1
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Gantz M, Mathis SV, Nintzel FEH, Lio P, Hollfelder F. On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering. Faraday Discuss 2024; 252:89-114. [PMID: 39133073 PMCID: PMC11318516 DOI: 10.1039/d4fd00065j] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 04/23/2024] [Indexed: 08/13/2024]
Abstract
Protein design and directed evolution have separately contributed enormously to protein engineering. Without being mutually exclusive, the former relies on computation from first principles, while the latter is a combinatorial approach based on chance. Advances in ultrahigh throughput (uHT) screening, next generation sequencing and machine learning may create alternative routes to engineered proteins, where functional information linked to specific sequences is interpreted and extrapolated in silico. In particular, the miniaturisation of functional tests in water-in-oil emulsion droplets with picoliter volumes and their rapid generation and analysis (>1 kHz) allows screening of >107-membered libraries in a day. Subsequently, decoding the selected clones by short or long-read sequencing methods leads to large sequence-function datasets that may allow extrapolation from experimental directed evolution to further improved mutants beyond the observed hits. In this work, we explore experimental strategies for how to draw up 'fitness landscapes' in sequence space with uHT droplet microfluidics, review the current state of AI/ML in enzyme engineering and discuss how uHT datasets may be combined with AI/ML to make meaningful predictions and accelerate biocatalyst engineering.
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Affiliation(s)
- Maximilian Gantz
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Simon V Mathis
- Department of Computer Science, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK
| | - Friederike E H Nintzel
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Pietro Lio
- Department of Computer Science, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK
| | - Florian Hollfelder
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
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2
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Hollmann F, Sanchis J, Reetz MT. Learning from Protein Engineering by Deconvolution of Multi-Mutational Variants. Angew Chem Int Ed Engl 2024; 63:e202404880. [PMID: 38884594 DOI: 10.1002/anie.202404880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 06/18/2024]
Abstract
This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. Following the establishment of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was introduced. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. During the past decade, this phenomenon was shown to be general. In some studies, molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non-additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi-dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so-called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non-additive effects. We conclude with suggestions for future work, and call for a unified overall picture of non-additivity and epistasis.
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Affiliation(s)
- Frank Hollmann
- Department of Biotechnology, Delft University of Technology, Van der Maasweg 9, 2629HZ, Delft, Netherlands
| | - Joaquin Sanchis
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
| | - Manfred T Reetz
- Max-Plank-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45481, Mülheim, Germany
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
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3
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Vornholt T, Mutný M, Schmidt GW, Schellhaas C, Tachibana R, Panke S, Ward TR, Krause A, Jeschek M. Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning. ACS CENTRAL SCIENCE 2024; 10:1357-1370. [PMID: 39071060 PMCID: PMC11273458 DOI: 10.1021/acscentsci.4c00258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/22/2024] [Accepted: 05/02/2024] [Indexed: 07/30/2024]
Abstract
Tailored enzymes are crucial for the transition to a sustainable bioeconomy. However, enzyme engineering is laborious and failure-prone due to its reliance on serendipity. The efficiency and success rates of engineering campaigns may be improved by applying machine learning to map the sequence-activity landscape based on small experimental data sets. Yet, it often proves challenging to reliably model large sequence spaces while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning, which we applied to engineer an artificial metalloenzyme (ArM) catalyzing a new-to-nature hydroamination reaction. Combining lab automation and next-generation sequencing, we acquired sequence-activity data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds. Critical characteristics of our pipeline include the cost-effective generation of information-rich data sets, the integration of an explorative round to improve the model's performance, and the inclusion of experimental noise. Our approach led to an order-of-magnitude boost in the hit rate while making efficient use of experimental resources. Search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts.
