1
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Harding-Larsen D, Funk J, Madsen NG, Gharabli H, Acevedo-Rocha CG, Mazurenko S, Welner DH. Protein representations: Encoding biological information for machine learning in biocatalysis. Biotechnol Adv 2024:108459. [PMID: 39366493 DOI: 10.1016/j.biotechadv.2024.108459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 09/19/2024] [Accepted: 09/29/2024] [Indexed: 10/06/2024]
Abstract
Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.
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Affiliation(s)
- David Harding-Larsen
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark
| | - Jonathan Funk
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark
| | - Niklas Gesmar Madsen
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark
| | - Hani Gharabli
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark
| | - Carlos G Acevedo-Rocha
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark
| | - 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
| | - Ditte Hededam Welner
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark.
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2
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Harding-Larsen D, Madsen CD, Teze D, Kittilä T, Langhorn MR, Gharabli H, Hobusch M, Otalvaro FM, Kırtel O, Bidart GN, Mazurenko S, Travnik E, Welner DH. GASP: A Pan-Specific Predictor of Family 1 Glycosyltransferase Acceptor Specificity Enabled by a Pipeline for Substrate Feature Generation and Large-Scale Experimental Screening. ACS OMEGA 2024; 9:27278-27288. [PMID: 38947828 PMCID: PMC11209901 DOI: 10.1021/acsomega.4c01583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 07/02/2024]
Abstract
Glycosylation represents a major chemical challenge; while it is one of the most common reactions in Nature, conventional chemistry struggles with stereochemistry, regioselectivity, and solubility issues. In contrast, family 1 glycosyltransferase (GT1) enzymes can glycosylate virtually any given nucleophilic group with perfect control over stereochemistry and regioselectivity. However, the appropriate catalyst for a given reaction needs to be identified among the tens of thousands of available sequences. Here, we present the glycosyltransferase acceptor specificity predictor (GASP) model, a data-driven approach to the identification of reactive GT1:acceptor pairs. We trained a random forest-based acceptor predictor on literature data and validated it on independent in-house generated data on 1001 GT1:acceptor pairs, obtaining an AUROC of 0.79 and a balanced accuracy of 72%. The performance was stable even in the case of completely new GT1s and acceptors not present in the training data set, highlighting the pan-specificity of GASP. Moreover, the model is capable of parsing all known GT1 sequences, as well as all chemicals, the latter through a pipeline for the generation of 153 chemical features for a given molecule taking the CID or SMILES as input (freely available at https://github.com/degnbol/GASP). To investigate the power of GASP, the model prediction probability scores were compared to GT1 substrate conversion yields from a newly published data set, with the top 50% of GASP predictions corresponding to reactions with >50% synthetic yields. The model was also tested in two comparative case studies: glycosylation of the antihelminth drug niclosamide and the plant defensive compound DIBOA. In the first study, the model achieved an 83% hit rate, outperforming a hit rate of 53% from a random selection assay. In the second case study, the hit rate of GASP was 50%, and while being lower than the hit rate of 83% using expert-selected enzymes, it provides a reasonable performance for the cases when an expert opinion is unavailable. The hierarchal importance of the generated chemical features was investigated by negative feature selection, revealing properties related to cyclization and atom hybridization status to be the most important characteristics for accurate prediction. Our study provides a GT1:acceptor predictor which can be trained on other data sets enabled by the automated feature generation pipelines. We also release the new in-house generated data set used for testing of GASP to facilitate the future development of GT1 activity predictors and their robust benchmarking.
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Affiliation(s)
- David Harding-Larsen
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Christian Degnbol Madsen
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
- The
University of Melbourne Faculty of Science, Melbourne Integrative
Genomics, University of Melbourne, Building 184, Royal Parade, Parkville
3010, Melbourne, VIC 3052, Australia
| | - David Teze
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Tiia Kittilä
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | | | - Hani Gharabli
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Mandy Hobusch
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Felipe Mejia Otalvaro
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Onur Kırtel
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Gonzalo Nahuel Bidart
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Stanislav Mazurenko
- Department
of Experimental Biology and RECETOX, Faculty of Science, Masarykova Univerzita, Kamenice 5/A4, Brno 625 00, Czech Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, Brno 656
91, Czech Republic
| | - Evelyn Travnik
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Ditte Hededam Welner
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
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3
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Han Y, Zhang H, Zeng Z, Liu Z, Lu D, Liu Z. Descriptor-augmented machine learning for enzyme-chemical interaction predictions. Synth Syst Biotechnol 2024; 9:259-268. [PMID: 38450325 PMCID: PMC10915406 DOI: 10.1016/j.synbio.2024.02.006] [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: 11/01/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/08/2024] Open
Abstract
Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.
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Affiliation(s)
- Yilei Han
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
| | - Haoye Zhang
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Zheni Zeng
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Zhiyuan Liu
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Diannan Lu
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
| | - Zheng Liu
- Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China
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4
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Kroll A, Ranjan S, Lercher MJ. A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships. PLoS Comput Biol 2024; 20:e1012100. [PMID: 38768223 PMCID: PMC11142704 DOI: 10.1371/journal.pcbi.1012100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 05/31/2024] [Accepted: 04/24/2024] [Indexed: 05/22/2024] Open
Abstract
The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.
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Affiliation(s)
- Alexander Kroll
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Düsseldorf, Germany
| | - Sahasra Ranjan
- Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
| | - Martin J. Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, Düsseldorf, Germany
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5
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Wang X, Quinn D, Moody TS, Huang M. ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes. J Chem Inf Model 2024; 64:3123-3139. [PMID: 38573056 PMCID: PMC11040732 DOI: 10.1021/acs.jcim.4c00058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/15/2024] [Accepted: 03/11/2024] [Indexed: 04/05/2024]
Abstract
Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts. ALDELE incorporates both structural and sequence representations of proteins, alongside representations of ligands by subgraphs and overall physicochemical properties. Comprehensive evaluation demonstrated that ALDELE can predict the catalytic activities of enzymes, and particularly, it identifies residue-based hotspots to guide enzyme engineering and generates substrate heat maps to explore the substrate scope for a given biocatalyst. Moreover, our models notably match empirical data, reinforcing the practicality and reliability of our approach through the alignment with confirmed mutation sites. ALDELE offers a facile and comprehensive solution by integrating different toolkits tailored for different purposes at affordable computational cost and therefore would be valuable to speed up the discovery of new functional enzymes for their exploitation by the industry.
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Affiliation(s)
- Xiangwen Wang
- School
of Chemistry and Chemical Engineering, Queen’s
University Belfast, Belfast BT9 5AG, Northern Ireland, U.K.
- Department
of Biocatalysis and Isotope Chemistry, Almac
Sciences, Craigavon BT63 5QD, Northern Ireland, U.K.
| | - Derek Quinn
- Department
of Biocatalysis and Isotope Chemistry, Almac
Sciences, Craigavon BT63 5QD, Northern Ireland, U.K.
| | - Thomas S. Moody
- Department
of Biocatalysis and Isotope Chemistry, Almac
Sciences, Craigavon BT63 5QD, Northern Ireland, U.K.
- Arran
Chemical Company Limited, Unit 1 Monksland Industrial Estate, Athlone,
Co., Roscommon N37 DN24, Ireland
| | - Meilan Huang
- School
of Chemistry and Chemical Engineering, Queen’s
University Belfast, Belfast BT9 5AG, Northern Ireland, U.K.
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6
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Bretagne D, Pâris A, Matthews D, Fougère L, Burrini N, Wagner GK, Daniellou R, Lafite P. "Mix and match" auto-assembly of glycosyltransferase domains delivers biocatalysts with improved substrate promiscuity. J Biol Chem 2024; 300:105747. [PMID: 38354783 PMCID: PMC10937113 DOI: 10.1016/j.jbc.2024.105747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 01/25/2024] [Accepted: 02/08/2024] [Indexed: 02/16/2024] Open
Abstract
Glycosyltransferases (GT) catalyze the glycosylation of bioactive natural products, including peptides and proteins, flavonoids, and sterols, and have been extensively used as biocatalysts to generate glycosides. However, the often narrow substrate specificity of wild-type GTs requires engineering strategies to expand it. The GT-B structural family is constituted by GTs that share a highly conserved tertiary structure in which the sugar donor and acceptor substrates bind in dedicated domains. Here, we have used this selective binding feature to design an engineering process to generate chimeric glycosyltransferases that combine auto-assembled domains from two different GT-B enzymes. Our approach enabled the generation of a stable dimer with broader substrate promiscuity than the parent enzymes that were related to relaxed interactions between domains in the dimeric GT-B. Our findings provide a basis for the development of a novel class of heterodimeric GTs with improved substrate promiscuity for applications in biotechnology and natural product synthesis.
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Affiliation(s)
- Damien Bretagne
- Institut de Chimie Organique et Analytique (ICOA), UMR 7311 CNRS-Université d'Orléans, Université d'Orléans, Orléans Cedex 2, France; School of Pharmacy, Queen's University Belfast, Medical Biology Centre, Belfast, United Kingdom
| | - Arnaud Pâris
- Institut de Chimie Organique et Analytique (ICOA), UMR 7311 CNRS-Université d'Orléans, Université d'Orléans, Orléans Cedex 2, France
| | - David Matthews
- School of Pharmacy, Queen's University Belfast, Medical Biology Centre, Belfast, United Kingdom
| | - Laëtitia Fougère
- Institut de Chimie Organique et Analytique (ICOA), UMR 7311 CNRS-Université d'Orléans, Université d'Orléans, Orléans Cedex 2, France
| | - Nastassja Burrini
- Institut de Chimie Organique et Analytique (ICOA), UMR 7311 CNRS-Université d'Orléans, Université d'Orléans, Orléans Cedex 2, France
| | - Gerd K Wagner
- School of Pharmacy, Queen's University Belfast, Medical Biology Centre, Belfast, United Kingdom
| | - Richard Daniellou
- Institut de Chimie Organique et Analytique (ICOA), UMR 7311 CNRS-Université d'Orléans, Université d'Orléans, Orléans Cedex 2, France; Chaire de Cosmétologie, AgroParisTech, Orléans, France; Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France.
| | - Pierre Lafite
- Institut de Chimie Organique et Analytique (ICOA), UMR 7311 CNRS-Université d'Orléans, Université d'Orléans, Orléans Cedex 2, France.
