1
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Fansher D, Besna JN, Fendri A, Pelletier JN. Choose Your Own Adventure: A Comprehensive Database of Reactions Catalyzed by Cytochrome P450 BM3 Variants. ACS Catal 2024; 14:5560-5592. [PMID: 38660610 PMCID: PMC11036407 DOI: 10.1021/acscatal.4c00086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 04/26/2024]
Abstract
Cytochrome P450 BM3 monooxygenase is the topic of extensive research as many researchers have evolved this enzyme to generate a variety of products. However, the abundance of information on increasingly diversified variants of P450 BM3 that catalyze a broad array of chemistry is not in a format that enables easy extraction and interpretation. We present a database that categorizes variants by their catalyzed reactions and includes details about substrates to provide reaction context. This database of >1500 P450 BM3 variants is downloadable and machine-readable and includes instructions to maximize ease of gathering information. The database allows rapid identification of commonly reported substitutions, aiding researchers who are unfamiliar with the enzyme in identifying starting points for enzyme engineering. For those actively engaged in engineering P450 BM3, the database, along with this review, provides a powerful and user-friendly platform to understand, predict, and identify the attributes of P450 BM3 variants, encouraging the further engineering of this enzyme.
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Affiliation(s)
- Douglas
J. Fansher
- Chemistry
Department, Université de Montréal, Montreal, QC, Canada H2V 0B3
- PROTEO,
The Québec Network for Research on Protein Function, Engineering,
and Applications, 201
Av. du Président-Kennedy, Montréal, QC, Canada H2X 3Y7
- CGCC,
Center in Green Chemistry and Catalysis, Montreal, QC, Canada H2V 0B3
| | - Jonathan N. Besna
- PROTEO,
The Québec Network for Research on Protein Function, Engineering,
and Applications, 201
Av. du Président-Kennedy, Montréal, QC, Canada H2X 3Y7
- CGCC,
Center in Green Chemistry and Catalysis, Montreal, QC, Canada H2V 0B3
- Department
of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada H3T 1J4
| | - Ali Fendri
- Chemistry
Department, Université de Montréal, Montreal, QC, Canada H2V 0B3
- PROTEO,
The Québec Network for Research on Protein Function, Engineering,
and Applications, 201
Av. du Président-Kennedy, Montréal, QC, Canada H2X 3Y7
- CGCC,
Center in Green Chemistry and Catalysis, Montreal, QC, Canada H2V 0B3
| | - Joelle N. Pelletier
- Chemistry
Department, Université de Montréal, Montreal, QC, Canada H2V 0B3
- PROTEO,
The Québec Network for Research on Protein Function, Engineering,
and Applications, 201
Av. du Président-Kennedy, Montréal, QC, Canada H2X 3Y7
- CGCC,
Center in Green Chemistry and Catalysis, Montreal, QC, Canada H2V 0B3
- Department
of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada H3T 1J4
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2
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Karbhal R, Sawant S, Kulkarni-Kale U. iCEED: Integrated customized extraction of enzyme data. J Bioinform Comput Biol 2024; 22:2450005. [PMID: 38779780 DOI: 10.1142/s0219720024500057] [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] [Indexed: 05/25/2024]
Abstract
Enzymes catalyze diverse biochemical reactions and are building blocks of cellular and metabolic pathways. Data and metadata of enzymes are distributed across databases and are archived in various formats. The enzyme databases provide utilities for efficient searches and downloading enzyme records in batch mode but do not support organism-specific extraction of subsets of data. Users are required to write scripts for parsing entries for customized data extraction prior to downstream analysis. Integrated Customized Extraction of Enzyme Data (iCEED) has been developed to provide organism-specific customized data extraction utilities for seven commonly used enzyme databases and brings these resources under an integrated portal. iCEED provides dropdown menus and search boxes using typehead utility for submission of queries as well as enzyme class-based browsing utility. A utility to facilitate mapping and visualization of functionally important features on the three-dimensional (3D) structures of enzymes is integrated. The customized data extraction utilities provided in iCEED are expected to be useful for biochemists, biotechnologists, computational biologists, and life science researchers to build curated datasets of their choice through an easy to navigate web-based interface. The integrated feature visualization system is useful for a fine-grained understanding of the enzyme structure-function relationship. Desired subsets of data, extracted and curated using iCEED can be subsequently used for downstream processing, analyses, and knowledge discovery. iCEED can also be used for training and teaching purposes.