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Affiliation(s)
- Tobias Vornholt
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
- National
Centre of Competence in Research (NCCR) Molecular Systems Engineering, 4056 Basel,Switzerland
| | - Mojmír Mutný
- Department
of Computer Science, ETH Zurich, Andreasstrasse 5, 8092 Zurich, Switzerland
| | - Gregor W. Schmidt
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Christian Schellhaas
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Ryo Tachibana
- Department
of Chemistry, University of Basel, Mattenstrasse 24a, 4058 Basel, Switzerland
| | - Sven Panke
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
- National
Centre of Competence in Research (NCCR) Molecular Systems Engineering, 4056 Basel,Switzerland
| | - Thomas R. Ward
- National
Centre of Competence in Research (NCCR) Molecular Systems Engineering, 4056 Basel,Switzerland
- Department
of Chemistry, University of Basel, Mattenstrasse 24a, 4058 Basel, Switzerland
| | - Andreas Krause
- Department
of Computer Science, ETH Zurich, Andreasstrasse 5, 8092 Zurich, Switzerland
| | - Markus Jeschek
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
- Institute
of Microbiology, University of Regensburg, Universitätsstraße 31, 93053 Regensburg, Germany
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4
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Mosquera J, Bismuto A. Highlights from the 57th Bürgenstock Conference on Stereochemistry 2024. Chem Sci 2024; 15:9392-9396. [PMID: 38939160 PMCID: PMC11205270 DOI: 10.1039/d4sc90102a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
Herein, we share an overview of the scientific highlights from speakers at the latest edition of the longstanding Bürgenstock Conference.
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Affiliation(s)
- Jesús Mosquera
- Universidade da Coruña, CICA - Centro Interdisciplinar de Química e Bioloxía Rúa as Carballeiras 15071 A Coruña Spain
| | - Alessandro Bismuto
- Institute of Inorganic Chemistry, University of Bonn Gerhard-Domagk-Str. 1 53121 Bonn Germany
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5
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Ni J, Zhuang J, Shi Y, Chiang YC, Cheng GJ. Discovery and substrate specificity engineering of nucleotide halogenases. Nat Commun 2024; 15:5254. [PMID: 38898020 PMCID: PMC11186838 DOI: 10.1038/s41467-024-49147-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
C2'-halogenation has been recognized as an essential modification to enhance the drug-like properties of nucleotide analogs. The direct C2'-halogenation of the nucleotide 2'-deoxyadenosine-5'-monophosphate (dAMP) has recently been achieved using the Fe(II)/α-ketoglutarate-dependent nucleotide halogenase AdaV. However, the limited substrate scope of this enzyme hampers its broader applications. In this study, we report two halogenases capable of halogenating 2'-deoxyguanosine monophosphate (dGMP), thereby expanding the family of nucleotide halogenases. Computational studies reveal that nucleotide specificity is regulated by the binding pose of the phosphate group. Based on these findings, we successfully engineered the substrate specificity of these halogenases by mutating second-sphere residues. This work expands the toolbox of nucleotide halogenases and provides insights into the regulation mechanism of nucleotide specificity.
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Affiliation(s)
- Jie Ni
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, Guangdong, China
| | - Jingyuan Zhuang
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, Guangdong, China
| | - Yiming Shi
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, Guangdong, China
| | - Ying-Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, Guangdong, China
| | - Gui-Juan Cheng
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, Guangdong, China.
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6
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Huo T, Zhao X, Cheng Z, Wei J, Zhu M, Dou X, Jiao N. Late-stage modification of bioactive compounds: Improving druggability through efficient molecular editing. Acta Pharm Sin B 2024; 14:1030-1076. [PMID: 38487004 PMCID: PMC10935128 DOI: 10.1016/j.apsb.2023.11.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/14/2023] [Accepted: 11/13/2023] [Indexed: 03/17/2024] Open
Abstract
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.