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7
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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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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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Ono E, Murata J. Exploring the Evolvability of Plant Specialized Metabolism: Uniqueness Out Of Uniformity and Uniqueness Behind Uniformity. PLANT & CELL PHYSIOLOGY 2023; 64:1449-1465. [PMID: 37307423 PMCID: PMC10734894 DOI: 10.1093/pcp/pcad057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/28/2023] [Accepted: 06/09/2023] [Indexed: 06/14/2023]
Abstract
The huge structural diversity exhibited by plant specialized metabolites has primarily been considered to result from the catalytic specificity of their biosynthetic enzymes. Accordingly, enzyme gene multiplication and functional differentiation through spontaneous mutations have been established as the molecular mechanisms that drive metabolic evolution. Nevertheless, how plants have assembled and maintained such metabolic enzyme genes and the typical clusters that are observed in plant genomes, as well as why identical specialized metabolites often exist in phylogenetically remote lineages, is currently only poorly explained by a concept known as convergent evolution. Here, we compile recent knowledge on the co-presence of metabolic modules that are common in the plant kingdom but have evolved under specific historical and contextual constraints defined by the physicochemical properties of each plant specialized metabolite and the genetic presets of the biosynthetic genes. Furthermore, we discuss a common manner to generate uncommon metabolites (uniqueness out of uniformity) and an uncommon manner to generate common metabolites (uniqueness behind uniformity). This review describes the emerging aspects of the evolvability of plant specialized metabolism that underlie the vast structural diversity of plant specialized metabolites in nature.
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Affiliation(s)
- Eiichiro Ono
- Suntory Global Innovation Center Ltd. (SIC), 8-1-1 Seikadai, Seika-cho, Soraku-gun, Kyoto, 619-0284 Japan
| | - Jun Murata
- Bioorganic Research Institute (SUNBOR), Suntory Foundation for Life Sciences, 8-1-1 Seikadai, Seika-cho, Soraku-gun, Kyoto, 619-0284 Japan
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10
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Tang SN, Barnum CR, Szarzanowicz MJ, Sirirungruang S, Shih PM. Harnessing Plant Sugar Metabolism for Glycoengineering. BIOLOGY 2023; 12:1505. [PMID: 38132331 PMCID: PMC10741112 DOI: 10.3390/biology12121505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/27/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023]
Abstract
Plants possess an innate ability to generate vast amounts of sugar and produce a range of sugar-derived compounds that can be utilized for applications in industry, health, and agriculture. Nucleotide sugars lie at the unique intersection of primary and specialized metabolism, enabling the biosynthesis of numerous molecules ranging from small glycosides to complex polysaccharides. Plants are tolerant to perturbations to their balance of nucleotide sugars, allowing for the overproduction of endogenous nucleotide sugars to push flux towards a particular product without necessitating the re-engineering of upstream pathways. Pathways to produce even non-native nucleotide sugars may be introduced to synthesize entirely novel products. Heterologously expressed glycosyltransferases capable of unique sugar chemistries can further widen the synthetic repertoire of a plant, and transporters can increase the amount of nucleotide sugars available to glycosyltransferases. In this opinion piece, we examine recent successes and potential future uses of engineered nucleotide sugar biosynthetic, transport, and utilization pathways to improve the production of target compounds. Additionally, we highlight current efforts to engineer glycosyltransferases. Ultimately, the robust nature of plant sugar biochemistry renders plants a powerful chassis for the production of target glycoconjugates and glycans.
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Affiliation(s)
- Sophia N. Tang
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA;
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, CA 94608, USA; (M.J.S.)
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA
| | - Collin R. Barnum
- Biochemistry, Molecular, Cellular and Developmental Biology Graduate Group, University of California, Davis, CA 95616, USA
| | - Matthew J. Szarzanowicz
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, CA 94608, USA; (M.J.S.)
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Sasilada Sirirungruang
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, CA 94608, USA; (M.J.S.)
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Patrick M. Shih
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, CA 94608, USA; (M.J.S.)
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
- Innovative Genomics Institute, University of California, Berkeley, CA 94720, USA
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11
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Rates ADB, Cesarino I. Pour some sugar on me: The diverse functions of phenylpropanoid glycosylation. JOURNAL OF PLANT PHYSIOLOGY 2023; 291:154138. [PMID: 38006622 DOI: 10.1016/j.jplph.2023.154138] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 11/06/2023] [Indexed: 11/27/2023]
Abstract
The phenylpropanoid metabolism is the source of a vast array of specialized metabolites that play diverse functions in plant growth and development and contribute to all aspects of plant interactions with their surrounding environment. These compounds protect plants from damaging ultraviolet radiation and reactive oxygen species, provide mechanical support for the plants to stand upright, and mediate plant-plant and plant-microorganism communications. The enormous metabolic diversity of phenylpropanoids is further expanded by chemical modifications known as "decorative reactions", including hydroxylation, methylation, glycosylation, and acylation. Among these modifications, glycosylation is the major driving force of phenylpropanoid structural diversification, also contributing to the expansion of their properties. Phenylpropanoid glycosylation is catalyzed by regioselective uridine diphosphate (UDP)-dependent glycosyltransferases (UGTs), whereas glycosyl hydrolases known as β-glucosidases are the major players in deglycosylation. In this article, we review how the glycosylation process affects key physicochemical properties of phenylpropanoids, such as molecular stability and solubility, as well as metabolite compartmentalization/storage and biological activity/toxicity. We also summarize the recent knowledge on the functional implications of glycosylation of different classes of phenylpropanoid compounds. A balance of glycosylation/deglycosylation might represent an essential molecular mechanism to regulate phenylpropanoid homeostasis, allowing plants to dynamically respond to diverse environmental signals.
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Affiliation(s)
- Arthur de Barros Rates
- Departamento de Botânica, Instituto de Biociências, Universidade de São Paulo, Rua do Matão 277, 05508-090, São Paulo, Brazil
| | - Igor Cesarino
- Departamento de Botânica, Instituto de Biociências, Universidade de São Paulo, Rua do Matão 277, 05508-090, São Paulo, Brazil; Synthetic and Systems Biology Center, InovaUSP, Avenida Professor Lucio Martins Rodrigues 370, 05508-020, São Paulo, Brazil.
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12
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Zhang Q, Zheng W, Song Z, Zhang Q, Yang L, Wu J, Lin J, Xu G, Yu H. Machine Learning Enables Prediction of Pyrrolysyl-tRNA Synthetase Substrate Specificity. ACS Synth Biol 2023; 12:2403-2417. [PMID: 37486975 DOI: 10.1021/acssynbio.3c00225] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Knowledge about the substrate scope for a given enzyme is informative for elucidating biochemical pathways and also for expanding applications of the enzyme. However, no general methods are available to accurately predict the substrate specificity of an enzyme. Pyrrolysyl-tRNA synthetase (PylRS) is a powerful tool for incorporating various noncanonical amino acids (NCAAs) into proteins, which enabled us to probe, image, rationally engineer, and evolve protein structure and function. However, the incorporation of a new NCAA typically requires the selection of large libraries of PylRS with randomized mutations at active sites, and this process requires multiple rounds of selection for each new substrate. Therefore, a single aminoacyl-tRNA synthetase with broad substrate promiscuity is ideal to facilitate widespread applications of the genetic NCAA incorporation technique. Herein, machine learning models were developed to predict the substrate specificity of PylRS to accept novel NCAAs that could be incorporated into proteins by three PylRS mutants. The models were built from a training set of 285 unique enzyme-substrate pairs of three PylRS mutants including IFRS, BtaRS, and MFRS against 95 NCAAs. The best BaggingTree (BT) model was then used for virtually screening a NCAAs library containing 1474 phenylalanine, tyrosine, tryptophan, and alanine analogues, and 156 NCAAs were predicted to be accepted by at least one of the three PylRS mutants. Then, 27 NCAAs including 24 positive and 3 negative substrates were experimentally tested for their activities, and 20 of the 24 positive substrates showed weak or strong activity and were accepted by at least one PylRS mutant, among which 11 NCAAs were never reported to be incorporated into proteins before. Three negative substrates did not show any activity. Experimental results suggested that the BT model provides a three-class classification accuracy of 0.69 and a binary classification accuracy of 0.86. This study expanded the substrate scope of three PylRS variants and provided a framework for developing machine learning models to predict substrate specificity of other PylRS variants.
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Affiliation(s)
- Qunfeng Zhang
- Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Wenlong Zheng
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Hangzhou 311200, Zhejiang, China
| | - Zhongdi Song
- Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, Interdisciplinary Research Academy, Zhejiang Shuren University, Hangzhou 310015, China
| | - Qiang Zhang
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Hangzhou 311200, Zhejiang, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Lirong Yang
- Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Hangzhou 311200, Zhejiang, China
| | - Jianping Wu
- Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Hangzhou 311200, Zhejiang, China
| | - Jianping Lin
- Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Gang Xu
- Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Haoran Yu
- Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Hangzhou 311200, Zhejiang, China
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13
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Yin Q, Wu T, Gao R, Wu L, Shi Y, Wang X, Wang M, Xu Z, Zhao Y, Su X, Su Y, Han X, Yuan L, Xiang L, Chen S. Multi-omics reveal key enzymes involved in the formation of phenylpropanoid glucosides in Artemisia annua. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2023; 201:107795. [PMID: 37301186 DOI: 10.1016/j.plaphy.2023.107795] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/15/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
Although mainly known for producing artemisinin, Artemisia annua is enriched in phenylpropanoid glucosides (PGs) with significant bioactivities. However, the biosynthesis of A. annua PGs is insufficiently investigated. Different A. annua ecotypes from distinct growing environments accumulate varying amounts of metabolites, including artemisinin and PGs such as scopolin. UDP-glucose:phenylpropanoid glucosyltransferases (UGTs) transfers glucose from UDP-glucose in PG biosynthesis. Here, we found that the low-artemisinin ecotype GS produces a higher amount of scopolin, compared to the high-artemisinin ecotype HN. By combining transcriptome and proteome analyses, we selected 28 candidate AaUGTs from 177 annotated AaUGTs. Using AlphaFold structural prediction and molecular docking, we determined the binding affinities of 16 AaUGTs. Seven of the AaUGTs enzymatically glycosylated phenylpropanoids. AaUGT25 converted scopoletin to scopolin and esculetin to esculin. The lack of accumulation of esculin in the leaf and the high catalytic efficiency of AaUGT25 on esculetin suggest that esculetin is methylated to scopoletin, the precursor of scopolin. We also discovered that AaOMT1, a previously uncharacterized O-methyltransferase, converts esculetin to scopoletin, suggesting an alternative route for producing scopoletin, which contributes to the high-level accumulation of scopolin in A. annua leaves. AaUGT1 and AaUGT25 responded to induction of stress-related phytohormones, implying the involvement of PGs in stress responses.
