1
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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: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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2
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Range J, Halupczok C, Lohmann J, Swainston N, Kettner C, Bergmann FT, Weidemann A, Wittig U, Schnell S, Pleiss J. EnzymeML-a data exchange format for biocatalysis and enzymology. FEBS J 2021; 289:5864-5874. [PMID: 34890097 DOI: 10.1111/febs.16318] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/15/2021] [Accepted: 12/09/2021] [Indexed: 11/30/2022]
Abstract
EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.
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Affiliation(s)
- Jan Range
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
| | - Colin Halupczok
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
| | - Jens Lohmann
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
| | - Neil Swainston
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | | | | | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies, Germany
| | - Santiago Schnell
- Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
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3
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Shrivastava AD, Swainston N, Samanta S, Roberts I, Wright Muelas M, Kell DB. MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra. Biomolecules 2021; 11:1793. [PMID: 34944436 PMCID: PMC8699281 DOI: 10.3390/biom11121793] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/14/2021] [Accepted: 11/27/2021] [Indexed: 12/15/2022] Open
Abstract
The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the 'translation' a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the 'true' molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are 'similar' to the top hit. In addition to using the 'top hits' directly, we can produce a rank order of these by 'round-tripping' candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to 'learn' millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.
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Affiliation(s)
- Aditya Divyakant Shrivastava
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
- Department of Computer Science and Engineering, Nirma University, Ahmedabad 382481, India
| | - Neil Swainston
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
- Mellizyme Biotechnology Ltd., Liverpool Science Park IC1, 131 Mount Pleasant, Liverpool L3 5TF, UK
| | - Soumitra Samanta
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
| | - Ivayla Roberts
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
| | - Marina Wright Muelas
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
| | - Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (A.D.S.); (N.S.); (S.S.); (I.R.); (M.W.M.)
- Mellizyme Biotechnology Ltd., Liverpool Science Park IC1, 131 Mount Pleasant, Liverpool L3 5TF, UK
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kongens Lyngby, Denmark
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Yeoh JW, Swainston N, Vegh P, Zulkower V, Carbonell P, Holowko MB, Peddinti G, Poh CL. SynBiopython: an open-source software library for Synthetic Biology. Synth Biol (Oxf) 2021. [PMCID: PMC8063678 DOI: 10.1093/synbio/ysab001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.
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Affiliation(s)
- Jing Wui Yeoh
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Neil Swainston
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Peter Vegh
- Edinburgh Genome Foundry, University of Edinburgh, Edinburgh, UK
| | | | - Pablo Carbonell
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Maciej B Holowko
- CSIRO Synthetic Biology Future Science Platform, Canberra, ACT, Australia
| | - Gopal Peddinti
- VTT Technical Research Center of Finland, Espoo, Finland
| | - Chueh Loo Poh
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
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5
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Kell DB, Samanta S, Swainston N. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. Biochem J 2020; 477:4559-4580. [PMID: 33290527 PMCID: PMC7733676 DOI: 10.1042/bcj20200781] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/11/2020] [Accepted: 11/12/2020] [Indexed: 12/15/2022]
Abstract
The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.
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Affiliation(s)
- Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Soumitra Samanta
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
| | - Neil Swainston
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
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6
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Khemchandani Y, O'Hagan S, Samanta S, Swainston N, Roberts TJ, Bollegala D, Kell DB. DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach. J Cheminform 2020; 12:53. [PMID: 33431037 PMCID: PMC7487898 DOI: 10.1186/s13321-020-00454-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 08/18/2020] [Indexed: 02/03/2023] Open
Abstract
We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.
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Affiliation(s)
- Yash Khemchandani
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool, L69 7ZB, UK
- Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, 400 076, India
| | - Stephen O'Hagan
- Dept of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN, UK
| | - Soumitra Samanta
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool, L69 7ZB, UK
| | - Neil Swainston
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool, L69 7ZB, UK
| | - Timothy J Roberts
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool, L69 7ZB, UK
| | - Danushka Bollegala
- Dept of Computer Science, University of Liverpool, Ashton Building, Ashton Street, Liverpool, L69 3BX, UK
| | - Douglas B Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool, L69 7ZB, UK.
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 200, Kgs, 2800, Lyngby, Denmark.
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Doçi G, Fuchs L, Kharbanda Y, Schickling P, Zulkower V, Hillson N, Oberortner E, Swainston N, Kabisch J. DNA Scanner: a web application for comparing DNA synthesis feasibility, price and turnaround time across vendors. Synth Biol (Oxf) 2020. [DOI: 10.1093/synbio/ysaa011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
DNA synthesis has become a major enabler of modern bioengineering, allowing scientists to simply order online in silico-designed DNA molecules. Rapidly decreasing DNA synthesis service prices and the concomitant increase of research and development scales bolstered by computer-aided DNA design tools and laboratory automation has driven up the demand for synthetic DNA. While vendors provide user-friendly online portals for purchasing synthetic DNA, customers still face the time-consuming task of checking each vendor of choice for their ability and pricing to synthesize the desired sequences. As a result, ordering large batches of DNA sequences can be a laborious manual procedure in an otherwise increasingly automatable workflow. Even when they are available, there is a high degree of technical knowledge and effort required to integrate vendors’ application programming interfaces (APIs) into computer-aided DNA design tools or automated lab processes. Here, we introduce DNA Scanner, a software package comprising (i) a web-based user interface enabling users to compare the feasibility, price and turnaround time of synthetic DNA sequences across selected vendors and (ii) a Python API enabling integration of these functionalities into computer-aided DNA design tools and automated lab processes. We have developed DNA Scanner to uniformly streamline interactions between synthetic DNA vendors, members of the Global Biofoundry Alliance and the scientific community at large.
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Affiliation(s)
- Gledon Doçi
- Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany
| | - Lukas Fuchs
- Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany
| | - Yash Kharbanda
- Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany
| | - Paul Schickling
- Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany
| | - Valentin Zulkower
- Edinburgh Genome Foundry, SynthSys, School of Biological Sciences, University of Edinburgh, EH9 3BF, Edinburgh, UK
- Software Working Group, Global Biofoundries Alliance
| | - Nathan Hillson
- Software Working Group, Global Biofoundries Alliance
- DOE Agile BioFoundry, Emeryville, CA 94608, USA
- DOE Joint Genome Institute, Berkeley, CA 94720, USA
- Biological Systems and Engineering, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA
| | - Ernst Oberortner
- Software Working Group, Global Biofoundries Alliance
- DOE Joint Genome Institute, Berkeley, CA 94720, USA
- Environmental Genomics and Systems Biology Divisions, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA
| | - Neil Swainston
- Software Working Group, Global Biofoundries Alliance
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Johannes Kabisch
- Software Working Group, Global Biofoundries Alliance
- Computer-aided Synthetic Biology, Technical University Darmstadt, Darmstadt, Germany
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt, Germany
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8
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Dunstan MS, Robinson CJ, Jervis AJ, Yan C, Carbonell P, Hollywood KA, Currin A, Swainston N, Feuvre RL, Micklefield J, Faulon JL, Breitling R, Turner N, Takano E, Scrutton NS. Engineering Escherichia coli towards de novo production of gatekeeper (2 S)-flavanones: naringenin, pinocembrin, eriodictyol and homoeriodictyol. Synth Biol (Oxf) 2020; 5:ysaa012. [PMID: 33195815 PMCID: PMC7644443 DOI: 10.1093/synbio/ysaa012] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/15/2020] [Accepted: 07/22/2020] [Indexed: 11/13/2022] Open
Abstract
Natural plant-based flavonoids have drawn significant attention as dietary supplements due to their potential health benefits, including anti-cancer, anti-oxidant and anti-asthmatic activities. Naringenin, pinocembrin, eriodictyol and homoeriodictyol are classified as (2S)-flavanones, an important sub-group of naturally occurring flavonoids, with wide-reaching applications in human health and nutrition. These four compounds occupy a central position as branch point intermediates towards a broad spectrum of naturally occurring flavonoids. Here, we report the development of Escherichia coli production chassis for each of these key gatekeeper flavonoids. Selection of key enzymes, genetic construct design and the optimization of process conditions resulted in the highest reported titers for naringenin (484 mg/l), improved production of pinocembrin (198 mg/l) and eriodictyol (55 mg/l from caffeic acid), and provided the first example of in vivo production of homoeriodictyol directly from glycerol (17 mg/l). This work provides a springboard for future production of diverse downstream natural and non-natural flavonoid targets.
