produce methodologies bearing increased regioselectivity, scope, and functional group tolerance is envisaged to narrow the gap between methodology development and medicinal chemistry research. The authors predict that the sheer versatility of these techniques in facilitating challenging chemical transformations of bioactive molecules will continue to increase the efficiency of the drug discovery process.

Directed Evolution of an Iron(II)- and α-Ketoglutarate-Dependent Dioxygenase for Site-Selective Azidation of Unactivated Aliphatic C-H Bonds

FeII - and α-ketoglutarate-dependent halogenases and oxygenases can catalyze site-selective functionalization of C-H bonds via a variety of C-X bond forming reactions, but achieving high chemoselectivity for functionalization using non-native functional groups remains rare. The current study shows that directed evolution can be used to engineer variants of the dioxygenase SadX that address this challenge. Site-selective azidation of succinylated amino acids and a succinylated amine was achieved as a result of mutations throughout the SadX structure. The installed azide group was reduced to a primary amine, and the succinyl group required for azidation was enzymatically cleaved to provide the corresponding amine. These results provide a promising starting point for evolving additional SadX variants with activity on structurally distinct substrates and for enabling enzymatic C-H functionalization with other non-native functional groups.

Biocatalytic control of site-selectivity and chain length-selectivity in radical amino acid halogenases

Biocatalytic C-H activation has the potential to merge enzymatic and synthetic strategies for bond formation. FeII/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 C4 vs C5 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.

Engineering Fe(II)/α-Ketoglutarate-Dependent Halogenases and Desaturases

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.

Non-Native Anionic Ligand Binding and Reactivity in Engineered Variants of the Fe(II)- and α-Ketoglutarate-Dependent Oxygenase, SadA

Mononuclear non-heme Fe(II)- and α-ketoglutarate-dependent oxygenases (FeDOs) catalyze a site-selective C-H hydroxylation. Variants of these enzymes in which a conserved Asp/Glu residue in the Fe(II)-binding facial triad is replaced by Ala/Gly can, in some cases, bind various anionic ligands and catalyze non-native chlorination and bromination reactions. In this study, we explore the binding of different anions to an FeDO facial triad variant, SadX, and the effects of that binding on HO• vs X• rebound. We establish not only that chloride and bromide enable non-native halogenation reactions but also that all anions investigated, including azide, cyanate, formate, and fluoride, significantly accelerate and influence the site selectivity of SadX hydroxylation catalysis. Azide and cyanate also lead to the formation of products resulting from N3•, NCO•, and OCN• rebound. While fluoride rebound is not observed, the rate acceleration provided by this ligand leads us to calculate barriers for HO• and F• rebound from a putative Fe(III)(OH)(F) intermediate. These calculations suggest that the lack of fluorination is due to the relative barriers of the HO• and F• rebound transition states rather than an inaccessible barrier for F• rebound. Together, these results improve our understanding of the FeDO facial triad variant tolerance of different anionic ligands, their ability to promote rebound involving these ligands, and inherent rebound preferences relative to HO• that will aid efforts to develop non-native catalysis using these enzymes.

Redesigning Enzymes for Biocatalysis: Exploiting Structural Understanding for Improved Selectivity

The discovery of new enzymes, alongside the push to make chemical processes more sustainable, has resulted in increased industrial interest in the use of biocatalytic processes to produce high-value and chiral precursor chemicals. Huge strides in protein engineering methodology and in silico tools have facilitated significant progress in the discovery and production of enzymes for biocatalytic processes. However, there are significant gaps in our knowledge of the relationship between enzyme structure and function. This has demonstrated the need for improved computational methods to model mechanisms and understand structure dynamics. Here, we explore efforts to rationally modify enzymes toward changing aspects of their catalyzed chemistry. We highlight examples of enzymes where links between enzyme function and structure have been made, thus enabling rational changes to the enzyme structure to give predictable chemical outcomes. We look at future directions the field could take and the technologies that will enable it.

Recent Advances in Biocatalysis for Drug Synthesis

Biocatalysis is constantly providing novel options for the synthesis of active pharmaceutical ingredients (APIs). In addition to drug development and manufacturing, biocatalysis also plays a role in drug discovery and can support many active ingredient syntheses at an early stage to build up entire scaffolds in a targeted and preparative manner. Recent progress in recruiting new enzymes by genome mining and screening or adapting their substrate, as well as product scope, by protein engineering has made biocatalysts a competitive tool applied in academic and industrial spheres. This is especially true for the advances in the field of nonribosomal peptide synthesis and enzyme cascades that are expanding the capabilities for the discovery and synthesis of new bioactive compounds via biotransformation. Here we highlight some of the most recent developments to add to the portfolio of biocatalysis with special relevance for the synthesis and late-stage functionalization of APIs, in order to bypass pure chemical processes.

Making Enzymes Suitable for Organic Chemistry by Rational Protein Design

This review outlines recent developments in protein engineering of stereo‐ and regioselective enzymes, which are of prime interest in organic and pharmaceutical chemistry as well as biotechnology. The widespread application of enzymes was hampered for decades due to limited enantio‐, diastereo‐ and regioselectivity, which was the reason why most organic chemists were not interested in biocatalysis. This attitude began to change with the advent of semi‐rational directed evolution methods based on focused saturation mutagenesis at sites lining the binding pocket. Screening constitutes the labor‐intensive step (bottleneck), which is the reason why various research groups are continuing to develop techniques for the generation of small and smart mutant libraries. Rational enzyme design, traditionally an alternative to directed evolution, provides small collections of mutants which require minimal screening. This approach first focused on thermostabilization, and did not enter the field of stereoselectivity until later. Computational guides such as the Rosetta algorithms, HotSpot Wizard metric, and machine learning (ML) contribute significantly to decision making. The newest advancements show that semi‐rational directed evolution such as CAST/ISM and rational enzyme design no longer develop on separate tracks, instead, they have started to merge. Indeed, researchers utilizing the two approaches have learned from each other. Today, the toolbox of organic chemists includes enzymes, primarily because the possibility of controlling stereoselectivity by protein engineering has ensured reliability when facing synthetic challenges. This review was also written with the hope that undergraduate and graduate education will include enzymes more so than in the past.