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Affiliation(s)
- Qinggang Yin
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China; Key Laboratory of Beijing for Identification and Safety Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Tianze Wu
- School of Chemistry Chemical Engineering and Life Sciences, Wuhan University of Technology, No. 122, Lo Lion Road, Wuhan, Hubei, 430070, China
| | - Ranran Gao
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Lan Wu
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Yuhua Shi
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Xingwen Wang
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Mengyue Wang
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Zhichao Xu
- Ministry of Education, Key Laboratory of Saline-alkali Vegetation Ecology Restoration (Northeast Forestry University), Harbin, 150006, China
| | - Yueliang Zhao
- College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China
| | - Xiaojia Su
- College of Life Science and Technology, Henan Institute of Science and Technology, Xinxiang, 453000, China
| | - Yanyan Su
- Amway(China) Botanical R&D Center, Wuxi, 214115, China
| | - Xiaoyan Han
- China National Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Ling Yuan
- Department of Plant and Soil Sciences, University of Kentucky, Lexington, KY, 40546, USA; Kentucky Tobacco Research and Development Center, University of Kentucky, Lexington, KY, 40546-0236, USA
| | - Li Xiang
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Shilin Chen
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China; Key Laboratory of Beijing for Identification and Safety Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
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14
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Singh R, Sledzieski S, Bryson B, Cowen L, Berger B. Contrastive learning in protein language space predicts interactions between drugs and protein targets. Proc Natl Acad Sci U S A 2023; 120:e2220778120. [PMID: 37289807 PMCID: PMC10268324 DOI: 10.1073/pnas.2220778120] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/10/2023] [Indexed: 06/10/2023] Open
Abstract
Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance of one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pretrained protein language models ("PLex") and employing a protein-anchored contrastive coembedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with subnanomolar affinity, plus a strongly binding EPHB1 inhibitor (KD = 1.3 nM). Furthermore, ConPLex embeddings are interpretable, which enables us to visualize the drug-target embedding space and use embeddings to characterize the function of human cell-surface proteins. We anticipate that ConPLex will facilitate efficient drug discovery by making highly sensitive in silico drug screening feasible at the genome scale. ConPLex is available open source at https://ConPLex.csail.mit.edu.
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Affiliation(s)
- Rohit Singh
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Samuel Sledzieski
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Bryan Bryson
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA02155
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA02139
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15
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Deng J, Cui Q. Second-Shell Residues Contribute to Catalysis by Predominately Preorganizing the Apo State in PafA. J Am Chem Soc 2023; 145:11333-11347. [PMID: 37172218 PMCID: PMC10810092 DOI: 10.1021/jacs.3c02423] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Residues beyond the first coordination shell are often observed to make considerable cumulative contributions in enzymes. Due to typically indirect perturbations of multiple physicochemical properties of the active site, however, their individual and specific roles in enzyme catalysis and disease-causing mutations remain difficult to predict and understand at the molecular level. Here we analyze the contributions of several second-shell residues in phosphate-irrepressible alkaline phosphatase of flavobacterium (PafA), a representative system as one of the most efficient enzymes. By adopting a multifaceted approach that integrates quantum-mechanical/molecular-mechanical free energy computations, molecular-mechanical molecular dynamics simulations, and density functional theory cluster model calculations, we probe the rate-limiting phosphoryl transfer step and structural properties of all relevant enzyme states. In combination with available experimental data, our computational results show that mutations of the studied second-shell residues impact catalytic efficiency mainly by perturbation of the apo state and therefore substrate binding, while they do not affect the ground state or alter the nature of phosphoryl transfer transition state significantly. Several second-shell mutations also modulate the active site hydration level, which in turn influences the energetics of phosphoryl transfer. These mechanistic insights also help inform strategies that may improve the efficiency of enzyme design and engineering by going beyond the current focus on the first coordination shell.
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Affiliation(s)
- Jiahua Deng
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Qiang Cui
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
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16
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Kroll A, Ranjan S, Engqvist MKM, Lercher MJ. A general model to predict small molecule substrates of enzymes based on machine and deep learning. Nat Commun 2023; 14:2787. [PMID: 37188731 DOI: 10.1038/s41467-023-38347-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 04/21/2023] [Indexed: 05/17/2023] Open
Abstract
For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science.
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Affiliation(s)
- Alexander Kroll
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Sahasra Ranjan
- Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Martin K M Engqvist
- Department of Biology and Bioengineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- EnginZyme AB, Tomtebodevägen 6, 17165, Stockholm, Sweden
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany.
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17
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Vasina M, Kovar D, Damborsky J, Ding Y, Yang T, deMello A, Mazurenko S, Stavrakis S, Prokop Z. In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning. Biotechnol Adv 2023; 66:108171. [PMID: 37150331 DOI: 10.1016/j.biotechadv.2023.108171] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.
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Affiliation(s)
- Michal Vasina
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - David Kovar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - Yun Ding
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Tianjin Yang
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland; Department of Biochemistry, University of Zurich, 8057 Zurich, Switzerland
| | - Andrew deMello
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
| | - Stavros Stavrakis
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
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18
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Sirirungruang S, Barnum CR, Tang SN, Shih PM. Plant glycosyltransferases for expanding bioactive glycoside diversity. Nat Prod Rep 2023. [PMID: 36853278 DOI: 10.1039/d2np00077f] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Glycosylation is a successful strategy to alter the pharmacological properties of small molecules, and it has emerged as a unique approach to expand the chemical space of natural products that can be explored in drug discovery. Traditionally, most glycosylation events have been carried out chemically, often requiring many protection and deprotection steps to achieve a target molecule. Enzymatic glycosylation by glycosyltransferases could provide an alternative strategy for producing new glycosides. In particular, the glycosyltransferase family has greatly expanded in plants, representing a rich enzymatic resource to mine and expand the diversity of glycosides with novel bioactive properties. This article highlights previous and prospective uses for plant glycosyltransferases in generating bioactive glycosides and altering their pharmacological properties.
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Affiliation(s)
- Sasilada Sirirungruang
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA.,Feedstocks Division, Joint BioEnergy Institute, Emeryville, CA, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Center for Biomolecular Structure, Function and Application, Suranaree University of Technology, Nakhon Ratchasima, Thailand
| | - Collin R Barnum
- Department of Plant Biology, University of California, Davis, CA, USA
| | - Sophia N Tang
- Feedstocks Division, Joint BioEnergy Institute, Emeryville, CA, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Patrick M Shih
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA.,Feedstocks Division, Joint BioEnergy Institute, Emeryville, CA, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Innovative Genomics Institute, University of California, Berkeley, CA, USA
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19
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Song Z, Zhang Q, Wu W, Pu Z, Yu H. Rational design of enzyme activity and enantioselectivity. Front Bioeng Biotechnol 2023; 11:1129149. [PMID: 36761300 PMCID: PMC9902596 DOI: 10.3389/fbioe.2023.1129149] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 01/16/2023] [Indexed: 01/25/2023] Open
Abstract
The strategy of rational design to engineer enzymes is to predict the potential mutants based on the understanding of the relationships between protein structure and function, and subsequently introduce the mutations using the site-directed mutagenesis. Rational design methods are universal, relatively fast and have the potential to be developed into algorithms that can quantitatively predict the performance of the designed sequences. Compared to the protein stability, it was more challenging to design an enzyme with improved activity or selectivity, due to the complexity of enzyme molecular structure and inadequate understanding of the relationships between enzyme structures and functions. However, with the development of computational force, advanced algorithm and a deeper understanding of enzyme catalytic mechanisms, rational design could significantly simplify the process of engineering enzyme functions and the number of studies applying rational design strategy has been increasing. Here, we reviewed the recent advances of applying the rational design strategy to engineer enzyme functions including activity and enantioselectivity. Five strategies including multiple sequence alignment, strategy based on steric hindrance, strategy based on remodeling interaction network, strategy based on dynamics modification and computational protein design are discussed and the successful cases using these strategies are introduced.
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Affiliation(s)
- Zhongdi Song
- Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, Interdisciplinary Research Academy, Zhejiang Shuren University, Hangzhou, China
| | - Qunfeng Zhang
- Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wenhui Wu
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Hangzhou, Zhejiang, China
| | - Zhongji Pu
- Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Hangzhou, Zhejiang, China
| | - Haoran Yu
- Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Hangzhou, Zhejiang, China
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20
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Jiang Y, Ran X, Yang ZJ. Data-driven enzyme engineering to identify function-enhancing enzymes. Protein Eng Des Sel 2023; 36:gzac009. [PMID: 36214500 PMCID: PMC10365845 DOI: 10.1093/protein/gzac009] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/08/2022] [Accepted: 09/28/2022] [Indexed: 01/22/2023] Open
Abstract
Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence-structure-function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.
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Affiliation(s)
- Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Xinchun Ran
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Zhongyue J Yang
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37235, USA
- Data Science Institute, Vanderbilt University, Nashville, TN 37235, USA
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235, USA
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21
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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22
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Kari J, Schaller K, Molina GA, Borch K, Westh P. The Sabatier principle as a tool for discovery and engineering of industrial enzymes. Curr Opin Biotechnol 2022; 78:102843. [PMID: 36375405 DOI: 10.1016/j.copbio.2022.102843] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 10/16/2022] [Indexed: 11/13/2022]
Abstract
The recent breakthrough in all-atom, protein structure prediction opens new avenues for a range of computational approaches in enzyme design. These new approaches could become instrumental for the development of technical biocatalysts, and hence our transition toward more sustainable industries. Here, we discuss one approach, which is well-known within inorganic catalysis, but essentially unexploited in biotechnology. Specifically, we review examples of linear free-energy relationships (LFERs) for enzyme reactions and discuss how LFERs and the associated Sabatier Principle may be implemented in algorithms that estimate kinetic parameters and enzyme performance based on model structures.
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Affiliation(s)
- Jeppe Kari
- Roskilde University, Dept. Science and Environment, Universitetsvej 1, DK-4000 Roskilde, Denmark
| | - Kay Schaller
- Technical University of Denmark, Dept. of Biotechnology and Biomedicine, Sølvtofts Plads 224, DK-2800, Kgs. Lyngby, Denmark; University of Copenhagen, Dept. of Drug Design and Pharmacology, Universitetsparken 2, DK-2100 Copenhagen, Denmark
| | - Gustavo A Molina
- Technical University of Denmark, Dept. of Biotechnology and Biomedicine, Sølvtofts Plads 224, DK-2800, Kgs. Lyngby, Denmark; Technical University of Denmark, The Novo Nordisk Foundation Center for Biosustainability, Build. 220, Kemitorvet, DK-2800, Kgs. Lyngby, Denmark
| | - Kim Borch
- Novozymes A/S, Biologiens vej 2, DK-2800, Kgs. Lyngby, Denmark
| | - Peter Westh
- Technical University of Denmark, Dept. of Biotechnology and Biomedicine, Sølvtofts Plads 224, DK-2800, Kgs. Lyngby, Denmark.