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Affiliation(s)
- Mark S Dunstan
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Christopher J Robinson
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Adrian J Jervis
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Cunyu Yan
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Pablo Carbonell
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Katherine A Hollywood
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Andrew Currin
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Neil Swainston
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Rosalind Le Feuvre
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Jason Micklefield
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Jean-Loup Faulon
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
- MICALIS, INRA-AgroParisTech, Domaine de Vilvert, 78352 Jouy en Josas Cedex, France
| | - Rainer Breitling
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Nicholas Turner
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Eriko Takano
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Nigel S Scrutton
- Manchester aaSynthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester M1 7DN, UK
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9
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Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik‐Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier‐Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, Hucka M. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol 2020; 16:e9110. [PMID: 32845085 PMCID: PMC8411907 DOI: 10.15252/msb.20199110] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 06/24/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022] Open
Abstract
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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Samanta S, O’Hagan S, Swainston N, Roberts TJ, Kell DB. VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder. Molecules 2020; 25:E3446. [PMID: 32751155 PMCID: PMC7435890 DOI: 10.3390/molecules25153446] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/21/2020] [Accepted: 07/28/2020] [Indexed: 01/13/2023] Open
Abstract
Molecular similarity is an elusive but core "unsupervised" cheminformatics concept, yet different "fingerprint" encodings of molecular structures return very different similarity values, even when using the same similarity metric. Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none are "better" than the others, nor than encoding-free metrics such as maximum common substructure (MCSS). We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE). This learns the joint distribution p(z|x) where z is a latent vector and x are the (same) input/output data. It takes the form of a "bowtie"-shaped artificial neural network. In the middle is a "bottleneck layer" or latent vector in which inputs are transformed into, and represented as, a vector of numbers (encoding), with a reverse process (decoding) seeking to return the SMILES string that was the input. We train a VAE on over six million druglike molecules and natural products (including over one million in the final holdout set). The VAE vector distances provide a rapid and novel metric for molecular similarity that is both easily and rapidly calculated. We describe the method and its application to a typical similarity problem in cheminformatics.
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Affiliation(s)
- Soumitra Samanta
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (S.S.); (N.S.); (T.J.R.)
| | - Steve O’Hagan
- Department of Chemistry, The Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester M1 7DN, UK;
| | - Neil Swainston
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (S.S.); (N.S.); (T.J.R.)
| | - Timothy J. Roberts
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (S.S.); (N.S.); (T.J.R.)
| | - Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK; (S.S.); (N.S.); (T.J.R.)
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs Lyngby, Denmark
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11
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Robinson CJ, Carbonell P, Jervis AJ, Yan C, Hollywood KA, Dunstan MS, Currin A, Swainston N, Spiess R, Taylor S, Mulherin P, Parker S, Rowe W, Matthews NE, Malone KJ, Le Feuvre R, Shapira P, Barran P, Turner NJ, Micklefield J, Breitling R, Takano E, Scrutton NS. Rapid prototyping of microbial production strains for the biomanufacture of potential materials monomers. Metab Eng 2020; 60:168-182. [PMID: 32335188 PMCID: PMC7225752 DOI: 10.1016/j.ymben.2020.04.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 04/09/2020] [Accepted: 04/16/2020] [Indexed: 12/11/2022]
Abstract
Bio-based production of industrial chemicals using synthetic biology can provide alternative green routes from renewable resources, allowing for cleaner production processes. To efficiently produce chemicals on-demand through microbial strain engineering, biomanufacturing foundries have developed automated pipelines that are largely compound agnostic in their time to delivery. Here we benchmark the capabilities of a biomanufacturing pipeline to enable rapid prototyping of microbial cell factories for the production of chemically diverse industrially relevant material building blocks. Over 85 days the pipeline was able to produce 17 potential material monomers and key intermediates by combining 160 genetic parts into 115 unique biosynthetic pathways. To explore the scale-up potential of our prototype production strains, we optimized the enantioselective production of mandelic acid and hydroxymandelic acid, achieving gram-scale production in fed-batch fermenters. The high success rate in the rapid design and prototyping of microbially-produced material building blocks reveals the potential role of biofoundries in leading the transition to sustainable materials production.
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Affiliation(s)
- Christopher J Robinson
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Pablo Carbonell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Adrian J Jervis
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Cunyu Yan
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Katherine A Hollywood
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Mark S Dunstan
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Andrew Currin
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Reynard Spiess
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Sandra Taylor
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Paul Mulherin
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Steven Parker
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - William Rowe
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Nicholas E Matthews
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Manchester Institute of Innovation Research, Alliance Manchester Business School, The University of Manchester, Manchester, M15 6PB, UK.
| | - Kirk J Malone
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Rosalind Le Feuvre
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Philip Shapira
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Manchester Institute of Innovation Research, Alliance Manchester Business School, The University of Manchester, Manchester, M15 6PB, UK.