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Affiliation(s)
- Rajiv Karbhal
- Bioinformatics Centre, Savitribai Phule Pune University, Pune 411007, India
| | - Sangeeta Sawant
- Bioinformatics Centre, Savitribai Phule Pune University, Pune 411007, India
| | - Urmila Kulkarni-Kale
- Bioinformatics Centre, Savitribai Phule Pune University, Pune 411007, India
- Department of Natural Sciences and Environmental Health, University of Southeastern Norway, Gullbringvegen 36, 3800 Bø, Norway
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3
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Hu M, Suthers PF, Maranas CD. KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states. Metab Eng 2024; 82:123-133. [PMID: 38336004 DOI: 10.1016/j.ymben.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/24/2024] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.
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Affiliation(s)
- Mengqi Hu
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.
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4
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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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5
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Prešern U, Goličnik M. Enzyme Databases in the Era of Omics and Artificial Intelligence. Int J Mol Sci 2023; 24:16918. [PMID: 38069254 PMCID: PMC10707154 DOI: 10.3390/ijms242316918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023] Open
Abstract
Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.
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Affiliation(s)
| | - Marko Goličnik
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia;
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6
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Finnigan W, Lubberink M, Hepworth LJ, Citoler J, Mattey AP, Ford GJ, Sangster J, Cosgrove SC, da Costa BZ, Heath RS, Thorpe TW, Yu Y, Flitsch SL, Turner NJ. RetroBioCat Database: A Platform for Collaborative Curation and Automated Meta-Analysis of Biocatalysis Data. ACS Catal 2023; 13:11771-11780. [PMID: 37671181 PMCID: PMC10476152 DOI: 10.1021/acscatal.3c01418] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 06/26/2023] [Indexed: 09/07/2023]
Abstract
Despite the increasing use of biocatalysis for organic synthesis, there are currently no databases that adequately capture synthetic biotransformations. The lack of a biocatalysis database prevents accelerating biocatalyst characterization efforts from being leveraged to quickly identify candidate enzymes for reactions or cascades, slowing their development. The RetroBioCat Database (available at retrobiocat.com) addresses this gap by capturing information on synthetic biotransformations and providing an analysis platform that allows biocatalysis data to be searched and explored through a range of highly interactive data visualization tools. This database makes it simple to explore available enzymes, their substrate scopes, and how characterized enzymes are related to each other and the wider sequence space. Data entry is facilitated through an openly accessible curation platform, featuring automated tools to accelerate the process. The RetroBioCat Database democratizes biocatalysis knowledge and has the potential to accelerate biocatalytic reaction development, making it a valuable resource for the community.
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Affiliation(s)
- William Finnigan
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
| | | | - Lorna J. Hepworth
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Joan Citoler
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Ashley P. Mattey
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Grayson J. Ford
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Jack Sangster
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
| | | | - Bruna Zucoloto da Costa
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Rachel S. Heath
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
| | | | - Yuqi Yu
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Sabine L. Flitsch
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Nicholas J. Turner
- Department of Chemistry, Manchester Institute of Biotechnology, University
of Manchester, 131 Princess Street, Manchester M1 7DN, U.K.