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23
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Wittmund M, Cadet F, Davari MD. Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering. ACS Catal 2022. [DOI: 10.1021/acscatal.2c01426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Marcel Wittmund
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany
| | - Frederic Cadet
- Laboratory of Excellence LABEX GR, DSIMB, Inserm UMR S1134, University of Paris city & University of Reunion, Paris 75014, France
| | - Mehdi D. Davari
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany
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24
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Wu J, Zhu W, Shan X, Liu J, Zhao L, Zhao Q. Glycoside-specific metabolomics combined with precursor isotopic labeling for characterizing plant glycosyltransferases. MOLECULAR PLANT 2022; 15:1517-1532. [PMID: 35996753 DOI: 10.1016/j.molp.2022.08.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/19/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Glycosylation by uridine diphosphate-dependent glycosyltransferases (UGTs) in plants contributes to the complexity and diversity of secondary metabolites. UGTs are generally promiscuous in their use of acceptors, making it challenging to reveal the function of UGTs in vivo. Here, we described an approach that combined glycoside-specific metabolomics and precursor isotopic labeling analysis to characterize UGTs in Arabidopsis. We revisited the UGT72E cluster, which has been reported to catalyze the glycosylation of monolignols. Glycoside-specific metabolomics analysis reduced the number of differentially accumulated metabolites in the ugt72e1e2e3 mutant by at least 90% compared with that from traditional untargeted metabolomics analysis. In addition to the two previously reported monolignol glycosides, a total of 62 glycosides showed reduced accumulation in the ugt72e1e2e3 mutant, 22 of which were phenylalanine-derived glycosides, including 5-OH coniferyl alcohol-derived and lignan-derived glycosides, as confirmed by isotopic tracing of [13C6]-phenylalanine precursor. Our method revealed that UGT72Es could use coumarins as substrates, and genetic evidence showed that UGT72Es endowed plants with enhanced tolerance to low iron availability under alkaline conditions. Using the newly developed method, the function of UGT78D2 was also evaluated. These case studies suggest that this method can substantially contribute to the characterization of UGTs and efficiently investigate glycosylation processes, the complexity of which have been highly underestimated.
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Affiliation(s)
- Jie Wu
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wentao Zhu
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Xiaotong Shan
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jinyue Liu
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lingling Zhao
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Qiao Zhao
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Center for Plant Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China.
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25
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Dudley QM, Jo S, Guerrero DAS, Chhetry M, Smedley MA, Harwood WA, Sherden NH, O'Connor SE, Caputi L, Patron NJ. Reconstitution of monoterpene indole alkaloid biosynthesis in genome engineered Nicotiana benthamiana. Commun Biol 2022; 5:949. [PMID: 36088516 PMCID: PMC9464250 DOI: 10.1038/s42003-022-03904-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 08/25/2022] [Indexed: 12/17/2022] Open
Abstract
Monoterpene indole alkaloids (MIAs) are a diverse class of plant natural products that include a number of medicinally important compounds. We set out to reconstitute the pathway for strictosidine, a key intermediate of all MIAs, from central metabolism in Nicotiana benthamiana. A disadvantage of this host is that its rich background metabolism results in the derivatization of some heterologously produced molecules. Here we use transcriptomic analysis to identify glycosyltransferases that are upregulated in response to biosynthetic intermediates and produce plant lines with targeted mutations in the genes encoding them. Expression of the early MIA pathway in these lines produces a more favorable product profile. Strictosidine biosynthesis was successfully reconstituted, with the best yields obtained by the co-expression of 14 enzymes, of which a major latex protein-like enzyme (MLPL) from Nepeta (catmint) is critical for improving flux through the iridoid pathway. The removal of endogenous glycosyltransferases does not impact the yields of strictosidine, highlighting that the metabolic flux of the pathway enzymes to a stable biosynthetic intermediate minimizes the need to engineer the endogenous metabolism of the host. The production of strictosidine in planta expands the range of MIA products amenable to biological synthesis.
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Affiliation(s)
- Quentin M Dudley
- Engineering Biology, Earlham Institute, Norwich Research Park, Norwich, Norfolk, NR4 7UZ, UK
| | - Seohyun Jo
- Engineering Biology, Earlham Institute, Norwich Research Park, Norwich, Norfolk, NR4 7UZ, UK
| | - Delia Ayled Serna Guerrero
- Department of Natural Product Biosynthesis, Max Planck Institute for Chemical Ecology, Jena, 07745, Germany
| | - Monika Chhetry
- John Innes Centre, Norwich Research Park, Norwich, NR4 7TJ, UK
| | - Mark A Smedley
- John Innes Centre, Norwich Research Park, Norwich, NR4 7TJ, UK
| | - Wendy A Harwood
- John Innes Centre, Norwich Research Park, Norwich, NR4 7TJ, UK
| | - Nathaniel H Sherden
- John Innes Centre, Norwich Research Park, Norwich, NR4 7TJ, UK
- Octagon Therapeutics Ltd, 700 Main Street, North Cambridge, MA, 02139, USA
| | - Sarah E O'Connor
- Department of Natural Product Biosynthesis, Max Planck Institute for Chemical Ecology, Jena, 07745, Germany
| | - Lorenzo Caputi
- Department of Natural Product Biosynthesis, Max Planck Institute for Chemical Ecology, Jena, 07745, Germany.
| | - Nicola J Patron
- Engineering Biology, Earlham Institute, Norwich Research Park, Norwich, Norfolk, NR4 7UZ, UK.
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26
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Godoy CA, Pardo-Tamayo JS, Barbosa O. Microbial Lipases and Their Potential in the Production of Pharmaceutical Building Blocks. Int J Mol Sci 2022; 23:9933. [PMID: 36077332 PMCID: PMC9456414 DOI: 10.3390/ijms23179933] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022] Open
Abstract
Processes involving lipases in obtaining active pharmaceutical ingredients (APIs) are crucial to increase the sustainability of the industry. Despite their lower production cost, microbial lipases are striking for their versatile catalyzing reactions beyond their physiological role. In the context of taking advantage of microbial lipases in reactions for the synthesis of API building blocks, this review focuses on: (i) the structural origins of the catalytic properties of microbial lipases, including the results of techniques such as single particle monitoring (SPT) and the description of its selectivity beyond the Kazlauskas rule as the "Mirror-Image Packing" or the "Key Region(s) rule influencing enantioselectivity" (KRIE); (ii) immobilization methods given the conferred operative advantages in industrial applications and their modulating capacity of lipase properties; and (iii) a comprehensive description of microbial lipases use as a conventional or promiscuous catalyst in key reactions in the organic synthesis (Knoevenagel condensation, Morita-Baylis-Hillman (MBH) reactions, Markovnikov additions, Baeyer-Villiger oxidation, racemization, among others). Finally, this review will also focus on a research perspective necessary to increase microbial lipases application development towards a greener industry.
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Affiliation(s)
- César A. Godoy
- Laboratorio de Investigación en Biocatálisis y Biotransformaciones (LIBB), Grupo de Investigación en Ingeniería de los Procesos Agroalimentarios y Biotecnológicos (GIPAB), Departamento de Química, Universidad del Valle, Cali 76001, Colombia
| | - Juan S. Pardo-Tamayo
- Laboratorio de Investigación en Biocatálisis y Biotransformaciones (LIBB), Grupo de Investigación en Ingeniería de los Procesos Agroalimentarios y Biotecnológicos (GIPAB), Departamento de Química, Universidad del Valle, Cali 76001, Colombia
| | - Oveimar Barbosa
- Grupo de Investigación de Materiales Porosos (GIMPOAT), Departamento de Química, Universidad del Tolima, Ibague 730001, Colombia
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27
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Teze D, Bidart GN, Welner DH. Family 1 glycosyltransferases (GT1, UGTs) are subject to dilution-induced inactivation and low chemo stability toward their own acceptor substrates. Front Mol Biosci 2022; 9:909659. [PMID: 35936788 PMCID: PMC9354691 DOI: 10.3389/fmolb.2022.909659] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Glycosylation reactions are essential but challenging from a conventional chemistry standpoint. Conversely, they are biotechnologically feasible as glycosyltransferases can transfer sugar to an acceptor with perfect regio- and stereo-selectivity, quantitative yields, in a single reaction and under mild conditions. Low stability is often alleged to be a limitation to the biotechnological application of glycosyltransferases. Here we show that these enzymes are not necessarily intrinsically unstable, but that they present both dilution-induced inactivation and low chemostability towards their own acceptor substrates, and that these two phenomena are synergistic. We assessed 18 distinct GT1 enzymes against three unrelated acceptors (apigenin, resveratrol, and scopoletin—respectively a flavone, a stilbene, and a coumarin), resulting in a total of 54 enzymes: substrate pairs. For each pair, we varied catalyst and acceptor concentrations to obtain 16 different reaction conditions. Fifteen of the assayed enzymes (83%) displayed both low chemostability against at least one of the assayed acceptors at submillimolar concentrations, and dilution-induced inactivation. Furthermore, sensitivity to reaction conditions seems to be related to the thermal stability of the enzymes, the three unaffected enzymes having melting temperatures above 55°C, whereas the full enzyme panel ranged from 37.4 to 61.7°C. These results are important for GT1 understanding and engineering, as well as for discovery efforts and biotechnological use.
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Affiliation(s)
- David Teze
- *Correspondence: David Teze, ; Ditte Hededam Welner,
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28
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Shi Z, Liu P, Liao X, Mao Z, Zhang J, Wang Q, Sun J, Ma H, Ma Y. Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing. BIODESIGN RESEARCH 2022; 2022:9898461. [PMID: 37850146 PMCID: PMC10521697 DOI: 10.34133/2022/9898461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/26/2022] [Indexed: 10/19/2023] Open
Abstract
Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.
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Affiliation(s)
- Zhenkun Shi
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Pi Liu
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Xiaoping Liao
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Zhitao Mao
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Jianqi Zhang
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Qinhong Wang
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Jibin Sun
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Hongwu Ma
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Yanhe Ma
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
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29
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Behr M, Speeckaert N, Kurze E, Morel O, Prévost M, Mol A, Mahamadou Adamou N, Baragé M, Renaut J, Schwab W, El Jaziri M, Baucher M. Leaf necrosis resulting from downregulation of poplar glycosyltransferase UGT72A2. TREE PHYSIOLOGY 2022; 42:1084-1099. [PMID: 34865151 DOI: 10.1093/treephys/tpab161] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/29/2021] [Indexed: 06/13/2023]
Abstract
Reactive species (RS) causing oxidative stress are unavoidable by-products of various plant metabolic processes, such as photosynthesis, respiration or photorespiration. In leaves, flavonoids scavenge RS produced during photosynthesis and protect plant cells against deleterious oxidative damages. Their biosynthesis and accumulation are therefore under tight regulation at the cellular level. Glycosylation has emerged as an essential biochemical reaction in the homeostasis of various specialized metabolites such as flavonoids. This article provides a functional characterization of the Populus tremula x P. alba (poplar) UGT72A2 coding for a UDP-glycosyltransferase that is localized in the chloroplasts. Compared with the wild type, transgenic poplar lines with decreased expression of UGT72A2 are characterized by reduced growth and oxidative damages in leaves, as evidenced by necrosis, higher content of glutathione and lipid peroxidation products as well as diminished soluble peroxidase activity and NADPH to NADP+ ratio under standard growing conditions. They furthermore display lower pools of phenolics, anthocyanins and total flavonoids but higher proanthocyanidins content. Promoter analysis revealed the presence of cis-elements involved in photomorphogenesis, chloroplast biogenesis and flavonoid biosynthesis. The UGT72A2 is regulated by the poplar MYB119, a transcription factor known to regulate the flavonoid biosynthesis pathway. Phylogenetic analysis and molecular docking suggest that UGT72A2 could glycosylate flavonoids; however, the actual substrate(s) was not consistently evidenced with either in vitro assays nor analyses of glycosylated products in leaves of transgenic poplar overexpressing or downregulated for UGT72A2. This article provides elements highlighting the importance of flavonoid glycosylation regarding protection against oxidative stress in poplar leaves and raises new questions about the link between this biochemical reaction and regulation of the redox homeostasis system.