| | - Perdita Barran
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
| | - Nicholas J Turner
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
| | - Jason Micklefield
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
| | - Rainer Breitling
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
| | - Eriko Takano
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
| | - Nigel S Scrutton
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
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12
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Superti-Furga G, Lackner D, Wiedmer T, Ingles-Prieto A, Barbosa B, Girardi E, Goldmann U, Gürtl B, Klavins K, Klimek C, Lindinger S, Liñeiro-Retes E, Müller AC, Onstein S, Redinger G, Reil D, Sedlyarov V, Wolf G, Crawford M, Everley R, Hepworth D, Liu S, Noell S, Piotrowski M, Stanton R, Zhang H, Corallino S, Faedo A, Insidioso M, Maresca G, Redaelli L, Sassone F, Scarabottolo L, Stucchi M, Tarroni P, Tremolada S, Batoulis H, Becker A, Bender E, Chang YN, Ehrmann A, Müller-Fahrnow A, Pütter V, Zindel D, Hamilton B, Lenter M, Santacruz D, Viollet C, Whitehurst C, Johnsson K, Leippe P, Baumgarten B, Chang L, Ibig Y, Pfeifer M, Reinhardt J, Schönbett J, Selzer P, Seuwen K, Bettembourg C, Biton B, Czech J, de Foucauld H, Didier M, Licher T, Mikol V, Pommereau A, Puech F, Yaligara V, Edwards A, Bongers BJ, Heitman LH, IJzerman AP, Sijben HJ, van Westen GJ, Grixti J, Kell DB, Mughal F, Swainston N, Wright-Muelas M, Bohstedt T, Burgess-Brown N, Carpenter L, Dürr K, Hansen J, Scacioc A, Banci G, Colas C, Digles D, Ecker G, Füzi B, Gamsjäger V, Grandits M, Martini R, Troger F, Altermatt P, Doucerain C, Dürrenberger F, Manolova V, Steck AL, Sundström H, Wilhelm M, Steppan CM. The RESOLUTE consortium: unlocking SLC transporters for drug discovery. Nat Rev Drug Discov 2020; 19:429-430. [DOI: 10.1038/d41573-020-00056-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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13
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Currin A, Swainston N, Dunstan MS, Jervis AJ, Mulherin P, Robinson CJ, Taylor S, Carbonell P, Hollywood KA, Yan C, Takano E, Scrutton NS, Breitling R. Highly multiplexed, fast and accurate nanopore sequencing for verification of synthetic DNA constructs and sequence libraries. Synth Biol (Oxf) 2019; 4:ysz025. [PMID: 32995546 PMCID: PMC7445882 DOI: 10.1093/synbio/ysz025] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/01/2019] [Accepted: 10/03/2019] [Indexed: 01/09/2023] Open
Abstract
Synthetic biology utilizes the Design-Build-Test-Learn pipeline for the engineering of biological systems. Typically, this requires the construction of specifically designed, large and complex DNA assemblies. The availability of cheap DNA synthesis and automation enables high-throughput assembly approaches, which generates a heavy demand for DNA sequencing to verify correctly assembled constructs. Next-generation sequencing is ideally positioned to perform this task, however with expensive hardware costs and bespoke data analysis requirements few laboratories utilize this technology in-house. Here a workflow for highly multiplexed sequencing is presented, capable of fast and accurate sequence verification of DNA assemblies using nanopore technology. A novel sample barcoding system using polymerase chain reaction is introduced, and sequencing data are analyzed through a bespoke analysis algorithm. Crucially, this algorithm overcomes the problem of high-error rate nanopore data (which typically prevents identification of single nucleotide variants) through statistical analysis of strand bias, permitting accurate sequence analysis with single-base resolution. As an example, 576 constructs (6 × 96 well plates) were processed in a single workflow in 72 h (from Escherichia coli colonies to analyzed data). Given our procedure's low hardware costs and highly multiplexed capability, this provides cost-effective access to powerful DNA sequencing for any laboratory, with applications beyond synthetic biology including directed evolution, single nucleotide polymorphism analysis and gene synthesis.
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Affiliation(s)
- Andrew Currin
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK.,Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Mark S Dunstan
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Adrian J Jervis
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Paul Mulherin
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Christopher J Robinson
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Sandra Taylor
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Pablo Carbonell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Katherine A Hollywood
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Cunyu Yan
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Eriko Takano
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Nigel S Scrutton
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Rainer Breitling
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,School of Natural Sciences, Department of Chemistry, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
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14
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Currin A, Kwok J, Sadler JC, Bell EL, Swainston N, Ababi M, Day P, Turner NJ, Kell DB. GeneORator: An Effective Strategy for Navigating Protein Sequence Space More Efficiently through Boolean OR-Type DNA Libraries. ACS Synth Biol 2019; 8:1371-1378. [PMID: 31132850 PMCID: PMC7007284 DOI: 10.1021/acssynbio.9b00063] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Directed evolution requires the creation of genetic diversity and subsequent screening or selection for improved variants. For DNA mutagenesis, conventional site-directed methods implicitly utilize the Boolean AND operator (creating all mutations simultaneously), producing a combinatorial explosion in the number of genetic variants as the number of mutations increases. We introduce GeneORator, a novel strategy for creating DNA libraries based on the Boolean logical OR operator. Here, a single library is divided into many subsets, each containing different combinations of the desired mutations. Consequently, the effect of adding more mutations on the number of genetic combinations is additive (Boolean OR logic) and not exponential (AND logic). We demonstrate this strategy with large-scale mutagenesis studies, using monoamine oxidase-N ( Aspergillus niger) as the exemplar target. First, we mutated every residue in the secondary structure-containing regions (276 out of a total 495 amino acids) to screen for improvements in kcat. Second, combinatorial OR-type libraries permitted screening of diverse mutation combinations in the enzyme active site to detect activity toward novel substrates. In both examples, OR-type libraries effectively reduced the number of variants searched up to 1010-fold, dramatically reducing the screening effort required to discover variants with improved and/or novel activity. Importantly, this approach enables the screening of a greater diversity of mutation combinations, accessing a larger area of a protein's sequence space. OR-type libraries can be applied to any biological engineering objective requiring DNA mutagenesis, and the approach has wide ranging applications in, for example, enzyme engineering, antibody engineering, and synthetic biology.
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Affiliation(s)
- Andrew Currin
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
- School of Chemistry, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Jane Kwok
- School of Chemistry, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Joanna C. Sadler
- School of Chemistry, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Elizabeth L. Bell
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
- School of Chemistry, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Maria Ababi
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, United Kingdom
- School of Computer Science, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Philip Day
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Nicholas J. Turner
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
- School of Chemistry, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Douglas B. Kell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
- School of Chemistry, The University of Manchester, Manchester M13 9PL, United Kingdom
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15
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Jervis AJ, Carbonell P, Vinaixa M, Dunstan MS, Hollywood KA, Robinson CJ, Rattray NJW, Yan C, Swainston N, Currin A, Sung R, Toogood H, Taylor S, Faulon JL, Breitling R, Takano E, Scrutton NS. Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli. ACS Synth Biol 2019; 8:127-136. [PMID: 30563328 DOI: 10.1021/acssynbio.8b00398] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The field of synthetic biology aims to make the design of biological systems predictable, shrinking the huge design space to practical numbers for testing. When designing microbial cell factories, most optimization efforts have focused on enzyme and strain selection/engineering, pathway regulation, and process development. In silico tools for the predictive design of bacterial ribosome binding sites (RBSs) and RBS libraries now allow translational tuning of biochemical pathways; however, methods for predicting optimal RBS combinations in multigene pathways are desirable. Here we present the implementation of machine learning algorithms to model the RBS sequence-phenotype relationship from representative subsets of large combinatorial RBS libraries allowing the accurate prediction of optimal high-producers. Applied to a recombinant monoterpenoid production pathway in Escherichia coli, our approach was able to boost production titers by over 60% when screening under 3% of a library. To facilitate library screening, a multiwell plate fermentation procedure was developed, allowing increased screening throughput with sufficient resolution to discriminate between high and low producers. High producers from one library did not translate during scale-up, but the reduced screening requirements allowed rapid rescreening at the larger scale. This methodology is potentially compatible with any biochemical pathway and provides a powerful tool toward predictive design of bacterial production chassis.