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Affiliation(s)
- Tongyu Huo
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Xinyi Zhao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Zengrui Cheng
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Jialiang Wei
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
- Changping Laboratory, Beijing 102206, China
| | - Minghui Zhu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Xiaodong Dou
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Ning Jiao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
- Changping Laboratory, Beijing 102206, China
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, East China Normal University, Shanghai 200062, China
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7
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Honda Malca S, Duss N, Meierhofer J, Patsch D, Niklaus M, Reiter S, Hanlon SP, Wetzl D, Kuhn B, Iding H, Buller R. Effective engineering of a ketoreductase for the biocatalytic synthesis of an ipatasertib precursor. Commun Chem 2024; 7:46. [PMID: 38418529 PMCID: PMC10902378 DOI: 10.1038/s42004-024-01130-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/15/2024] [Indexed: 03/01/2024] Open
Abstract
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.
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Affiliation(s)
- Sumire Honda Malca
- Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
| | - Nadine Duss
- Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
| | - Jasmin Meierhofer
- Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
- Analytical Research and Development, MSD Werthenstein BioPharma GmbH, Industrie Nord 1, 6105 Schachen, Switzerland
| | - David Patsch
- Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
| | - Michael Niklaus
- Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
| | - Stefanie Reiter
- Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
- Manufacturing Science and Technology, Fisher Clinical Services GmbH, Biotech Innovation Park, 2543 Lengnau, Switzerland
| | - Steven Paul Hanlon
- Process Chemistry and Catalysis, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Dennis Wetzl
- Process Chemistry and Catalysis, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
- Nonclinical Drug Development, Boehringer Ingelheim International GmbH, Birkendorfer Strasse 65, 88397 Biberach an der Riss, Germany
| | - Bernd Kuhn
- Pharmaceutical Research and Early Development, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Hans Iding
- Process Chemistry and Catalysis, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Rebecca Buller
- Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland.
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8
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Ao YF, Dörr M, Menke MJ, Born S, Heuson E, Bornscheuer UT. Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity. Chembiochem 2024; 25:e202300754. [PMID: 38029350 DOI: 10.1002/cbic.202300754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/01/2023]
Abstract
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.
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Affiliation(s)
- Yu-Fei Ao
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Zhongguancun North First Street 2, Beijing, 100190, China
- University of Chinese Academy of Sciences, Yuquan Road 19(A), Beijing, 100049, China
| | - Mark Dörr
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
| | - Marian J Menke
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
| | - Stefan Born
- Technische Universität Berlin, Chair of Bioprocess Engineering, Ackerstraße 76, 13355, Berlin, Germany
| | - Egon Heuson
- Univ. Lille, CNRS, Centrale Lille, Univ. Artois, UMR 8181 UCCS, Unité de Catalyse et Chimie du Solide, 59000, Lille, France
| | - Uwe T Bornscheuer
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
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9
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Buller R, Lutz S, Kazlauskas RJ, Snajdrova R, Moore JC, Bornscheuer UT. From nature to industry: Harnessing enzymes for biocatalysis. Science 2023; 382:eadh8615. [PMID: 37995253 DOI: 10.1126/science.adh8615] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/17/2023] [Indexed: 11/25/2023]
Abstract
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.
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Affiliation(s)
- R Buller
- Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, 8820 Wädenswil, Switzerland
| | - S Lutz
- Codexis Incorporated, Redwood City, CA 94063, USA
| | - R J Kazlauskas
- Department of Biochemistry, Molecular Biology and Biophysics, Biotechnology Institute, University of Minnesota, Saint Paul, MN 55108, USA
| | - R Snajdrova
- Novartis Institutes for BioMedical Research, Global Discovery Chemistry, 4056 Basel, Switzerland
| | - J C Moore
- MRL, Merck & Co., Rahway, NJ 07065, USA
| | - U T Bornscheuer
- Institute of Biochemistry, Dept. of Biotechnology and Enzyme Catalysis, Greifswald University, Greifswald, Germany
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10
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Kouba P, Kohout P, Haddadi F, Bushuiev A, Samusevich R, Sedlar J, Damborsky J, Pluskal T, Sivic J, Mazurenko S. Machine Learning-Guided Protein Engineering. ACS Catal 2023; 13:13863-13895. [PMID: 37942269 PMCID: PMC10629210 DOI: 10.1021/acscatal.3c02743] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/20/2023] [Indexed: 11/10/2023]
Abstract
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.