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Affiliation(s)
- Marc Behr
- Laboratory of Plant Biotechnology, Université libre de Bruxelles, 12 rue des Profs Jeener et Brachet, Gosselies 6041, Belgium
| | - Nathanael Speeckaert
- Laboratory of Plant Biotechnology, Université libre de Bruxelles, 12 rue des Profs Jeener et Brachet, Gosselies 6041, Belgium
| | - Elisabeth Kurze
- Biotechnology of Natural Products, Technische Universität München, 85354 Freising, Germany
| | - Oriane Morel
- Laboratory of Plant Biotechnology, Université libre de Bruxelles, 12 rue des Profs Jeener et Brachet, Gosselies 6041, Belgium
| | - Martine Prévost
- Unité de recherche Structure et Fonction des Membranes Biologiques, Université libre de Bruxelles, Bruxelles, Belgium
| | - Adeline Mol
- Laboratory of Plant Biotechnology, Université libre de Bruxelles, 12 rue des Profs Jeener et Brachet, Gosselies 6041, Belgium
| | - Nassirou Mahamadou Adamou
- Laboratory of Plant Biotechnology, Université libre de Bruxelles, 12 rue des Profs Jeener et Brachet, Gosselies 6041, Belgium
- Laboratoire de Biotechnologie Végétale et Amélioration des Plantes (LABAP), Université Abdou Moumouni de Niamey, Niamey, Niger
| | - Moussa Baragé
- Laboratoire de Biotechnologie Végétale et Amélioration des Plantes (LABAP), Université Abdou Moumouni de Niamey, Niamey, Niger
| | - Jenny Renaut
- Luxembourg Institute of Science and Technology, 4422 Belvaux, Luxembourg
| | - Wilfried Schwab
- Biotechnology of Natural Products, Technische Universität München, 85354 Freising, Germany
| | - Mondher El Jaziri
- Laboratory of Plant Biotechnology, Université libre de Bruxelles, 12 rue des Profs Jeener et Brachet, Gosselies 6041, Belgium
| | - Marie Baucher
- Laboratory of Plant Biotechnology, Université libre de Bruxelles, 12 rue des Profs Jeener et Brachet, Gosselies 6041, Belgium
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30
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Gao HY, Liu Y, Tan FF, Zhu LW, Jia KZ, Tang YJ. Advances and Challenges in Enzymatic C-glycosylation of Flavonoids in Plants. Curr Pharm Des 2022; 28:1466-1479. [PMID: 35466866 DOI: 10.2174/1381612828666220422085128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 03/03/2022] [Indexed: 11/22/2022]
Abstract
Flavonoid glycosides play required determinant roles in plants and have considerable potential for applications in medicine and biotechnology. Glycosyltransferases transfer a sugar moiety from uridine diphosphate-activated sugar molecules to an acceptor flavonoid via C-O and C-C linkages. Compared with O-glycosylflavonoids, C-glycosylflavonoids are more stable, are resistant to glycosidase or acid hydrolysis, exhibit better pharmacological properties, and have received more attention. Herein, we discuss the mining of C-glycosylflavones and the corresponding C-glycosyltransferases and evaluate the differences in structure and catalytic mechanisms between C-glycosyltransferase and O-glycosyltransferase. We conclude that promiscuity and specificity are key determinants for general flavonoid C-glycosyltransferase engineering and summarize the C-glycosyltransferase engineering strategy. A thorough understanding of the properties, catalytic mechanisms, and engineering of C-glycosyltransferases will be critical for any future biotechnological applications in areas such as the production of desired C-glycosylflavonoids for nutritional or medicinal use.
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Affiliation(s)
- Hui-Yao Gao
- National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei Key Laboratory of Industrial Microbiology, Hubei Provincial Cooperative Innovation Center of Industrial Fermentation, Hubei University of Technology, Wuhan 430068, China
| | - Yan Liu
- National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei Key Laboratory of Industrial Microbiology, Hubei Provincial Cooperative Innovation Center of Industrial Fermentation, Hubei University of Technology, Wuhan 430068, China
| | - Fei-Fan Tan
- National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei Key Laboratory of Industrial Microbiology, Hubei Provincial Cooperative Innovation Center of Industrial Fermentation, Hubei University of Technology, Wuhan 430068, China
| | - Li-Wen Zhu
- National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei Key Laboratory of Industrial Microbiology, Hubei Provincial Cooperative Innovation Center of Industrial Fermentation, Hubei University of Technology, Wuhan 430068, China
| | - Kai-Zhi Jia
- National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei Key Laboratory of Industrial Microbiology, Hubei Provincial Cooperative Innovation Center of Industrial Fermentation, Hubei University of Technology, Wuhan 430068, China
| | - Ya-Jie Tang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China
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31
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UGT72, a Major Glycosyltransferase Family for Flavonoid and Monolignol Homeostasis in Plants. BIOLOGY 2022; 11:biology11030441. [PMID: 35336815 PMCID: PMC8945231 DOI: 10.3390/biology11030441] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/07/2022] [Accepted: 03/11/2022] [Indexed: 11/16/2022]
Abstract
Simple Summary Phenylpropanoids are specialized metabolites playing crucial roles in plant developmental processes and in plant defense towards pathogens. The attachment of sugar moieties to these small hydrophobic molecules renders them more hydrophilic and increases their solubility. The UDP-glycosyltransferase 72 family (UGT72) of plants has been shown to glycosylate mainly two classes of phenylpropanoids, (i) the monolignols that are the building blocks of lignin, the second most abundant polymer after cellulose, and (ii) the flavonoids, which play determinant roles in plant interactions with other organisms and in response to stress. The purpose of this review is to bring an overview of the current knowledge of the UGT72 family and to highlight its role in the homeostasis of these molecules. Potential applications in pharmacology and in wood, paper pulp, and bioethanol production are given within the perspectives. Abstract Plants have developed the capacity to produce a diversified range of specialized metabolites. The glycosylation of those metabolites potentially decreases their toxicity while increasing their stability and their solubility, modifying their transport and their storage. The UGT, forming the largest glycosyltransferase superfamily in plants, combine enzymes that glycosylate mainly hormones and phenylpropanoids by using UDP-sugar as a sugar donor. Particularly, members of the UGT72 family have been shown to glycosylate the monolignols and the flavonoids, thereby being involved in their homeostasis. First, we explore primitive UGTs in algae and liverworts that are related to the angiosperm UGT72 family and their role in flavonoid homeostasis. Second, we describe the role of several UGT72s glycosylating monolignols, some of which have been associated with lignification. In addition, the role of other UGT72 members that glycosylate flavonoids and are involved in the development and/or stress response is depicted. Finally, the importance to explore the subcellular localization of UGTs to study their roles in planta is discussed.
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32
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He B, Bai X, Tan Y, Xie W, Feng Y, Yang GY. Glycosyltransferases: Mining, engineering and applications in biosynthesis of glycosylated plant natural products. Synth Syst Biotechnol 2022; 7:602-620. [PMID: 35261926 PMCID: PMC8883072 DOI: 10.1016/j.synbio.2022.01.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/10/2021] [Accepted: 01/02/2022] [Indexed: 12/14/2022] Open
Abstract
UDP-Glycosyltransferases (UGTs) catalyze the transfer of nucleotide-activated sugars to specific acceptors, among which the GT1 family enzymes are well-known for their function in biosynthesis of natural product glycosides. Elucidating GT function represents necessary step in metabolic engineering of aglycone glycosylation to produce drug leads, cosmetics, nutrients and sweeteners. In this review, we systematically summarize the phylogenetic distribution and catalytic diversity of plant GTs. We also discuss recent progress in the identification of novel GT candidates for synthesis of plant natural products (PNPs) using multi-omics technology and deep learning predicted models. We also highlight recent advances in rational design and directed evolution engineering strategies for new or improved GT functions. Finally, we cover recent breakthroughs in the application of GTs for microbial biosynthesis of some representative glycosylated PNPs, including flavonoid glycosides (fisetin 3-O-glycosides, astragalin, scutellarein 7-O-glucoside), terpenoid glycosides (rebaudioside A, ginsenosides) and polyketide glycosides (salidroside, polydatin).
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Affiliation(s)
- Bo He
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xue Bai
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yumeng Tan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Wentao Xie
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Guang-Yu Yang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
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Li Y, Li J, Diao M, Peng L, Huang S, Xie N. Characterization of a Group of UDP-Glycosyltransferases Involved in the Biosynthesis of Triterpenoid Saponins of Panax notoginseng. ACS Synth Biol 2022; 11:770-779. [PMID: 35107265 DOI: 10.1021/acssynbio.1c00469] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
UDP-glycosyltransferase (UGT)-mediated glycosylation is a common modification in triterpene saponins, which exhibit a wide range of bioactivities and important pharmacological effects. However, few UGTs involved in saponin biosynthesis have been identified, limiting the biosynthesis of saponins. In this study, an efficient heterologous expression system was established for evaluating the UGT-mediated glycosylation process of triterpene saponins. Six UGTs (UGTPn17, UGTPn42, UGTPn35, UGTPn87, UGTPn19, and UGTPn12) from Panax notoginseng were predicted and found to be responsible for efficient and direct enzymatic biotransformation of 21 triterpenoid saponins via 26 various glycosylation reactions. Among them, UGTPn87 exhibited promiscuous sugar-donor specificity of UDP-glucose (UDP-Glc) and UDP-xylose (UDP-Xyl) by catalyzing the elongation of the second sugar chain at the C3 or/and C20 sites of protopanaxadiol-type saponins with a UDP-Glc or UDP-Xyl donor, as well as at the C20 site of protopanaxadiol-type saponins with a UDP-Glc donor. Two new saponins, Fd-Xyl and Fe-Xyl, were generated by catalyzing the C3-O-Glc xylosylations of notoginsenoside Fd and notoginsenoside Fe when incubated with UGTPn87. Moreover, the complete biosynthetic pathways of 17 saponins were elucidated, among which notoginsenoside L, vinaginsenoside R16, gypenoside LXXV, and gypenoside XVII were revealed in Panax for the first time. A yeast cell factory was constructed with a yield of Rh2 at 354.69 mg/L and a glycosylation ratio of 60.40% in flasks. Our results reveal the biosynthetic pathway of a group of saponins in P. notoginseng and provide a theoretical basis for producing rare and valuable saponins, promoting their industrial application in medicine and functional foods.