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Affiliation(s)
- Adrian J. Jervis
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Pablo Carbonell
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Maria Vinaixa
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Mark S. Dunstan
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Katherine A. Hollywood
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Christopher J. Robinson
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Nicholas J. W. Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, Strathclyde University, 161 Cathedral Street, Glasgow G4 0RE, United Kingdom
| | - Cunyu Yan
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Neil Swainston
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Andrew Currin
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Rehana Sung
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Helen Toogood
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Sandra Taylor
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Jean-Loup Faulon
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
- MICALIS, INRA-AgroParisTech, Domaine de Vilvert, 78352 Jouy en Josas Cedex, France
| | - Rainer Breitling
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Eriko Takano
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Nigel S. Scrutton
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
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16
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Abstract
The ability of RNA to sense, regulate, and store information is an attractive attribute for a variety of functional applications including the development of regulatory control devices for synthetic biology. RNA folding and function is known to be highly context sensitive, which limits the modularity and reuse of RNA regulatory devices to control different heterologous sequences and genes. We explored the cause and effect of sequence context sensitivity for translational ON riboswitches located in the 5' UTR, by constructing and screening a library of N-terminal synonymous codon variants. By altering the N-terminal codon usage we were able to obtain RNA devices with a broad range of functional performance properties (ON, OFF, fold-change). Linear regression and calculated metrics were used to rationalize the major determining features leading to optimal riboswitch performance, and to identify multiple interactions between the explanatory metrics. Finally, partial least squared (PLS) analysis was employed in order to understand the metrics and their respective effect on performance. This PLS model was shown to provide good explanation of our library. This study provides a novel multivariant analysis framework to rationalize the codon context performance of allosteric RNA-devices. The framework will also serve as a platform for future riboswitch context engineering endeavors.
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Affiliation(s)
- Ross Kent
- Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Samantha Halliwell
- Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Kate Young
- Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Neil Swainston
- Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Neil Dixon
- Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, M13 9PL, United Kingdom
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17
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Swainston N, Kettner C. A repository for quality-assured data on enzyme activity. Nature 2018; 556:309. [PMID: 29670271 DOI: 10.1038/d41586-018-04630-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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18
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Swainston N, Baici A, Bakker BM, Cornish-Bowden A, Fitzpatrick PF, Halling P, Leyh TS, O'Donovan C, Raushel FM, Reschel U, Rohwer JM, Schnell S, Schomburg D, Tipton KF, Tsai MD, Westerhoff HV, Wittig U, Wohlgemuth R, Kettner C. STRENDA DB: enabling the validation and sharing of enzyme kinetics data. FEBS J 2018; 285:2193-2204. [PMID: 29498804 DOI: 10.1111/febs.14427] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 02/27/2018] [Indexed: 01/15/2023]
Abstract
Standards for reporting enzymology data (STRENDA) DB is a validation and storage system for enzyme function data that incorporates the STRENDA Guidelines. It provides authors who are preparing a manuscript with a user-friendly, web-based service that checks automatically enzymology data sets entered in the submission form that they are complete and valid before they are submitted as part of a publication to a journal.
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Affiliation(s)
- Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals, Manchester Institute of Biotechnology, University of Manchester, UK
| | - Antonio Baici
- Department of Biochemistry, University of Zürich, Switzerland
| | - Barbara M Bakker
- University Medical Center Groningen, University of Groningen, The Netherlands
| | | | | | - Peter Halling
- WestCHEM, Department of Pure & Applied Chemistry, University of Strathclyde, Glasgow, UK
| | - Thomas S Leyh
- The Albert-Einstein-College of Medicine, Bronx, NY, USA
| | - Claire O'Donovan
- European Bioinformatics Institute, EMBL Outstation, Cambridge, UK
| | - Frank M Raushel
- Department of Chemistry, Texas A&M University, College Station, TX, USA
| | - Udo Reschel
- Beilstein-Institut, Frankfurt am Main, Germany
| | - Johann M Rohwer
- Department of Biochemistry, University of Stellenbosch, South Africa
| | - Santiago Schnell
- Department of Molecular & Integrative Physiology, Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Dietmar Schomburg
- Bioinformatics and Systems Biology, Technical University of Braunschweig, Germany
| | - Keith F Tipton
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland
| | - Ming-Daw Tsai
- Institute of Biochemical Sciences, Academia Sinica, Taipei, Taiwan
| | - Hans V Westerhoff
- Manchester Centre for Integrative Systems Biology, School for Chemical Engineering and Analytical Science, University of Manchester, UK.,Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Science, University of Amsterdam, The Netherlands.,Molecular Cell Biology, Faculty of Sciences, Vrije Universiteit Amsterdam, The Netherlands
| | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Germany
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19
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Currin A, Dunstan MS, Johannissen LO, Hollywood KA, Vinaixa M, Jervis AJ, Swainston N, Rattray NJW, Gardiner JM, Kell DB, Takano E, Toogood HS, Scrutton NS. Engineering the "Missing Link" in Biosynthetic (-)-Menthol Production: Bacterial Isopulegone Isomerase. ACS Catal 2018; 8:2012-2020. [PMID: 29750129 PMCID: PMC5937688 DOI: 10.1021/acscatal.7b04115] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 01/15/2018] [Indexed: 12/28/2022]
Abstract
![]()
The
realization of a synthetic biology approach to microbial (1R,2S,5R)-(−)-menthol (1) production relies on the identification
of a gene encoding an isopulegone isomerase (IPGI), the only enzyme
in the Mentha piperita biosynthetic
pathway as yet unidentified. We demonstrate that Δ5-3-ketosteroid
isomerase (KSI) from Pseudomonas putida can act as an IPGI, producing (R)-(+)-pulegone
((R)-2) from (+)-cis-isopulegone (3). Using a robotics-driven semirational
design strategy, we identified a key KSI variant encoding four active
site mutations, which confer a 4.3-fold increase in activity over
the wild-type enzyme. This was assisted by the generation of crystal
structures of four KSI variants, combined with molecular modeling
of 3 binding to identify key active site residue targets.
The KSI variant was demonstrated to function efficiently within cascade
biocatalytic reactions with downstream Mentha enzymes pulegone reductase and (−)-menthone:(−)-menthol
reductase to generate 1 from 3. This study
introduces the use of a recombinant IPGI, engineered to function efficiently
within a biosynthetic pathway for the production of 1 in microorganisms.