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Affiliation(s)
- Petr Kouba
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Faculty of
Electrical Engineering, Czech Technical
University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic
| | - Pavel Kohout
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Faraneh Haddadi
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Anton Bushuiev
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Raman Samusevich
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Jiri Sedlar
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Jiri Damborsky
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Tomas Pluskal
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Josef Sivic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Stanislav Mazurenko
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
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11
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Markus B, C GC, Andreas K, Arkadij K, Stefan L, Gustav O, Elina S, Radka S. Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design. ACS Catal 2023; 13:14454-14469. [PMID: 37942268 PMCID: PMC10629211 DOI: 10.1021/acscatal.3c03417] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023]
Abstract
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.
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Affiliation(s)
- Braun Markus
- Department
of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010 Graz, Austria
| | - Gruber Christian C
- Enzyme
and Drug Discovery, Innophore. 1700 Montgomery Street, San Francisco, California 94111, United States
| | - Krassnigg Andreas
- Enzyme
and Drug Discovery, Innophore. 1700 Montgomery Street, San Francisco, California 94111, United States
| | - Kummer Arkadij
- Moderna,
Inc., 200 Technology
Square, Cambridge, Massachusetts 02139, United States
| | - Lutz Stefan
- Codexis
Inc., 200 Penobscot Drive, Redwood City, California 94063, United States
| | - Oberdorfer Gustav
- Department
of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010 Graz, Austria
| | - Siirola Elina
- Novartis
Institute for Biomedical Research, Global Discovery Chemistry, Basel CH-4108, Switzerland
| | - Snajdrova Radka
- Novartis
Institute for Biomedical Research, Global Discovery Chemistry, Basel CH-4108, Switzerland
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12
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Ge F, Chen G, Qian M, Xu C, Liu J, Cao J, Li X, Hu D, Xu Y, Xin Y, Wang D, Zhou J, Shi H, Tan Z. Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:14911-14930. [PMID: 37800676 DOI: 10.1021/acs.jafc.3c05029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
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.
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Affiliation(s)
- Feiyin Ge
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Gang Chen
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Minjing Qian
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Cheng Xu
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Jiao Liu
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Jiaqi Cao
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Xinchao Li
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Die Hu
- School of Pharmacy & School of Biological and Food Engineering, Changzhou University, Changzhou 213164, People's Republic of China
| | - Yangsen Xu
- Dongtai Hanfangyuan Biotechnology Co. Ltd., Yancheng 224241, People's Republic of China
| | - Ya Xin
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Dianlong Wang
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Jia Zhou
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Hao Shi
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
| | - Zhongbiao Tan
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an 223003, People's Republic of China
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13
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Patsch D, Eichenberger M, Voss M, Bornscheuer UT, Buller RM. LibGENiE - A bioinformatic pipeline for the design of information-enriched enzyme libraries. Comput Struct Biotechnol J 2023; 21:4488-4496. [PMID: 37736300 PMCID: PMC10510078 DOI: 10.1016/j.csbj.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 09/23/2023] Open
Abstract
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.
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Affiliation(s)
- David Patsch
- Zurich University of Applied Sciences, School of Life Sciences and Facility Management, Institute of Chemistry and Biotechnology, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
- Institute of Biochemistry, Department of Biotechnology & Enzyme Catalysis, Greifswald University, Felix-Hausdorff-Strasse 4, 17487 Greifswald, Germany
| | - Michael Eichenberger
- Zurich University of Applied Sciences, School of Life Sciences and Facility Management, Institute of Chemistry and Biotechnology, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
| | - Moritz Voss
- Zurich University of Applied Sciences, School of Life Sciences and Facility Management, Institute of Chemistry and Biotechnology, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
| | - Uwe T. Bornscheuer
- Institute of Biochemistry, Department of Biotechnology & Enzyme Catalysis, Greifswald University, Felix-Hausdorff-Strasse 4, 17487 Greifswald, Germany
| | - Rebecca M. Buller
- Zurich University of Applied Sciences, School of Life Sciences and Facility Management, Institute of Chemistry and Biotechnology, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
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14
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Abstract
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.