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Affiliation(s)
- Yanting Li
- College of Life Science and Technology, Guangxi University, 100 Daxue Road, Nanning 530004, China
- State Key Laboratory of Non-Food Biomass and Enzyme Technology, National Engineering Research Center for Non-Food Biorefinery, Guangxi Biomass Engineering Technology Research Center, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China
| | - Jianxiu Li
- State Key Laboratory of Non-Food Biomass and Enzyme Technology, National Engineering Research Center for Non-Food Biorefinery, Guangxi Biomass Engineering Technology Research Center, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China
| | - Mengxue Diao
- State Key Laboratory of Non-Food Biomass and Enzyme Technology, National Engineering Research Center for Non-Food Biorefinery, Guangxi Biomass Engineering Technology Research Center, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China
| | - Longyun Peng
- State Key Laboratory of Non-Food Biomass and Enzyme Technology, National Engineering Research Center for Non-Food Biorefinery, Guangxi Biomass Engineering Technology Research Center, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China
| | - Shihai Huang
- College of Life Science and Technology, Guangxi University, 100 Daxue Road, Nanning 530004, China
| | - Nengzhong Xie
- State Key Laboratory of Non-Food Biomass and Enzyme Technology, National Engineering Research Center for Non-Food Biorefinery, Guangxi Biomass Engineering Technology Research Center, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China
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Yadav A, Vagne Q, Sens P, Iyengar G, Rao M. Glycan processing in the Golgi: optimal information coding and constraints on cisternal number and enzyme specificity. eLife 2022; 11:76757. [PMID: 35175197 PMCID: PMC9154746 DOI: 10.7554/elife.76757] [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: 01/04/2022] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Many proteins that undergo sequential enzymatic modification in the Golgi cisternae are displayed at the plasma membrane as cell identity markers. The modified proteins, called glycans, represent a molecular code. The fidelity of this glycan code is measured by how accurately the glycan synthesis machinery realises the desired target glycan distribution for a particular cell type and niche. In this paper, we construct a simplified chemical synthesis model to quantitatively analyse the tradeoffs between the number of cisternae, and the number and specificity of enzymes, required to synthesize a prescribed target glycan distribution of a certain complexity to within a given fidelity. We find that to synthesize complex distributions, such as those observed in real cells, one needs to have multiple cisternae and precise enzyme partitioning in the Golgi. Additionally, for fixed number of enzymes and cisternae, there is an optimal level of specificity (promiscuity) of enzymes that achieves the target distribution with high fidelity. The geometry of the fidelity landscape in the multidimensional space of the number and specificity of enzymes, inter-cisternal transfer rates, and number of cisternae, provides a measure for robustness and identifies stiff and sloppy directions. Our results show how the complexity of the target glycan distribution and number of glycosylation enzymes places functional constraints on the Golgi cisternal number and enzyme specificity.
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Affiliation(s)
| | - Quentin Vagne
- Laboratoire Physico Chimie Curie, Institut Curie, CNRS UMR168, Paris, France
| | - Pierre Sens
- Laboratoire Physico Chimie Curie, Institut Curie, CNRS UMR168, Paris, France
| | - Garud Iyengar
- Industrial Engineering and Operations Research, Columbia University, New York, United States
| | - Madan Rao
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore, India
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35
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Machine learning modeling of family wide enzyme-substrate specificity screens. PLoS Comput Biol 2022; 18:e1009853. [PMID: 35143485 PMCID: PMC8865696 DOI: 10.1371/journal.pcbi.1009853] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 02/23/2022] [Accepted: 01/21/2022] [Indexed: 11/19/2022] Open
Abstract
Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications. Predicting interactions between compounds and proteins represents a long-standing dream of drug discovery and protein engineering. Robust models of enzyme-substrate scope would dramatically advance our ability to design synthetic routes involving enzymatic catalysis. However, the lack of standardization between compound-protein interaction studies makes it difficult to evaluate the generalizability of such models. In this work we take a critical step forward by standardizing high-quality datasets measuring enzyme-substrate interactions, outlining rigorous evaluations, and proposing a new way to integrate structural information into protein representations. In testing previous modeling approaches, we highlight a surprising inability of existing models to effectively leverage compound-protein interactions to improve generalization, which challenges a perception in the literature. This establishes future opportunities for model development and integration of enzyme-substrate scope models into computer-aided synthesis planning software.
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Yin Q, Wei Y, Han X, Chen J, Gao H, Sun W. Unraveling the Glucosylation of Astringency Compounds of Horse Chestnut via Integrative Sensory Evaluation, Flavonoid Metabolism, Differential Transcriptome, and Phylogenetic Analysis. FRONTIERS IN PLANT SCIENCE 2022; 12:830343. [PMID: 35185970 PMCID: PMC8850972 DOI: 10.3389/fpls.2021.830343] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 12/27/2021] [Indexed: 06/12/2023]
Abstract
The seeds of Chinese horse chestnut are used as a source of starch and escin, whereas the potential use of whole plant has been ignored. The astringency and bitterness of tea produced from the leaves and flowers were found to be significantly better than those of green tea, suggesting that the enriched flavonoids maybe sensory determinates. During 47 flavonoids identified in leaves and flowers, seven flavonol glycosides in the top 10 including astragalin and isoquercitrin were significantly higher content in flowers than in leaves. The crude proteins of flowers could catalyze flavonol glucosides' formation, in which three glycosyltransferases contributed to the flavonol glucosylation were screened out by multi-dimensional integration of transcriptome, evolutionary analyses, recombinant enzymatic analysis and molecular docking. The deep exploration for flavonol profile and glycosylation provides theoretical and experimental basis for utilization of flowers and leaves of Aesculus chinensis as additives and dietary supplements.
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Affiliation(s)
- Qinggang Yin
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yiding Wei
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaoyan Han
- Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, China
| | - Jingwang Chen
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Han Gao
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Wei Sun
- Artemisinin Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
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37
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Coines J, Cuxart I, Teze D, Rovira C. Computer Simulation to Rationalize “Rational” Engineering of Glycoside Hydrolases and Glycosyltransferases. J Phys Chem B 2022; 126:802-812. [PMID: 35073079 PMCID: PMC8819650 DOI: 10.1021/acs.jpcb.1c09536] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
![]()
Glycoside hydrolases
and glycosyltransferases are the main classes
of enzymes that synthesize and degrade carbohydrates, molecules essential
to life that are a challenge for classical chemistry. As such, considerable
efforts have been made to engineer these enzymes and make them pliable
to human needs, ranging from directed evolution to rational design,
including mechanism engineering. Such endeavors fall short and are
unreported in numerous cases, while even success is a necessary but
not sufficient proof that the chemical rationale behind the design
is correct. Here we review some of the recent work in CAZyme mechanism
engineering, showing that computational simulations are instrumental
to rationalize experimental data, providing mechanistic insight into
how native and engineered CAZymes catalyze chemical reactions. We
illustrate this with two recent studies in which (i) a glycoside hydrolase
is converted into a glycoside phosphorylase and (ii) substrate specificity
of a glycosyltransferase is engineered toward forming O-, N-, or S-glycosidic bonds.
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Affiliation(s)
- Joan Coines
- Departament de Química Inorgànica i Orgànica and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, Barcelona 08028, Spain
| | - Irene Cuxart
- Departament de Química Inorgànica i Orgànica and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, Barcelona 08028, Spain
| | - David Teze
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
| | - Carme Rovira
- Departament de Química Inorgànica i Orgànica and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, Barcelona 08028, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain
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Catalytic flexibility of rice glycosyltransferase OsUGT91C1 for the production of palatable steviol glycosides. Nat Commun 2021; 12:7030. [PMID: 34857750 PMCID: PMC8639739 DOI: 10.1038/s41467-021-27144-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 10/26/2021] [Indexed: 02/05/2023] Open
Abstract
Steviol glycosides are the intensely sweet components of extracts from Stevia rebaudiana. These molecules comprise an invariant steviol aglycone decorated with variable glycans and could widely serve as a low-calorie sweetener. However, the most desirable steviol glycosides Reb D and Reb M, devoid of unpleasant aftertaste, are naturally produced only in trace amounts due to low levels of specific β (1-2) glucosylation in Stevia. Here, we report the biochemical and structural characterization of OsUGT91C1, a glycosyltransferase from Oryza sativa, which is efficient at catalyzing β (1-2) glucosylation. The enzyme's ability to bind steviol glycoside substrate in three modes underlies its flexibility to catalyze β (1-2) glucosylation in two distinct orientations as well as β (1-6) glucosylation. Guided by the structural insights, we engineer this enzyme to enhance the desirable β (1-2) glucosylation, eliminate β (1-6) glucosylation, and obtain a promising catalyst for the industrial production of naturally rare but palatable steviol glycosides.
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Kurze E, Wüst M, Liao J, McGraphery K, Hoffmann T, Song C, Schwab W. Structure-function relationship of terpenoid glycosyltransferases from plants. Nat Prod Rep 2021; 39:389-409. [PMID: 34486004 DOI: 10.1039/d1np00038a] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Covering: up to 2021Terpenoids are physiologically active substances that are of great importance to humans. Their physicochemical properties are modified by glycosylation, in terms of polarity, volatility, solubility and reactivity, and their bioactivities are altered accordingly. Significant scientific progress has been made in the functional study of glycosylated terpenes and numerous plant enzymes involved in regio- and enantioselective glycosylation have been characterized, a reaction that remains chemically challenging. Crucial clues to the mechanism of terpenoid glycosylation were recently provided by the first crystal structures of a diterpene glycosyltransferase UGT76G1. Here, we review biochemically characterized terpenoid glycosyltransferases, compare their functions and primary structures, discuss their acceptor and donor substrate tolerance and product specificity, and elaborate features of the 3D structures of the first terpenoid glycosyltransferases from plants.