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20
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Swainston N, Dunstan M, Jervis AJ, Robinson CJ, Carbonell P, Williams AR, Faulon JL, Scrutton NS, Kell DB. PartsGenie: an integrated tool for optimizing and sharing synthetic biology parts. Bioinformatics 2018; 34:2327-2329. [DOI: 10.1093/bioinformatics/bty105] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 02/22/2018] [Indexed: 11/12/2022] Open
Affiliation(s)
- Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Mark Dunstan
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Adrian J Jervis
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Christopher J Robinson
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Pablo Carbonell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Alan R Williams
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Jean-Loup Faulon
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
- MICALIS, INRA-AgroParisTech, Domaine de Vilvert, Jouy en Josas Cedex, France
| | - Nigel S Scrutton
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Douglas B Kell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
- School of Chemistry, The University of Manchester, Manchester, UK
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21
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Carbonell P, Wong J, Swainston N, Takano E, Turner NJ, Scrutton NS, Kell DB, Breitling R, Faulon JL. Selenzyme: enzyme selection tool for pathway design. Bioinformatics 2018; 34:2153-2154. [PMID: 29425325 PMCID: PMC9881682 DOI: 10.1093/bioinformatics/bty065] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 02/06/2018] [Indexed: 02/02/2023] Open
Abstract
Summary Synthetic biology applies the principles of engineering to biology in order to create biological functionalities not seen before in nature. One of the most exciting applications of synthetic biology is the design of new organisms with the ability to produce valuable chemicals including pharmaceuticals and biomaterials in a greener; sustainable fashion. Selecting the right enzymes to catalyze each reaction step in order to produce a desired target compound is, however, not trivial. Here, we present Selenzyme, a free online enzyme selection tool for metabolic pathway design. The user is guided through several decision steps in order to shortlist the best candidates for a given pathway step. The tool graphically presents key information about enzymes based on existing databases and tools such as: similarity of sequences and of catalyzed reactions; phylogenetic distance between source organism and intended host species; multiple alignment highlighting conserved regions, predicted catalytic site, and active regions and relevant properties such as predicted solubility and transmembrane regions. Selenzyme provides bespoke sequence selection for automated workflows in biofoundries. Availability and implementation The tool is integrated as part of the pathway design stage into the design-build-test-learn SYNBIOCHEM pipeline. The Selenzyme web server is available at http://selenzyme.synbiochem.co.uk. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Jerry Wong
- BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology
| | - Neil Swainston
- BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology
| | - Eriko Takano
- BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology,School of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Nicholas J Turner
- BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology,School of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Nigel S Scrutton
- BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology,School of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Douglas B Kell
- BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology,School of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Rainer Breitling
- BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology,School of Chemistry, The University of Manchester, Manchester M1 7DN, UK
| | - Jean-Loup Faulon
- BBSRC/EPSRC Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology,School of Chemistry, The University of Manchester, Manchester M1 7DN, UK,MICALIS, INRA-AgroParisTech, Domaine de Vilvert, 78352 Jouy en Josas Cedex, France
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22
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Currin A, Swainston N, Day PJ, Kell DB. SpeedyGenes: Exploiting an Improved Gene Synthesis Method for the Efficient Production of Synthetic Protein Libraries for Directed Evolution. Methods Mol Biol 2018; 1472:63-78. [PMID: 27671932 DOI: 10.1007/978-1-4939-6343-0_5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Gene synthesis is a fundamental technology underpinning much research in the life sciences. In particular, synthetic biology and biotechnology utilize gene synthesis to assemble any desired DNA sequence, which can then be incorporated into novel parts and pathways. Here, we describe SpeedyGenes, a gene synthesis method that can assemble DNA sequences with greater fidelity (fewer errors) than existing methods, but that can also be used to encode extensive, statistically designed sequence variation at any position in the sequence to create diverse (but accurate) variant libraries. We summarize the integrated use of GeneGenie to design DNA and oligonucleotide sequences, followed by the procedure for assembling these accurately and efficiently using SpeedyGenes.
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Affiliation(s)
- Andrew Currin
- Manchester Institute of Biotechnology, The University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. .,School of Chemistry, The University of Manchester, Manchester, M13 9PL, UK. .,Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), The University of Manchester, 131, Princess St, Manchester, M1 7DN, UK.
| | - Neil Swainston
- Manchester Institute of Biotechnology, The University of Manchester, 131, Princess St, Manchester, M1 7DN, UK.,Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), The University of Manchester, 131, Princess St, Manchester, M1 7DN, UK.,School of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Philip J Day
- Manchester Institute of Biotechnology, The University of Manchester, 131, Princess St, Manchester, M1 7DN, UK.,Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), The University of Manchester, 131, Princess St, Manchester, M1 7DN, UK.,Faculty of Medical and Human Sciences, The University of Manchester, Manchester, M13 9PT, UK
| | - Douglas B Kell
- Manchester Institute of Biotechnology, The University of Manchester, 131, Princess St, Manchester, M1 7DN, UK.,School of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.,Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), The University of Manchester, 131, Princess St, Manchester, M1 7DN, UK
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23
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Robinson CJ, Dunstan MS, Swainston N, Titchmarsh J, Takano E, Scrutton NS, Jervis AJ. Multifragment DNA Assembly of Biochemical Pathways via Automated Ligase Cycling Reaction. Methods Enzymol 2018; 608:369-392. [DOI: 10.1016/bs.mie.2018.04.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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24
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McMurry JA, Juty N, Blomberg N, Burdett T, Conlin T, Conte N, Courtot M, Deck J, Dumontier M, Fellows DK, Gonzalez-Beltran A, Gormanns P, Grethe J, Hastings J, Hériché JK, Hermjakob H, Ison JC, Jimenez RC, Jupp S, Kunze J, Laibe C, Le Novère N, Malone J, Martin MJ, McEntyre JR, Morris C, Muilu J, Müller W, Rocca-Serra P, Sansone SA, Sariyar M, Snoep JL, Soiland-Reyes S, Stanford NJ, Swainston N, Washington N, Williams AR, Wimalaratne SM, Winfree LM, Wolstencroft K, Goble C, Mungall CJ, Haendel MA, Parkinson H. Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data. PLoS Biol 2017; 15:e2001414. [PMID: 28662064 PMCID: PMC5490878 DOI: 10.1371/journal.pbio.2001414] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.
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Affiliation(s)
- Julie A. McMurry
- Department of Medical Informatics and Epidemiology and OHSU Library, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Nick Juty
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Niklas Blomberg
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Tony Burdett
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Tom Conlin
- Department of Medical Informatics and Epidemiology and OHSU Library, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Nathalie Conte
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Mélanie Courtot
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - John Deck
- Berkeley Natural History Museums, University of California at Berkeley, Berkely, California, United States of America
| | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, the Netherlands
| | - Donal K. Fellows
- School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | | | - Philipp Gormanns
- Institute of Experimental Genetics, Helmholtz Centre Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jeffrey Grethe
- Center for Research in Biological Systems, University of California San Diego, La Jolla, California, United States of America
| | | | | | - Henning Hermjakob
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Jon C. Ison
- Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
| | - Rafael C. Jimenez
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Simon Jupp
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - John Kunze
- California Digital Library, Oakland, California, United States of America
| | - Camille Laibe
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | | | - James Malone
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Maria Jesus Martin
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Johanna R. McEntyre
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Chris Morris
- Science and Technology Facilities Council, Daresbury Laboratory, Warrington, United Kingdom
| | - Juha Muilu
- Genomics Coordination Center, Department of Genetics, University Medical Center Groningen and Groningen Bioinformatics Center, University of Groningen, Groningen, the Netherlands
| | - Wolfgang Müller
- Scientific Databases and Visualization at Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | | | | | - Murat Sariyar
- Institute for Medical Informatics, Bern University of Applied Sciences, Engineering and Information Technology, Bern, Switzerland
| | - Jacky L. Snoep
- Manchester Institute of Biology, University of Manchester, Manchester, United Kingdom
- Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa
| | - Stian Soiland-Reyes
- School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Natalie J. Stanford
- School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals, University of Manchester, Manchester, United Kingdom
| | - Nicole Washington
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Alan R. Williams
- School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Sarala M. Wimalaratne
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Lilly M. Winfree
- Department of Medical Informatics and Epidemiology and OHSU Library, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, the Netherlands
| | - Carole Goble
- School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Melissa A. Haendel
- Department of Medical Informatics and Epidemiology and OHSU Library, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Helen Parkinson
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
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Carbonell P, Currin A, Jervis AJ, Rattray NJW, Swainston N, Yan C, Takano E, Breitling R. Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle. Nat Prod Rep 2016; 33:925-32. [PMID: 27185383 PMCID: PMC5063057 DOI: 10.1039/c6np00018e] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Indexed: 12/11/2022]
Abstract
Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.
