Evolution of Biocatalysis at Novartis over the last 40 Years

The fortieth anniversary of biocatalysis started at Ciba-Geigy and later at Novartis is a great time to pause and reflect on development of science and technology in this field. Enzyme-based synthesis became a highly valued enabling tool for pharmaceutical research and development over the last decades. In this perspective we aim to discuss how the scientific approaches and trends evolved over the time and present future challenges and opportunities

Enzymatic Bioconjugation: A Perspective from the Pharmaceutical Industry.

Enzymes have firmly established themselves as bespoke catalysts for small molecule transformations in the pharmaceutical industry, from early research and development stages to large-scale production. In principle, their exquisite selectivity and rate acceleration can also be leveraged for modifying macromolecules to form bioconjugates. However, available catalysts face stiff competition from other bioorthogonal chemistries. In this Perspective, we seek to illuminate applications of enzymatic bioconjugation in the face of an expanding palette of new drug modalities. With these applications, we wish to highlight some examples of current successes and pitfalls of using enzymes for bioconjugation along the pipeline and try to illustrate opportunities for further development

Intensified biocatalytic production of enantiomerically pure halophenylalanines from acrylic acids using ammonium carbamate as the ammonia source

An industrial-scale method employing a phenylalanine ammonia lyase enzyme.</p

MOESM1 of Exploring productive sequence space in directed evolution using binary patterning versus conventional mutagenesis strategies

Additional file 1. Additional figures and tables including: Table S1. Best LEH variants of library CB and AD for desymmetrization of epoxide 1. Table S2. Distances [in Å] and angles [degrees] calculated for the highest ranked docking poses calculated for cyclohexene oxide in the WT, SZ502 and SZ503 homology models of LEH. Table S3. List of primers used in this study. Table S4. Analytic conditions of GC. Figure S1. Highest ranked docking pose for cyclohexene oxide (1) in the (a) SZ502 and (b) SZ503 mutant homology models of LEH. Figure S2. Docking of substrate 1 to WT LEH

Understanding and Overcoming the Limitations of Bacillus badius and Caldalkalibacillus thermarum Amine Dehydrogenases for Biocatalytic Reductive Amination

The direct asymmetric reductive amination of ketones using ammonia as the sole amino donor is a growing field of research in both chemocatalysis and biocatalysis. Recent research has focused on the enzyme engineering of amino acid dehydrogenases (to obtain amine dehydrogenases), and this technology promises to be a potentially exploitable route for chiral amine synthesis. However, the use of these enzymes in industrial biocatalysis has not yet been demonstrated with substrate loadings above 80 mM, because of the enzymes’ generally low turnover numbers (<i>k</i>_cat < 0.1 s^–1) and variable stability under reaction conditions. In this work, a newly engineered amine dehydrogenase from a phenylalanine dehydrogenase (PheDH) from Caldalkalibacillus thermarum was recruited and compared against an existing amine dehydrogenase (AmDH) from Bacillus badius for both kinetic and thermostability parameters, with the former exhibiting an increased thermostability (melting temperature, <i>T</i>_m) of 83.5 °C, compared to 56.5 °C for the latter. The recruited enzyme was further used in the reductive amination of up to 400 mM of phenoxy-2-propanone (<i>c</i> = 96%, ee (<i>R</i>) < 99%) in a biphasic reaction system utilizing a lyophilized whole-cell preparation. Finally, we performed computational docking simulations to rationalize the generally lower turnover numbers of AmDHs, compared to their PheDH counterparts

Accelerating Biocatalysis Discovery with Machine Learning: A New Era in Enzyme Engineering new title based on editors feedback: Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design

New emerging computational tools promise to revolutionize protein engineering for biocatalysis application 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 to 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 discuss and also to celebrate these innovative new approaches in protein science by highlighting their potential on selected recent developments and applications and offering thoughts on future opportunities. It also critically assesses the technology’s limitations, unanswered questions and unmet challenges

Simultaneous machine-directed evolution of an imine reductase for activity and stereoselectivity

Biocatalysis is an effective tool to access small quantities of chiral molecules that are otherwise hard to synthesize or purify. A time-efficient process is needed to develop an enzyme that is adequate to perform desired chemistry. We evaluated machine-directed evolution as an enzyme engineering strategy, using a stereoselective imine reductase as the model system. Within one cycle, it was found that machine-directed evolution yielded a library of high activity mutants with a dramatically shifted activity distribution, compared to traditional directed evolution. Structure-guided analysis revealed that linear additivity may provide a simple explanation for the effectiveness of machine-directed evolution. The study concludes with a cost-benefit analysis showing that machine-directed evolution gives a good return on investment in high cost per measurement regimes