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Affiliation(s)
- Elisabeth Kurze
- Biotechnology of Natural Products, TUM School of Life Sciences, Technische Universität München, Liesel-Beckmann-Str. 1, 85354 Freising, Germany.
| | - Matthias Wüst
- Chair of Food Chemistry, Institute of Nutritional and Food Sciences, University of Bonn, Endenicher Allee 19C, 53115 Bonn, Germany.
| | - Jieren Liao
- Biotechnology of Natural Products, TUM School of Life Sciences, Technische Universität München, Liesel-Beckmann-Str. 1, 85354 Freising, Germany.
| | - Kate McGraphery
- Biotechnology of Natural Products, TUM School of Life Sciences, Technische Universität München, Liesel-Beckmann-Str. 1, 85354 Freising, Germany.
| | - Thomas Hoffmann
- Biotechnology of Natural Products, TUM School of Life Sciences, Technische Universität München, Liesel-Beckmann-Str. 1, 85354 Freising, Germany.
| | - Chuankui Song
- State Key Laboratory of Tea Plant Biology and Utilization, International Joint Laboratory on Tea Chemistry and Health Effects, Anhui Agricultural University Hefei, Anhui 230036, People's Republic of China.
| | - Wilfried Schwab
- Biotechnology of Natural Products, TUM School of Life Sciences, Technische Universität München, Liesel-Beckmann-Str. 1, 85354 Freising, Germany. .,State Key Laboratory of Tea Plant Biology and Utilization, International Joint Laboratory on Tea Chemistry and Health Effects, Anhui Agricultural University Hefei, Anhui 230036, People's Republic of China.
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40
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Structural modeling of two plant UDP-dependent sugar-sugar glycosyltransferases reveals a conserved glutamic acid residue that is a hallmark for sugar acceptor recognition. J Struct Biol 2021; 213:107777. [PMID: 34391905 DOI: 10.1016/j.jsb.2021.107777] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 06/29/2021] [Accepted: 08/04/2021] [Indexed: 12/17/2022]
Abstract
Glycosylation is one of the common modifications of plant metabolites, playing a major role in the chemical/biological diversity of a wide range of compounds. Plant metabolite glycosylation is catalyzed almost exclusively by glycosyltransferases, mainly by Uridine-diphosphate dependent Glycosyltransferases (UGTs). Several X-ray structures have been determined for primary glycosyltransferases, however, little is known regarding structure-function aspects of sugar-sugar/branch-forming O-linked UGTs (SBGTs) that catalyze the transfer of a sugar from the UDP-sugar donor to an acceptor sugar moiety of a previously glycosylated metabolite substrate. In this study we developed novel insights into the structural basis for SBGT catalytic activity by modelling the 3d-structures of two enzymes; a rhamnosyl-transferase Cs1,6RhaT - that catalyzes rhamnosylation of flavonoid-3-glucosides and flavonoid-7-glucosides and a UGT94D1 - that catalyzes glucosylation of (+)-Sesaminol 2-O-β-d-glucoside at the C6 of the primary sugar moiety. Based on these structural models and docking studies a glutamate (E290 or E268 in Cs1,6RhaT or UGT94D1, respectively) and a tryptophan (W28 or W15 in Cs1,6RhaT or UGT94D1, respectively) appear to interact with the sugar acceptor and are suggested to be important for the recognition of the sugar-moiety of the acceptor-substrate. Functional analysis of substitution mutants for the glutamate and tryptophan residues in Cs1,6RhaT further support their role in determining sugar-sugar/branch-forming GT specificity. Phylogenetic analysis of the UGT family in plants demonstrates that the glutamic-acid residue is a hallmark of SBGTs that is entirely absent from the corresponding position in primary UGTs.
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41
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Romero E, Jones BS, Hogg BN, Rué Casamajo A, Hayes MA, Flitsch SL, Turner NJ, Schnepel C. Enzymkatalysierte späte Modifizierungen: Besser spät als nie. ANGEWANDTE CHEMIE (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 133:16962-16993. [PMID: 38505660 PMCID: PMC10946893 DOI: 10.1002/ange.202014931] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 01/15/2021] [Indexed: 03/21/2024]
Abstract
AbstractDie Enzymkatalyse gewinnt zunehmend an Bedeutung in der Synthesechemie. Die durch Bioinformatik und Enzym‐Engineering stetig wachsende Zahl von Biokatalysatoren eröffnet eine große Vielfalt selektiver Reaktionen. Insbesondere für späte Funktionalisierungsreaktionen ist die Biokatalyse ein geeignetes Werkzeug, das oftmals der konventionellen De‐novo‐Synthese überlegen ist. Enzyme haben sich als nützlich erwiesen, um funktionelle Gruppen direkt in komplexe Molekülgerüste einzuführen sowie für die rasche Diversifizierung von Substanzbibliotheken. Biokatalytische Oxyfunktionalisierungen, Halogenierungen, Methylierungen, Reduktionen und Amidierungen sind von besonderem Interesse, da diese Strukturmotive häufig in Pharmazeutika vertreten sind. Dieser Aufsatz gibt einen Überblick über die Stärken und Schwächen der enzymkatalysierten späten Modifizierungen durch native und optimierte Enzyme in der Synthesechemie. Ebenso werden wichtige Beispiele in der Wirkstoffentwicklung hervorgehoben.
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Affiliation(s)
- Elvira Romero
- Compound Synthesis and ManagementDiscovery Sciences, BioPharmaceuticals R&DAstraZenecaGötheborgSchweden
| | - Bethan S. Jones
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNVereinigtes Königreich
| | - Bethany N. Hogg
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNVereinigtes Königreich
| | - Arnau Rué Casamajo
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNVereinigtes Königreich
| | - Martin A. Hayes
- Compound Synthesis and ManagementDiscovery Sciences, BioPharmaceuticals R&DAstraZenecaGötheborgSchweden
| | - Sabine L. Flitsch
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNVereinigtes Königreich
| | - Nicholas J. Turner
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNVereinigtes Königreich
| | - Christian Schnepel
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNVereinigtes Königreich
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Romero E, Jones BS, Hogg BN, Rué Casamajo A, Hayes MA, Flitsch SL, Turner NJ, Schnepel C. Enzymatic Late-Stage Modifications: Better Late Than Never. Angew Chem Int Ed Engl 2021; 60:16824-16855. [PMID: 33453143 PMCID: PMC8359417 DOI: 10.1002/anie.202014931] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 01/15/2021] [Indexed: 12/16/2022]
Abstract
Enzyme catalysis is gaining increasing importance in synthetic chemistry. Nowadays, the growing number of biocatalysts accessible by means of bioinformatics and enzyme engineering opens up an immense variety of selective reactions. Biocatalysis especially provides excellent opportunities for late-stage modification often superior to conventional de novo synthesis. Enzymes have proven to be useful for direct introduction of functional groups into complex scaffolds, as well as for rapid diversification of compound libraries. Particularly important and highly topical are enzyme-catalysed oxyfunctionalisations, halogenations, methylations, reductions, and amide bond formations due to the high prevalence of these motifs in pharmaceuticals. This Review gives an overview of the strengths and limitations of enzymatic late-stage modifications using native and engineered enzymes in synthesis while focusing on important examples in drug development.
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Affiliation(s)
- Elvira Romero
- Compound Synthesis and ManagementDiscovery Sciences, BioPharmaceuticals R&DAstraZenecaGothenburgSweden
| | - Bethan S. Jones
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNUnited Kingdom
| | - Bethany N. Hogg
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNUnited Kingdom
| | - Arnau Rué Casamajo
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNUnited Kingdom
| | - Martin A. Hayes
- Compound Synthesis and ManagementDiscovery Sciences, BioPharmaceuticals R&DAstraZenecaGothenburgSweden
| | - Sabine L. Flitsch
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNUnited Kingdom
| | - Nicholas J. Turner
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNUnited Kingdom
| | - Christian Schnepel
- School of ChemistryThe University of ManchesterManchester Institute of Biotechnology131 Princess StreetManchesterM1 7DNUnited Kingdom
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Ali MY, Chang Q, Yan Q, Qian Z, Guo X, Thow K, Wu J, Zhang Y, Feng Y. Highly Efficient Biosynthesis of Glycyrrhetinic Acid Glucosides by Coupling of Microbial Glycosyltransferase to Plant Sucrose Synthase. Front Bioeng Biotechnol 2021; 9:645079. [PMID: 34169062 PMCID: PMC8218813 DOI: 10.3389/fbioe.2021.645079] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/30/2021] [Indexed: 01/19/2023] Open
Abstract
Glycyrrhetinic acid (GA) is a principal bioactive pentacyclic triterpenoid from Glycyrrhiza uralensis. Uridine diphosphate-dependent glycosyltransferases (UGTs) have been widely used to catalyze glycosylation of diverse nature products for the development of potential therapeutic compounds. In this study, we have characterized a UGT109A3 from Bacillus subtilis, which can glycosylate both the free C3 hydroxyl and C30 carboxyl groups of GA to yield a unique 3, 30-O-β-D-diglucoside-GA. By coupling the microbial UGT109A3 to plant sucrose synthase (SUS), GA-diglucoside could be biosynthesized in an efficient and economical way. With a fed-batch glycosylation, a large scale of GA-diglucoside (6.26 mM, 4.98 g/L in 8 h) could be enzymatically transformed from GA. The obtained GA-diglucoside showed a significant water solubility improvement of around 3.4 × 103 fold compared with that of the parent GA (29 μM). Moreover, it also exhibited dose-dependent cytotoxicity toward human colon carcinoma Caco-2 cell line according to MTT assay, having an IC50 at 160 μM. This study not only establishes efficient platform for producing GA-glucosides, but is also valuable for developing further the biosynthesis of other complex glycosylated natural products.
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Affiliation(s)
- Mohamed Yassin Ali
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Biochemistry Department, Faculty of Agriculture, Fayoum University, Fayoum, Egypt
| | - Qing Chang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Quande Yan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Zheng Qian
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xiang Guo
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Kieran Thow
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jinhong Wu
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Yong Zhang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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44
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Franconetti A, Ardá A, Asensio JL, Blériot Y, Thibaudeau S, Jiménez-Barbero J. Glycosyl Oxocarbenium Ions: Structure, Conformation, Reactivity, and Interactions. Acc Chem Res 2021; 54:2552-2564. [PMID: 33930267 PMCID: PMC8173606 DOI: 10.1021/acs.accounts.1c00021] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Indexed: 12/13/2022]
Abstract
Carbohydrates (glycans, saccharides, and sugars) are essential molecules in all domains of life. Research on glycoscience spans from chemistry to biomedicine, including material science and biotechnology. Access to pure and well-defined complex glycans using synthetic methods depends on the success of the employed glycosylation reaction. In most cases, the mechanism of the glycosylation reaction is believed to involve the oxocarbenium ion. Understanding the structure, conformation, reactivity, and interactions of this glycosyl cation is essential to predict the outcome of the reaction. In this Account, building on our contributions on this topic, we discuss the theoretical and experimental approaches that have been employed to decipher the key features of glycosyl cations, from their structures to their interactions and reactivity.We also highlight that, from a chemical perspective, the glycosylation reaction can be described as a continuum, from unimolecular SN1 with naked oxocarbenium cations as intermediates to bimolecular SN2-type mechanisms, which involve the key role of counterions and donors. All these factors should be considered and are discussed herein. The importance of dissociative mechanisms (involving contact ion pairs, solvent-separated ion pairs, solvent-equilibrated ion pairs) with bimolecular features in most reactions is also highlighted.The role of theoretical calculations to predict the conformation, dynamics, and reactivity of the oxocarbenium ion is also discussed, highlighting the advances in this field that now allow access to the conformational preferences of a variety of oxocarbenium ions and their reactivities under SN1-like conditions.Specifically, the ground-breaking use of superacids to generate these cations is emphasized, since it has permitted characterization of the structure and conformation of a variety of glycosyl oxocarbenium ions in superacid solution by NMR spectroscopy.We also pay special attention to the reactivity of these glycosyl ions, which depends on the conditions, including the counterions, the possible intra- or intermolecular participation of functional groups that may stabilize the cation and the chemical nature of the acceptor, either weak or strong nucleophile. We discuss recent investigations from different experimental perspectives, which identified the involved ionic intermediates, estimating their lifetimes and reactivities and studying their interactions with other molecules. In this context, we also emphasize the relationship between the chemical methods that can be employed to modulate the sensitivity of glycosyl cations and the way in which glycosyl modifying enzymes (glycosyl hydrolases and transferases) build and cleave glycosidic linkages in nature. This comparison provides inspiration on the use of molecules that regulate the stability and reactivity of glycosyl cations.