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Affiliation(s)
- Pablo Carbonell
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Andrew Currin
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Adrian J. Jervis
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Nicholas J. W. Rattray
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Neil Swainston
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Cunyu Yan
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Eriko Takano
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Rainer Breitling
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
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26
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Swainston N, Hastings J, Dekker A, Muthukrishnan V, May J, Steinbeck C, Mendes P. libChEBI: an API for accessing the ChEBI database. J Cheminform 2016; 8:11. [PMID: 26933452 PMCID: PMC4772646 DOI: 10.1186/s13321-016-0123-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 02/16/2016] [Indexed: 01/29/2023] Open
Abstract
Background ChEBI is a database and ontology of chemical entities of biological interest. It is widely used as a source of identifiers to facilitate unambiguous reference to chemical entities within biological models, databases, ontologies and literature. ChEBI contains a wealth of chemical data, covering over 46,500 distinct chemical entities, and related data such as chemical formula, charge, molecular mass, structure, synonyms and links to external databases. Furthermore, ChEBI is an ontology, and thus provides meaningful links between chemical entities. Unlike many other resources, ChEBI is fully human-curated, providing a reliable, non-redundant collection of chemical entities and related data. While ChEBI is supported by a web service for programmatic access and a number of download files, it does not have an API library to facilitate the use of ChEBI and its data in cheminformatics software. Results To provide
this missing functionality, libChEBI, a comprehensive API library for accessing ChEBI data, is introduced. libChEBI is available in Java, Python and MATLAB versions from http://github.com/libChEBI, and provides full programmatic access to all data held within the ChEBI database through a simple and documented API. libChEBI is reliant upon the (automated) download and regular update of flat files that are held locally. As such, libChEBI can be embedded in both on- and off-line software applications. Conclusions libChEBI allows better support of ChEBI and its data in the development of new cheminformatics software. Covering three key programming languages, it allows for the entirety of the ChEBI database to be accessed easily and quickly through a simple API. All code is open access and freely available. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0123-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Specialty Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN UK ; European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Janna Hastings
- European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Adriano Dekker
- European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | | | - John May
- European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK ; NextMove Software Ltd., Innovation Centre, Science Park, Milton Road, Cambridge, CB4 0EY UK
| | | | - Pedro Mendes
- Manchester Centre for Synthetic Biology of Fine and Specialty Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN UK ; School of Computer Science, University of Manchester, Manchester, M13 9PL UK ; Center for Quantitative Medicine, UConn Health, Farmington, CT 06030 USA
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Swainston N, Smallbone K, Hefzi H, Dobson PD, Brewer J, Hanscho M, Zielinski DC, Ang KS, Gardiner NJ, Gutierrez JM, Kyriakopoulos S, Lakshmanan M, Li S, Liu JK, Martínez VS, Orellana CA, Quek LE, Thomas A, Zanghellini J, Borth N, Lee DY, Nielsen LK, Kell DB, Lewis NE, Mendes P. Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 2016; 12:109. [PMID: 27358602 PMCID: PMC4896983 DOI: 10.1007/s11306-016-1051-4] [Citation(s) in RCA: 182] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 05/27/2016] [Indexed: 11/30/2022]
Abstract
INTRODUCTION The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed. OBJECTIVES We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources. METHODS Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions. RESULTS Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources. CONCLUSION Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).
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Affiliation(s)
- Neil Swainston
- />Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK
- />Faculty of Life Sciences, The University of Manchester, Manchester, M13 9PL UK
- />School of Computer Science, The University of Manchester, Manchester, M13 9PL UK
| | - Kieran Smallbone
- />School of Computer Science, The University of Manchester, Manchester, M13 9PL UK
| | - Hooman Hefzi
- />Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
- />Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA USA
| | - Paul D. Dobson
- />School of Computer Science, The University of Manchester, Manchester, M13 9PL UK
| | - Judy Brewer
- />Harvard Extension School, 51 Brattle St., Cambridge, MA 02138 USA
- />Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA 02139 USA
| | - Michael Hanscho
- />Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- />Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Daniel C. Zielinski
- />Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
| | - Kok Siong Ang
- />Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585 Singapore
- />Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668 Singapore
| | - Natalie J. Gardiner
- />Faculty of Life Sciences, The University of Manchester, Manchester, M13 9PL UK
| | - Jahir M. Gutierrez
- />Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
- />Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA USA
| | - Sarantos Kyriakopoulos
- />Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668 Singapore
| | - Meiyappan Lakshmanan
- />Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668 Singapore
| | - Shangzhong Li
- />Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
- />Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA USA
| | - Joanne K. Liu
- />Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA USA
| | - Veronica S. Martínez
- />Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Bldg 75), Brisbane, QLD 4072 Australia
| | - Camila A. Orellana
- />Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Bldg 75), Brisbane, QLD 4072 Australia
| | - Lake-Ee Quek
- />Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Bldg 75), Brisbane, QLD 4072 Australia
| | - Alex Thomas
- />Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA USA
- />Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA USA
| | | | - Nicole Borth
- />Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- />Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Dong-Yup Lee
- />Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585 Singapore
- />Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668 Singapore
| | - Lars K. Nielsen
- />Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Bldg 75), Brisbane, QLD 4072 Australia
| | - Douglas B. Kell
- />Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK
- />School of Chemistry, The University of Manchester, Manchester, M13 9PL UK
| | - Nathan E. Lewis
- />Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA USA
- />Department of Pediatrics, University of California, San Diego, La Jolla, CA USA
| | - Pedro Mendes
- />Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK
- />School of Computer Science, The University of Manchester, Manchester, M13 9PL UK
- />Center for Quantitative Medicine, UConn Health, 263 Farmington Avenue, Farmington, CT 06030-6033 USA
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Quinn JY, Cox RS, Adler A, Beal J, Bhatia S, Cai Y, Chen J, Clancy K, Galdzicki M, Hillson NJ, Le Novère N, Maheshwari AJ, McLaughlin JA, Myers CJ, P U, Pocock M, Rodriguez C, Soldatova L, Stan GBV, Swainston N, Wipat A, Sauro HM. SBOL Visual: A Graphical Language for Genetic Designs. PLoS Biol 2015; 13:e1002310. [PMID: 26633141 PMCID: PMC4669170 DOI: 10.1371/journal.pbio.1002310] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and DNA assembly features. These symbols can be used to draw illustrations for communication and instruction, and as image assets for computer-aided design. SBOL Visual is a community standard, freely available for personal, academic, and commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been used in scientific publications and software tools. We encourage users to use and modify them freely, and to join the SBOL Visual community: http://www.sbolstandard.org/visual.