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Affiliation(s)
- Dibyendu Mondal
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States
| | - Harrison M Snodgrass
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States
| | - Christian A Gomez
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States
| | - Jared C Lewis
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States
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15
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Castellino NJ, Montgomery AP, Danon JJ, Kassiou M. Late-stage Functionalization for Improving Drug-like Molecular Properties. Chem Rev 2023. [PMID: 37285604 DOI: 10.1021/acs.chemrev.2c00797] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
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.
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Affiliation(s)
| | | | - Jonathan J Danon
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | - Michael Kassiou
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
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16
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Montgomery AP, Joyce JM, Danon JJ, Kassiou M. An update on late-stage functionalization in today's drug discovery. Expert Opin Drug Discov 2023; 18:597-613. [PMID: 37114995 DOI: 10.1080/17460441.2023.2205635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
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.
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Affiliation(s)
| | - Jack M Joyce
- School of Chemistry, The University of Sydney, Sydney, Australia
| | - Jonathan J Danon
- School of Chemistry, The University of Sydney, Sydney, Australia
| | - Michael Kassiou
- School of Chemistry, The University of Sydney, Sydney, Australia
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17
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Gomez CA, Mondal D, Du Q, Chan N, Lewis JC. Directed Evolution of an Iron(II)- and α-Ketoglutarate-Dependent Dioxygenase for Site-Selective Azidation of Unactivated Aliphatic C-H Bonds. Angew Chem Int Ed Engl 2023; 62:e202301370. [PMID: 36757808 PMCID: PMC10050089 DOI: 10.1002/anie.202301370] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 02/10/2023]
Abstract
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.
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Affiliation(s)
- Christian A Gomez
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
| | - Dibyendu Mondal
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
- Kalsec Inc., 3713W. Main St., Kalamazoo, MI 49006, USA
| | - Qian Du
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
| | - Natalie Chan
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
| | - Jared C Lewis
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
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18
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Kissman EN, Neugebauer ME, Sumida KH, Swenson CV, Sambold NA, Marchand JA, Millar DC, Chang MCY. Biocatalytic control of site-selectivity and chain length-selectivity in radical amino acid halogenases. Proc Natl Acad Sci U S A 2023; 120:e2214512120. [PMID: 36913566 PMCID: PMC10041140 DOI: 10.1073/pnas.2214512120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 02/14/2023] [Indexed: 03/14/2023] Open
Abstract
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.
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Affiliation(s)
- Elijah N. Kissman
- Department of Chemistry, University of California, Berkeley, CA94720
| | - Monica E. Neugebauer
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA94720
| | - Kiera H. Sumida
- Department of Chemistry, University of California, Berkeley, CA94720
| | | | - Nicholas A. Sambold
- Department of Molecular and Cell Biology, University of California, Berkeley, CA94720
| | - Jorge A. Marchand
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA94720
| | - Douglas C. Millar
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA94720
| | - Michelle C. Y. Chang
- Department of Chemistry, University of California, Berkeley, CA94720
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA94720
- Department of Molecular and Cell Biology, University of California, Berkeley, CA94720
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19
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Kastner DW, Nandy A, Mehmood R, Kulik HJ. Mechanistic Insights into Substrate Positioning That Distinguish Non-heme Fe(II)/α-Ketoglutarate-Dependent Halogenases and Hydroxylases. ACS Catal 2023. [DOI: 10.1021/acscatal.2c06241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- David W. Kastner
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Rimsha Mehmood
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heather J. Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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20
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Papadopoulou A, Meyer F, Buller RM. Engineering Fe(II)/α-Ketoglutarate-Dependent Halogenases and Desaturases. Biochemistry 2023; 62:229-240. [PMID: 35446547 DOI: 10.1021/acs.biochem.2c00115] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
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.