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7
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Giess T, Itzigehl S, Range J, Schömig R, Bruckner JR, Pleiss J. FAIR and scalable management of small-angle X-ray scattering data. J Appl Crystallogr 2023; 56:565-575. [PMID: 37032968 PMCID: PMC10077856 DOI: 10.1107/s1600576723001577] [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: 09/19/2022] [Accepted: 02/21/2023] [Indexed: 04/07/2023] Open
Abstract
A modular and extensible research data management toolbox based on the programming language Python and the widely used computing platform Jupyter Notebook has been established for the acquisition, visualization, analysis and storage of small-angle X-ray scattering data. A modular research data management toolbox based on the programming language Python, the widely used computing platform Jupyter Notebook, the standardized data exchange format for analytical data (AnIML) and the generic repository Dataverse has been established and applied to analyze small-angle X-ray scattering (SAXS) data according to the FAIR data principles (findable, accessible, interoperable and reusable). The SAS-tools library is a community-driven effort to develop tools for data acquisition, analysis, visualization and publishing of SAXS data. Metadata from the experiment and the results of data analysis are stored as an AnIML document using the novel Python-native pyAnIML API. The AnIML document, measured raw data and plots resulting from the analysis are combined into an archive in OMEX format and uploaded to Dataverse using the novel easyDataverse API, which makes each data set accessible via a unique DOI and searchable via a structured metadata block. SAS-tools is applied to study the effects of alkyl chain length and counterions on the phase diagrams of alkyltrimethylammonium surfactants in order to demonstrate the feasibility and usefulness of a scalable data management workflow for experiments in physical chemistry.
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Affiliation(s)
- Torsten Giess
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Selina Itzigehl
- Institute of Physical Chemistry, University of Stuttgart, Pfaffenwaldring 55, Stuttgart 70569, Germany
| | - Jan Range
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Richard Schömig
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Johanna R. Bruckner
- Institute of Physical Chemistry, University of Stuttgart, Pfaffenwaldring 55, Stuttgart 70569, Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
- Correspondence e-mail:
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8
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Lauterbach S, Dienhart H, Range J, Malzacher S, Spöring JD, Rother D, Pinto MF, Martins P, Lagerman CE, Bommarius AS, Høst AV, Woodley JM, Ngubane S, Kudanga T, Bergmann FT, Rohwer JM, Iglezakis D, Weidemann A, Wittig U, Kettner C, Swainston N, Schnell S, Pleiss J. EnzymeML: seamless data flow and modeling of enzymatic data. Nat Methods 2023; 20:400-402. [PMID: 36759590 DOI: 10.1038/s41592-022-01763-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 12/21/2022] [Indexed: 02/11/2023]
Abstract
The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .
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Affiliation(s)
- Simone Lauterbach
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Hannah Dienhart
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Jan Range
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Stephan Malzacher
- Institute of Bio- and Geosciences 1, Forschungszentrum Jülich, Jülich, Germany.,Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Jan-Dirk Spöring
- Institute of Bio- and Geosciences 1, Forschungszentrum Jülich, Jülich, Germany.,Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Dörte Rother
- Institute of Bio- and Geosciences 1, Forschungszentrum Jülich, Jülich, Germany.,Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Maria Filipa Pinto
- i3S, Instituto de Investigação e Inovação em Saúde da Universidade do Porto, University of Porto, Porto, Portugal
| | - Pedro Martins
- i3S, Instituto de Investigação e Inovação em Saúde da Universidade do Porto, University of Porto, Porto, Portugal
| | - Colton E Lagerman
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Andreas S Bommarius
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Amalie Vang Høst
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs Lyngby, Denmark
| | - John M Woodley
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs Lyngby, Denmark
| | - Sandile Ngubane
- Department of Biotechnology and Food Science, Durban University of Technology, Durban, South Africa
| | - Tukayi Kudanga
- Department of Biotechnology and Food Science, Durban University of Technology, Durban, South Africa
| | | | - Johann M Rohwer
- Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa
| | - Dorothea Iglezakis
- Information and Communication Center, University of Stuttgart, Stuttgart, Germany
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | | | - Neil Swainston
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Santiago Schnell
- Department of Biological Sciences and Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany.