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Affiliation(s)
- Jacqueline Y. Quinn
- Autodesk Research, Autodesk Inc., San Francisco, California, United States of America
| | | | - Aaron Adler
- Information and Knowledge Technologies, Raytheon BBN Technologies, Cambridge, Massachusetts, United States of America
| | - Jacob Beal
- Information and Knowledge Technologies, Raytheon BBN Technologies, Cambridge, Massachusetts, United States of America
| | - Swapnil Bhatia
- Electrical and Computer Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Yizhi Cai
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna Chen
- Fuels Synthesis and Technologies Divisions, Joint BioEnergy Institute, Emeryville, California, United States of America
- Lawrence Berkeley National Lab, Berkeley, California, United States of America
| | - Kevin Clancy
- Synthetic Biology Unit, ThermoFisher Scientific, Carlsbad, California, United States of America
| | | | - Nathan J. Hillson
- Fuels Synthesis and Technologies Divisions, Joint BioEnergy Institute, Emeryville, California, United States of America
- Lawrence Berkeley National Lab, Berkeley, California, United States of America
| | | | - Akshay J. Maheshwari
- Stanford University School of Medicine, Stanford, California, United States of America
| | | | - Chris J. Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, United States of America
| | - Umesh P
- Department of Computational Biology & Bioinformatics, University of Kerala, Kerala, India
| | - Matthew Pocock
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
- Turing Ate My Hamster LTD, Newcastle upon Tyne, United Kingdom
| | - Cesar Rodriguez
- Department of Biomedical Sciences, College of Medicine, Florida State University, Tallahassee, Florida, United States of America
| | | | - Guy-Bart V. Stan
- Department of Bioengineering, Centre for Synthetic Biology and Innovation, Imperial College London, South Kensington Campus, London, United Kingdom
| | - Neil Swainston
- Centre for Synthetic Biology of Fine and Specialty Chemicals (SYNBIOCHEM), University of Manchester, Manchester, United Kingdom
| | - Anil Wipat
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Herbert M. Sauro
- Bioengineering, University of Washington, Seattle, Washington, United States of America
- * E-mail:
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Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, Turner S, Swainston N, Mendes P, Steinbeck C. ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Res 2015; 44:D1214-9. [PMID: 26467479 PMCID: PMC4702775 DOI: 10.1093/nar/gkv1031] [Citation(s) in RCA: 518] [Impact Index Per Article: 57.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Accepted: 09/28/2015] [Indexed: 12/31/2022] Open
Abstract
ChEBI is a database and ontology containing information about chemical entities of biological interest. It currently includes over 46,000 entries, each of which is classified within the ontology and assigned multiple annotations including (where relevant) a chemical structure, database cross-references, synonyms and literature citations. All content is freely available and can be accessed online at http://www.ebi.ac.uk/chebi. In this update paper, we describe recent improvements and additions to the ChEBI offering. We have substantially extended our collection of endogenous metabolites for several organisms including human, mouse, Escherichia coli and yeast. Our front-end has also been reworked and updated, improving the user experience, removing our dependency on Java applets in favour of embedded JavaScript components and moving from a monthly release update to a 'live' website. Programmatic access has been improved by the introduction of a library, libChEBI, in Java, Python and Matlab. Furthermore, we have added two new tools, namely an analysis tool, BiNChE, and a query tool for the ontology, OntoQuery.
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Affiliation(s)
- Janna Hastings
- Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Gareth Owen
- Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Adriano Dekker
- Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Marcus Ennis
- Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Namrata Kale
- Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Venkatesh Muthukrishnan
- Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Steve Turner
- Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Neil Swainston
- Manchester Centre for Integrative Systems Biology, University of Manchester, UK
| | - Pedro Mendes
- Manchester Centre for Integrative Systems Biology, University of Manchester, UK
| | - Christoph Steinbeck
- Cheminformatics and Metabolism, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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Stanford NJ, Millard P, Swainston N. RobOKoD: microbial strain design for (over)production of target compounds. Front Cell Dev Biol 2015; 3:17. [PMID: 25853130 PMCID: PMC4371745 DOI: 10.3389/fcell.2015.00017] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 02/25/2015] [Indexed: 11/16/2022] Open
Abstract
Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.
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Affiliation(s)
- Natalie J Stanford
- Manchester Institute of Biotechnology, University of Manchester Manchester, UK ; School of Computer Science, University of Manchester Manchester, UK
| | - Pierre Millard
- Manchester Institute of Biotechnology, University of Manchester Manchester, UK ; School of Computer Science, University of Manchester Manchester, UK ; INSA, UPS, INP, LISBP, Université de Toulouse Toulouse, France ; INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés Toulouse, France ; Centre National de la Recherche Scientifique, UMR5504 Toulouse, France
| | - Neil Swainston
- Manchester Institute of Biotechnology, University of Manchester Manchester, UK ; School of Computer Science, University of Manchester Manchester, UK
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Currin A, Swainston N, Day PJ, Kell DB. Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently. Chem Soc Rev 2015; 44:1172-239. [PMID: 25503938 PMCID: PMC4349129 DOI: 10.1039/c4cs00351a] [Citation(s) in RCA: 247] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Indexed: 12/21/2022]
Abstract
The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.
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Affiliation(s)
- Andrew Currin
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- School of Chemistry , The University of Manchester , Manchester M13 9PL , UK
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
| | - Neil Swainston
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
- School of Computer Science , The University of Manchester , Manchester M13 9PL , UK
| | - Philip J. Day
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
- Faculty of Medical and Human Sciences , The University of Manchester , Manchester M13 9PT , UK
| | - Douglas B. Kell
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- School of Chemistry , The University of Manchester , Manchester M13 9PL , UK
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
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Abstract
We exploit the recent availability of a community reconstruction of the human metabolic network ('Recon2') to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) 'nearest' human metabolite. This suggests a 'rule of 0.5' mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This 'rule of 0.5' may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.
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Affiliation(s)
- Steve O′Hagan
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
- The Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
| | - Neil Swainston
- The Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
- School of Computer Science, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
| | - Julia Handl
- Manchester Business School, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
| | - Douglas B. Kell
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
- The Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
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Currin A, Swainston N, Day PJ, Kell DB. SpeedyGenes: an improved gene synthesis method for the efficient production of error-corrected, synthetic protein libraries for directed evolution. Protein Eng Des Sel 2014; 27:273-80. [PMID: 25108914 PMCID: PMC4140418 DOI: 10.1093/protein/gzu029] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The de novo synthesis of genes is becoming increasingly common in synthetic biology studies. However, the inherent error rate (introduced by errors incurred during oligonucleotide synthesis) limits its use in synthesising protein libraries to only short genes. Here we introduce SpeedyGenes, a PCR-based method for the synthesis of diverse protein libraries that includes an error-correction procedure, enabling the efficient synthesis of large genes for use directly in functional screening. First, we demonstrate an accurate gene synthesis method by synthesising and directly screening (without pre-selection) a 747 bp gene for green fluorescent protein (yielding 85% fluorescent colonies) and a larger 1518 bp gene (a monoamine oxidase, producing 76% colonies with full catalytic activity, a 4-fold improvement over previous methods). Secondly, we show that SpeedyGenes can accommodate multiple and combinatorial variant sequences while maintaining efficient enzymatic error correction, which is particularly crucial for larger genes. In its first application for directed evolution, we demonstrate the use of SpeedyGenes in the synthesis and screening of large libraries of MAO-N variants. Using this method, libraries are synthesised, transformed and screened within 3 days. Importantly, as each mutation we introduce is controlled by the oligonucleotide sequence, SpeedyGenes enables the synthesis of large, diverse, yet controlled variant sequences for the purposes of directed evolution.