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Affiliation(s)
- Antonio Franconetti
- CIC
bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, Building
800, 48160 Derio, Spain
| | - Ana Ardá
- CIC
bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, Building
800, 48160 Derio, Spain
- lkerbasque,
Basque Foundation for Science, Maria Diaz de Haro 13, 48009 Bilbao, Spain
| | - Juan Luis Asensio
- Instituto
de Química Orgánica (IQOG-CSIC), Juan de la Cierva 3, 28006 Madrid, Spain
| | - Yves Blériot
- Université
de Poitiers, IC2MP, UMR CNRS
7285, Equipe “OrgaSynth”, 4 rue Michel Brunet, 86073 cedex 9 Poitiers, France
| | - Sébastien Thibaudeau
- Université
de Poitiers, IC2MP, UMR CNRS
7285, Equipe “OrgaSynth”, 4 rue Michel Brunet, 86073 cedex 9 Poitiers, France
| | - Jesús Jiménez-Barbero
- CIC
bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, Building
800, 48160 Derio, Spain
- lkerbasque,
Basque Foundation for Science, Maria Diaz de Haro 13, 48009 Bilbao, Spain
- Department
of Organic Chemistry II, Faculty of Science & Technology, University of the Basque Country, 48940 Leioa, Bizkaia, Spain
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45
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Zhao J, Mo T, Li X, Ding W, Zhang Q. Dissection of the Glycosylation in the Biosynthesis of the Heptadecaglycoside Antibiotic Saccharomicin A. J Org Chem 2021; 86:11117-11124. [PMID: 33724024 DOI: 10.1021/acs.joc.0c03056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Oligosaccharide natural products have diverse biological activities and represent a potentially important source for drug development. In this study, we focus on the glycosylation pathway in the biosynthesis of saccharomicin A (SA-A), an oligosaccharide antibiotic containing 17 sugar moieties. By extensive gene-knockout studies with comparative metabolic profile analysis, we established a complete pathway in assembling the heptadecasaccharide chain of SA-A, the longest saccharide chain found in natural products.
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Affiliation(s)
- Junfeng Zhao
- Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Tianlu Mo
- Department of Chemistry, Fudan University, Shanghai 200433, China.,State Key Laboratory of Microbial Metabolism, School of Life Sciences & Biotechnology Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xinhui Li
- Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Wei Ding
- State Key Laboratory of Microbial Metabolism, School of Life Sciences & Biotechnology Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Zhang
- Department of Chemistry, Fudan University, Shanghai 200433, China
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46
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Promiscuity and specificity of eukaryotic glycosyltransferases. Biochem Soc Trans 2021; 48:891-900. [PMID: 32539082 PMCID: PMC7329348 DOI: 10.1042/bst20190651] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 05/18/2020] [Accepted: 05/20/2020] [Indexed: 02/07/2023]
Abstract
Glycosyltransferases are a large family of enzymes responsible for covalently linking sugar monosaccharides to a variety of organic substrates. These enzymes drive the synthesis of complex oligosaccharides known as glycans, which play key roles in inter-cellular interactions across all the kingdoms of life; they also catalyze sugar attachment during the synthesis of small-molecule metabolites such as plant flavonoids. A given glycosyltransferase enzyme is typically responsible for attaching a specific donor monosaccharide, via a specific glycosidic linkage, to a specific moiety on the acceptor substrate. However these enzymes are often promiscuous, able catalyze linkages between a variety of donors and acceptors. In this review we discuss distinct classes of glycosyltransferase promiscuity, each illustrated by enzymatic examples from small-molecule or glycan synthesis. We highlight the physical causes of promiscuity, and its biochemical consequences. Structural studies of glycosyltransferases involved in glycan synthesis show that they make specific contacts with ‘recognition motifs’ that are much smaller than the full oligosaccharide substrate. There is a wide range in the sizes of glycosyltransferase recognition motifs: highly promiscuous enzymes recognize monosaccharide or disaccharide motifs across multiple oligosaccharides, while highly specific enzymes recognize large, complex motifs found on few oligosaccharides. In eukaryotes, the localization of glycosyltransferases within compartments of the Golgi apparatus may play a role in mitigating the glycan variability caused by enzyme promiscuity.
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47
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Feehan R, Montezano D, Slusky JSG. Machine learning for enzyme engineering, selection and design. Protein Eng Des Sel 2021; 34:gzab019. [PMID: 34296736 PMCID: PMC8299298 DOI: 10.1093/protein/gzab019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 06/18/2021] [Accepted: 06/23/2021] [Indexed: 11/15/2022] Open
Abstract
Machine learning is a useful computational tool for large and complex tasks such as those in the field of enzyme engineering, selection and design. In this review, we examine enzyme-related applications of machine learning. We start by comparing tools that can identify the function of an enzyme and the site responsible for that function. Then we detail methods for optimizing important experimental properties, such as the enzyme environment and enzyme reactants. We describe recent advances in enzyme systems design and enzyme design itself. Throughout we compare and contrast the data and algorithms used for these tasks to illustrate how the algorithms and data can be best used by future designers.
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Affiliation(s)
- Ryan Feehan
- Center for Computational Biology, The University of Kansas, 2030 Becker Dr., Lawrence, KS 66047-1620, USA
| | - Daniel Montezano
- Center for Computational Biology, The University of Kansas, 2030 Becker Dr., Lawrence, KS 66047-1620, USA
| | - Joanna S G Slusky
- Center for Computational Biology, The University of Kansas, 2030 Becker Dr., Lawrence, KS 66047-1620, USA
- Department of Molecular Biosciences, The University of Kansas, 1200 Sunnyside Ave. Lawrence, KS 66045-7600, USA
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48
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Teze D, Coines J, Fredslund F, Dubey KD, Bidart GN, Adams PD, Dueber JE, Svensson B, Rovira C, Welner DH. O-/N-/S-Specificity in Glycosyltransferase Catalysis: From Mechanistic Understanding to Engineering. ACS Catal 2021. [DOI: 10.1021/acscatal.0c04171] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- David Teze
- Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Joan Coines
- Departament de Química Inorgánica i Orgànica and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, Barcelona 08028, Spain
| | - Folmer Fredslund
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Kshatresh D. Dubey
- Departament de Química Inorgánica i Orgànica and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, Barcelona 08028, Spain
| | - Gonzalo N. Bidart
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Paul D. Adams
- Department of Bioengineering, University of California, Berkeley, California 94704, United States
- Joint BioEnergy Institute, Emeryville, California 94608, United States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - John E. Dueber
- Department of Bioengineering, University of California, Berkeley, California 94704, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Birte Svensson
- Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Carme Rovira
- Departament de Química Inorgánica i Orgànica and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, Barcelona 08028, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08018, Spain
| | - Ditte H. Welner
- Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- Joint BioEnergy Institute, Emeryville, California 94608, United States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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49
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Katsimpouras C, Stephanopoulos G. Enzymes in biotechnology: Critical platform technologies for bioprocess development. Curr Opin Biotechnol 2021; 69:91-102. [PMID: 33422914 DOI: 10.1016/j.copbio.2020.12.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 11/09/2020] [Accepted: 12/08/2020] [Indexed: 01/02/2023]
Abstract
Enzymes are core elements of biosynthetic pathways employed in the synthesis of numerous bioproducts. Here, we review enzyme promiscuity, enzyme engineering, enzyme immobilization, and cell-free systems as fundamental strategies of bioprocess development. Initially, promiscuous enzymes are the first candidates in the quest for new activities to power new, artificial, or bypass pathways that expand substrate range and catalyze the production of new products. If the activity or regulation of available enzymes is unsuitable for a process, protein engineering can be applied to improve them to the required level. When cell toxicity and low productivity cannot be engineered away, cell-free systems are an attractive option, especially in combination with enzyme immobilization that allows extended enzyme use. Overall, the above methods support powerful platforms for bioprocess development and optimization.
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Affiliation(s)
- Constantinos Katsimpouras
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139 MA, USA
| | - Gregory Stephanopoulos
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139 MA, USA.
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50
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Moulis C, Guieysse D, Morel S, Séverac E, Remaud-Siméon M. Natural and engineered transglycosylases: Green tools for the enzyme-based synthesis of glycoproducts. Curr Opin Chem Biol 2020; 61:96-106. [PMID: 33360622 DOI: 10.1016/j.cbpa.2020.11.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/16/2020] [Accepted: 11/19/2020] [Indexed: 01/22/2023]
Abstract
An increasing number of transglycosylase-based processes provide access to oligosaccharides or glycoconjugates, some of them reaching performance levels compatible with industrial developments. Nevertheless, the full potential of transglycosylases has not been explored because of the challenges in transforming a glycoside hydrolase into an efficient transglycosylase. Advances in studying enzyme structure/function relationships, screening enzyme activity, and generating synthetic libraries guided by computational protein design or machine learning methods should considerably accelerate the development of these catalysts. The time has now come for researchers to uncover their possibilities and learn how to design and precisely refine their activity to respond more rapidly to the growing demand for well-defined glycosidic structures.
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Affiliation(s)
- Claire Moulis
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, Toulouse, Cedex 04, F-31077, France.
| | - David Guieysse
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, Toulouse, Cedex 04, F-31077, France
| | - Sandrine Morel
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, Toulouse, Cedex 04, F-31077, France
| | - Etienne Séverac
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, Toulouse, Cedex 04, F-31077, France
| | - Magali Remaud-Siméon
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 135, Avenue de Rangueil, Toulouse, Cedex 04, F-31077, France.
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