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Affiliation(s)
- Athena Papadopoulou
- Competence Center for Biocatalysis, Zurich University of Applied Sciences, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
| | - Fabian Meyer
- Competence Center for Biocatalysis, Zurich University of Applied Sciences, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
| | - Rebecca M Buller
- Competence Center for Biocatalysis, Zurich University of Applied Sciences, Einsiedlerstrasse 31, 8820 Wädenswil, Switzerland
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21
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Büchler J, Hegarty E, Schroer K, Snajdrova R, Turner NJ, Loiseleur O, Buller R, Le Chapelain C. A Collaborative Journey towards the Late‐Stage Functionalization of Added‐Value Chemicals Using Engineered Halogenases. Helv Chim Acta 2022. [DOI: 10.1002/hlca.202200128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Affiliation(s)
- Johannes Büchler
- Zurich University of Applied Sciences School of Life Sciences and Facility Management Institute of Chemistry and Biotechnology, CH- 8820 Wädenswil Switzerland
- Department of Chemistry The University of Manchester Manchester Institute of Biotechnology, UK- Manchester M1 7DN United Kingdom
| | - Eimear Hegarty
- Zurich University of Applied Sciences School of Life Sciences and Facility Management Institute of Chemistry and Biotechnology, CH- 8820 Wädenswil Switzerland
| | - Kirsten Schroer
- Novartis Institutes for BioMedical Research Global Discovery Chemistry, CH- 4056 Basel Switzerland
| | - Radka Snajdrova
- Novartis Institutes for BioMedical Research Global Discovery Chemistry, CH- 4056 Basel Switzerland
| | - Nicholas J. Turner
- Department of Chemistry The University of Manchester Manchester Institute of Biotechnology, UK- Manchester M1 7DN United Kingdom
| | - Olivier Loiseleur
- Syngenta Crop Protection AG Schaffhauserstr. 101 CH-4332 Stein Switzerland
| | - Rebecca Buller
- Zurich University of Applied Sciences School of Life Sciences and Facility Management Institute of Chemistry and Biotechnology, CH- 8820 Wädenswil Switzerland
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22
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Chan NH, Gomez CA, Vennelakanti V, Du Q, Kulik HJ, Lewis JC. Non-Native Anionic Ligand Binding and Reactivity in Engineered Variants of the Fe(II)- and α-Ketoglutarate-Dependent Oxygenase, SadA. Inorg Chem 2022; 61:14477-14485. [PMID: 36044713 PMCID: PMC9789792 DOI: 10.1021/acs.inorgchem.2c02872] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
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.
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Affiliation(s)
- Natalie H. Chan
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA
| | - Christian A. Gomez
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA
| | - Vyshnavi Vennelakanti
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Qian Du
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA
| | - Heather J. Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Jared C. Lewis
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA
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23
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Wilson RH, Chatterjee S, Smithwick ER, Dalluge JJ, Bhagi-Damodaran A. Role of Secondary Coordination Sphere Residues in Halogenation Catalysis of Non-heme Iron Enzymes. ACS Catal 2022. [DOI: 10.1021/acscatal.2c00954] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- R. Hunter Wilson
- Department of Chemistry, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Sourav Chatterjee
- Department of Chemistry, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Elizabeth R. Smithwick
- Department of Chemistry, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Joseph J. Dalluge
- Department of Chemistry, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Ambika Bhagi-Damodaran
- Department of Chemistry, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, United States
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24
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Ding Y, Perez-Ortiz G, Peate J, Barry SM. Redesigning Enzymes for Biocatalysis: Exploiting Structural Understanding for Improved Selectivity. Front Mol Biosci 2022; 9:908285. [PMID: 35936784 PMCID: PMC9355150 DOI: 10.3389/fmolb.2022.908285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/08/2022] [Indexed: 11/13/2022] Open
Abstract
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.