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9
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Yan B, Ran X, Gollu A, Cheng Z, Zhou X, Chen Y, Yang ZJ. IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database. J Chem Inf Model 2022; 62:5841-5848. [PMID: 36286319 DOI: 10.1021/acs.jcim.2c01139] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Data-driven modeling has emerged as a new paradigm for biocatalyst design and discovery. Biocatalytic databases that integrate enzyme structure and function data are in urgent need. Here we describe IntEnzyDB as an integrated structure-kinetics database for facile statistical modeling and machine learning. IntEnzyDB employs a relational database architecture with a flattened data structure, which allows rapid data operation. This architecture also makes it easy for IntEnzyDB to incorporate more types of enzyme function data. IntEnzyDB contains enzyme kinetics and structure data from six enzyme commission classes. Using 1050 enzyme structure-kinetics pairs, we investigated the efficiency-perturbing propensities of mutations that are close or distal to the active site. The statistical results show that efficiency-enhancing mutations are globally encoded and that deleterious mutations are much more likely to occur in close mutations than in distal mutations. Finally, we describe a web interface that allows public users to access enzymology data stored in IntEnzyDB. IntEnzyDB will provide a computational facility for data-driven modeling in biocatalysis and molecular evolution.
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Affiliation(s)
- Bailu Yan
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.,Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37205, United States
| | - Xinchun Ran
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Anvita Gollu
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Zihao Cheng
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Xiang Zhou
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yiwen Chen
- Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Zhongyue J Yang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States.,Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States.,Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37205, United States
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10
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Menke MJ, Behr AS, Rosenthal K, Linke D, Kockmann N, Bornscheuer UT, Dörr M. Development of an Ontology for Biocatalysis. CHEM-ING-TECH 2022. [DOI: 10.1002/cite.202200066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Marian J. Menke
- University of Greifswald Dept. of Biotechnology & Enzyme Catalysis Felix-Hausdorff-Strasse 4 17487 Greifswald Germany
| | - Alexander S. Behr
- TU Dortmund University Department of Biochemical and Chemical Engineering Laboratory of Equipment Design Emil-Figge-Strasse 68 44227 Dortmund Germany
| | - Katrin Rosenthal
- TU Dortmund University Department of Biochemical and Chemical Engineering Chair for Bioprocess Engineering Emil-Figge-Strasse 66 44227 Dortmund Germany
| | - David Linke
- Leibniz-Institut für Katalyse e. V. Albert-Einstein-Strasse 29A 18059 Rostock Germany
| | - Norbert Kockmann
- TU Dortmund University Department of Biochemical and Chemical Engineering Laboratory of Equipment Design Emil-Figge-Strasse 68 44227 Dortmund Germany
| | - Uwe T. Bornscheuer
- University of Greifswald Dept. of Biotechnology & Enzyme Catalysis Felix-Hausdorff-Strasse 4 17487 Greifswald Germany
| | - Mark Dörr
- University of Greifswald Dept. of Biotechnology & Enzyme Catalysis Felix-Hausdorff-Strasse 4 17487 Greifswald Germany
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11
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Pei X, Luo Z, Qiao L, Xiao Q, Zhang P, Wang A, Sheldon RA. Putting precision and elegance in enzyme immobilisation with bio-orthogonal chemistry. Chem Soc Rev 2022; 51:7281-7304. [PMID: 35920313 DOI: 10.1039/d1cs01004b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The covalent immobilisation of enzymes generally involves the use of highly reactive crosslinkers, such as glutaraldehyde, to couple enzyme molecules to each other or to carriers through, for example, the free amino groups of lysine residues, on the enzyme surface. Unfortunately, such methods suffer from a lack of precision. Random formation of covalent linkages with reactive functional groups in the enzyme leads to disruption of the three dimensional structure and accompanying activity losses. This review focuses on recent advances in the use of bio-orthogonal chemistry in conjunction with rec-DNA to affect highly precise immobilisation of enzymes. In this way, cost-effective combination of production, purification and immobilisation of an enzyme is achieved, in a single unit operation with a high degree of precision. Various bio-orthogonal techniques for putting this precision and elegance into enzyme immobilisation are elaborated. These include, for example, fusing (grafting) peptide or protein tags to the target enzyme that enable its immobilisation in cell lysate or incorporating non-standard amino acids that enable the application of bio-orthogonal chemistry.