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25
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Reetz M. Making Enzymes Suitable for Organic Chemistry by Rational Protein Design. Chembiochem 2022; 23:e202200049. [PMID: 35389556 PMCID: PMC9401064 DOI: 10.1002/cbic.202200049] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 04/07/2022] [Indexed: 11/25/2022]
Abstract
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.
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Affiliation(s)
- Manfred Reetz
- Max-Planck-Institut fur KohlenforschungMülheim an der RuhrGermany
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26
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Wang F, Nishimoto Y, Yasuda M. Lewis Acid‐Catalyzed Diastereoselective C−C Bond Insertion of Diazo Esters into Secondary Benzylic Halides for the Synthesis of α,β‐Diaryl‐β‐haloesters. Angew Chem Int Ed Engl 2022; 61:e202204462. [DOI: 10.1002/anie.202204462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Fei Wang
- Department of Applied Chemistry Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka 565-0871 Japan
| | - Yoshihiro Nishimoto
- Department of Applied Chemistry Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka 565-0871 Japan
- Innovative Catalysis Science Division Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI) Osaka University Suita Osaka 565-0871 Japan
| | - Makoto Yasuda
- Department of Applied Chemistry Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka 565-0871 Japan
- Innovative Catalysis Science Division Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI) Osaka University Suita Osaka 565-0871 Japan
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27
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Yan Q, Zhang X, Chen Y, Guo B, Zhou P, Chen B, Huang Q, Wang JB. From Semirational to Rational Design: Developing a Substrate-Coupled System of Glucose Dehydrogenase for Asymmetric Synthesis. ACS Catal 2022. [DOI: 10.1021/acscatal.2c00705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Qipeng Yan
- Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education) and Key Laboratory of Phytochemistry R&D of Hunan Province, College of Chemistry and Chemical Engineering, Hunan Normal University, 410081 Changsha, P. R. China
| | - Xinhua Zhang
- Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education) and Key Laboratory of Phytochemistry R&D of Hunan Province, College of Chemistry and Chemical Engineering, Hunan Normal University, 410081 Changsha, P. R. China
| | - Yingzhuang Chen
- Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education) and Key Laboratory of Phytochemistry R&D of Hunan Province, College of Chemistry and Chemical Engineering, Hunan Normal University, 410081 Changsha, P. R. China
| | - Bin Guo
- Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education) and Key Laboratory of Phytochemistry R&D of Hunan Province, College of Chemistry and Chemical Engineering, Hunan Normal University, 410081 Changsha, P. R. China
| | - Pei Zhou
- Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education) and Key Laboratory of Phytochemistry R&D of Hunan Province, College of Chemistry and Chemical Engineering, Hunan Normal University, 410081 Changsha, P. R. China
| | - Bo Chen
- Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education) and Key Laboratory of Phytochemistry R&D of Hunan Province, College of Chemistry and Chemical Engineering, Hunan Normal University, 410081 Changsha, P. R. China
| | - Qun Huang
- Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education) and Key Laboratory of Phytochemistry R&D of Hunan Province, College of Chemistry and Chemical Engineering, Hunan Normal University, 410081 Changsha, P. R. China
| | - Jian-bo Wang
- Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education) and Key Laboratory of Phytochemistry R&D of Hunan Province, College of Chemistry and Chemical Engineering, Hunan Normal University, 410081 Changsha, P. R. China
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Wang F, Nishimoto Y, Yasuda M. Lewis Acid‐Catalyzed Diastereoselective C–C Bond Insertion of Diazo Esters into Secondary Benzylic Halides for the Synthesis of α,β‐Diaryl‐β‐haloesters. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202204462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Fei Wang
- Osaka University School of Engineering Graduate School of Engineering: Osaka Daigaku Kogakubu Daigakuin Kogaku Kenkyuka Applied Chemistry JAPAN
| | | | - Makoto Yasuda
- Osaka University Department of Applied Chemistry, Graduate School of Engineering 2-1 Yamadaoka, Suita 565-0871 Osaka JAPAN
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Kinner A, Nerke P, Siedentop R, Steinmetz T, Classen T, Rosenthal K, Nett M, Pietruszka J, Lütz S. Recent Advances in Biocatalysis for Drug Synthesis. Biomedicines 2022; 10:964. [PMID: 35625702 PMCID: PMC9138302 DOI: 10.3390/biomedicines10050964] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/16/2022] [Accepted: 04/17/2022] [Indexed: 02/01/2023] Open
Abstract
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.
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Affiliation(s)
- Alina Kinner
- Chair for Bioprocess Engineering, Department of Biochemical and Chemical Engineering, TU Dortmund University, 44227 Dortmund, Germany; (A.K.); (P.N.); (R.S.); (K.R.)
| | - Philipp Nerke
- Chair for Bioprocess Engineering, Department of Biochemical and Chemical Engineering, TU Dortmund University, 44227 Dortmund, Germany; (A.K.); (P.N.); (R.S.); (K.R.)
| | - Regine Siedentop
- Chair for Bioprocess Engineering, Department of Biochemical and Chemical Engineering, TU Dortmund University, 44227 Dortmund, Germany; (A.K.); (P.N.); (R.S.); (K.R.)
| | - Till Steinmetz
- Laboratory for Technical Biology, Department of Biochemical and Chemical Engineering, TU Dortmund University, 44227 Dortmund, Germany; (T.S.); (M.N.)
| | - Thomas Classen
- Institute of Bio- and Geosciences: Biotechnology (IBG-1), Forschungszentrum Jülich, 52428 Jülich, Germany; (T.C.); (J.P.)
| | - Katrin Rosenthal
- Chair for Bioprocess Engineering, Department of Biochemical and Chemical Engineering, TU Dortmund University, 44227 Dortmund, Germany; (A.K.); (P.N.); (R.S.); (K.R.)
| | - Markus Nett
- Laboratory for Technical Biology, Department of Biochemical and Chemical Engineering, TU Dortmund University, 44227 Dortmund, Germany; (T.S.); (M.N.)
| | - Jörg Pietruszka
- Institute of Bio- and Geosciences: Biotechnology (IBG-1), Forschungszentrum Jülich, 52428 Jülich, Germany; (T.C.); (J.P.)
- Institute of Bioorganic Chemistry, Heinrich Heine University Düsseldorf Located at Forschungszentrum Jülich, 52426 Jülich, Germany
| | - Stephan Lütz
- Chair for Bioprocess Engineering, Department of Biochemical and Chemical Engineering, TU Dortmund University, 44227 Dortmund, Germany; (A.K.); (P.N.); (R.S.); (K.R.)
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30
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Lim KJH, Hartono YD, Xue B, Go MK, Fan H, Yew WS. Structure-Guided Engineering of Prenyltransferase NphB for High-Yield and Regioselective Cannabinoid Production. ACS Catal 2022. [DOI: 10.1021/acscatal.2c00786] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Kevin Jie Han Lim
- Synthetic Biology for Clinical and Technological Innovation, National University of Singapore, 28 Medical Drive, Singapore 117456
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671
| | - Yossa Dwi Hartono
- Synthetic Biology for Clinical and Technological Innovation, National University of Singapore, 28 Medical Drive, Singapore 117456
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671
| | - Bo Xue
- Synthetic Biology for Clinical and Technological Innovation, National University of Singapore, 28 Medical Drive, Singapore 117456
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597
| | - Maybelle Kho Go
- Synthetic Biology for Clinical and Technological Innovation, National University of Singapore, 28 Medical Drive, Singapore 117456
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597
| | - Hao Fan
- Synthetic Biology for Clinical and Technological Innovation, National University of Singapore, 28 Medical Drive, Singapore 117456
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671
| | - Wen Shan Yew
- Synthetic Biology for Clinical and Technological Innovation, National University of Singapore, 28 Medical Drive, Singapore 117456
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597
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