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Affiliation(s)
- Xiaolin Pei
- College of Materials, Chemistry and Chemical Engineering, Key Laboratory of Organosilicon Chemistry and Material Technology, Ministry of Education, Key Laboratory of Organosilicon Material Technology, Hangzhou Normal University, Zhejiang Province, Hangzhou, 311121, Zhejiang, P. R. China
| | - Zhiyuan Luo
- College of Materials, Chemistry and Chemical Engineering, Key Laboratory of Organosilicon Chemistry and Material Technology, Ministry of Education, Key Laboratory of Organosilicon Material Technology, Hangzhou Normal University, Zhejiang Province, Hangzhou, 311121, Zhejiang, P. R. China
| | - Li Qiao
- College of Materials, Chemistry and Chemical Engineering, Key Laboratory of Organosilicon Chemistry and Material Technology, Ministry of Education, Key Laboratory of Organosilicon Material Technology, Hangzhou Normal University, Zhejiang Province, Hangzhou, 311121, Zhejiang, P. R. China
| | - Qinjie Xiao
- College of Materials, Chemistry and Chemical Engineering, Key Laboratory of Organosilicon Chemistry and Material Technology, Ministry of Education, Key Laboratory of Organosilicon Material Technology, Hangzhou Normal University, Zhejiang Province, Hangzhou, 311121, Zhejiang, P. R. China
| | - Pengfei Zhang
- College of Materials, Chemistry and Chemical Engineering, Key Laboratory of Organosilicon Chemistry and Material Technology, Ministry of Education, Key Laboratory of Organosilicon Material Technology, Hangzhou Normal University, Zhejiang Province, Hangzhou, 311121, Zhejiang, P. R. China
| | - Anming Wang
- College of Materials, Chemistry and Chemical Engineering, Key Laboratory of Organosilicon Chemistry and Material Technology, Ministry of Education, Key Laboratory of Organosilicon Material Technology, Hangzhou Normal University, Zhejiang Province, Hangzhou, 311121, Zhejiang, P. R. China
| | - Roger A Sheldon
- Molecular Sciences Institute, School of Chemistry, University of the Witwatersrand, PO Wits, 2050, Johannesburg, South Africa. .,Department of Biotechnology, Section BOC, Delft University of Technology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
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Enzyme Immobilization and Co-Immobilization: Main Framework, Advances and Some Applications. Processes (Basel) 2022. [DOI: 10.3390/pr10030494] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Enzymes are outstanding (bio)catalysts, not solely on account of their ability to increase reaction rates by up to several orders of magnitude but also for the high degree of substrate specificity, regiospecificity and stereospecificity. The use and development of enzymes as robust biocatalysts is one of the main challenges in biotechnology. However, despite the high specificities and turnover of enzymes, there are also drawbacks. At the industrial level, these drawbacks are typically overcome by resorting to immobilized enzymes to enhance stability. Immobilization of biocatalysts allows their reuse, increases stability, facilitates process control, eases product recovery, and enhances product yield and quality. This is especially important for expensive enzymes, for those obtained in low fermentation yield and with relatively low activity. This review provides an integrated perspective on (multi)enzyme immobilization that abridges a critical evaluation of immobilization methods and carriers, biocatalyst metrics, impact of key carrier features on biocatalyst performance, trends towards miniaturization and detailed illustrative examples that are representative of biocatalytic applications promoting sustainability.
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