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Affiliation(s)
- Andrew Currin
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
| | - Neil Swainston
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Computer Science, The University of Manchester, Manchester M13 9PL, UK
| | - Philip J Day
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK Faculty of Medical and Human Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Douglas B Kell
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
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Abstract
GeneGenie, a new online tool available at http://www.gene-genie.org, is introduced to support the design and self-assembly of synthetic genes and constructs. GeneGenie allows for the design of oligonucleotide cohorts encoding the gene sequence optimized for expression in any suitable host through an intuitive, easy-to-use web interface. The tool ensures consistent oligomer overlapping melting temperatures, minimizes the likelihood of misannealing, optimizes codon usage for expression in a selected host, allows for specification of forward and reverse cloning sequences (for downstream ligation) and also provides support for mutagenesis or directed evolution studies. Directed evolution studies are enabled through the construction of variant libraries via the optional specification of ‘variant codons’, containing mixtures of bases, at any position. For example, specifying the variant codon TNT (where N is any nucleotide) will generate an equimolar mixture of the codons TAT, TCT, TGT and TTT at that position, encoding a mixture of the amino acids Tyr, Ser, Cys and Phe. This facility is demonstrated through the use of GeneGenie to develop and synthesize a library of enhanced green fluorescent protein variants.
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Affiliation(s)
- Neil Swainston
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Computer Science, The University of Manchester, Manchester M13 9PL, UK
| | - Andrew Currin
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
| | - Philip J Day
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK Faculty of Medical and Human Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Douglas B Kell
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
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Büchel F, Rodriguez N, Swainston N, Wrzodek C, Czauderna T, Keller R, Mittag F, Schubert M, Glont M, Golebiewski M, van Iersel M, Keating S, Rall M, Wybrow M, Hermjakob H, Hucka M, Kell DB, Müller W, Mendes P, Zell A, Chaouiya C, Saez-Rodriguez J, Schreiber F, Laibe C, Dräger A, Le Novère N. Path2Models: large-scale generation of computational models from biochemical pathway maps. BMC Syst Biol 2013; 7:116. [PMID: 24180668 PMCID: PMC4228421 DOI: 10.1186/1752-0509-7-116] [Citation(s) in RCA: 126] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2013] [Accepted: 10/23/2013] [Indexed: 11/10/2022]
Abstract
BACKGROUND Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data. RESULTS To increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps. CONCLUSIONS To date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized.
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Affiliation(s)
- Finja Büchel
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | - Nicolas Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Babraham Institute, Babraham Research Campus, Cambridge, UK
| | - Neil Swainston
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
| | - Clemens Wrzodek
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | - Tobias Czauderna
- Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben D-06466, Germany
| | - Roland Keller
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | - Florian Mittag
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | - Michael Schubert
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Mihai Glont
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Martijn van Iersel
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Sarah Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Matthias Rall
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | - Michael Wybrow
- Caulfield School of Information Technology, Monash University, Victoria 3800, Australia
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Douglas B Kell
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
- School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
| | | | - Pedro Mendes
- Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
- School of Computer Science, The University of Manchester, Manchester M13 9PL, UK
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA
| | - Andreas Zell
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
| | | | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Falk Schreiber
- Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben D-06466, Germany
- Institute of Computer Science, University of Halle-Wittenberg, Halle, Germany
| | - Camille Laibe
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Andreas Dräger
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen 72076, Germany
- Present address: University of California, San Diego, Bioengineering Department, La Jolla, CA 92093-0412, USA
| | - Nicolas Le Novère
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Babraham Institute, Babraham Research Campus, Cambridge, UK
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Abstract
Following a strategy similar to that used in baker's yeast (Herrgård et al. Nat Biotechnol 26:1155-1160, 2008). A consensus yeast metabolic network obtained from a community approach to systems biology (Herrgård et al. 2008; Dobson et al. BMC Syst Biol 4:145, 2010). Further developments towards a genome-scale metabolic model of yeast (Dobson et al. 2010; Heavner et al. BMC Syst Biol 6:55, 2012). Yeast 5-an expanded reconstruction of the Saccharomyces cerevisiae metabolic network (Heavner et al. 2012) and in Salmonella typhimurium (Thiele et al. BMC Syst Biol 5:8, 2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonellatyphimurium LT2 (Thiele et al. 2011), a recent paper (Thiele et al. Nat Biotechnol 31:419-425, 2013). A community-driven global reconstruction of human metabolism (Thiele et al. 2013) described a much improved 'community consensus' reconstruction of the human metabolic network, called Recon 2, and the authors (that include the present ones) have made it freely available via a database at http://humanmetabolism.org/ and in SBML format at Biomodels (http://identifiers.org/biomodels.db/MODEL1109130000). This short analysis summarises the main findings, and suggests some approaches that will be able to exploit the availability of this model to advantage.
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Affiliation(s)
- Neil Swainston
- School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL UK
- Manchester Institute of Biotechnology, The University of Manchester, Princess Street, Manchester, M1 7DN UK
| | - Pedro Mendes
- Manchester Institute of Biotechnology, The University of Manchester, Princess Street, Manchester, M1 7DN UK
- School of Computer Science, The University of Manchester, Oxford Road, Manchester, M13 9PL UK
- Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24060 USA
| | - Douglas B. Kell
- School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL UK
- Manchester Institute of Biotechnology, The University of Manchester, Princess Street, Manchester, M1 7DN UK
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37
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Smallbone K, Messiha HL, Carroll KM, Winder CL, Malys N, Dunn WB, Murabito E, Swainston N, Dada JO, Khan F, Pir P, Simeonidis E, Spasić I, Wishart J, Weichart D, Hayes NW, Jameson D, Broomhead DS, Oliver SG, Gaskell SJ, McCarthy JEG, Paton NW, Westerhoff HV, Kell DB, Mendes P. A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. FEBS Lett 2013; 587:2832-41. [PMID: 23831062 PMCID: PMC3764422 DOI: 10.1016/j.febslet.2013.06.043] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2013] [Revised: 06/24/2013] [Accepted: 06/25/2013] [Indexed: 11/17/2022]
Abstract
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.
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Affiliation(s)
- Kieran Smallbone
- Manchester Centre for Integrative Systems Biology, Manchester Institute of Biotechnology, The University of Manchester, UK
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38
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Lee D, Smallbone K, Dunn WB, Murabito E, Winder CL, Kell DB, Mendes P, Swainston N. Improving metabolic flux predictions using absolute gene expression data. BMC Syst Biol 2012; 6:73. [PMID: 22713172 PMCID: PMC3477026 DOI: 10.1186/1752-0509-6-73] [Citation(s) in RCA: 107] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2012] [Accepted: 06/05/2012] [Indexed: 12/13/2022]
Abstract
Background Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se. Results An alternative objective function is presented, that is based upon maximising the correlation between experimentally measured absolute gene expression data and predicted internal reaction fluxes. Using quantitative transcriptomics data acquired from Saccharomyces cerevisiae cultures under two growth conditions, the method outperforms traditional approaches for predicting experimentally measured exometabolic flux that are reliant upon maximisation of the rate of biomass production. Conclusion Due to its improved prediction of experimentally measured metabolic fluxes, and of its lack of a requirement for knowledge of the biomass composition of the organism under the conditions of interest, the approach is likely to be of rather general utility. The method has been shown to predict fluxes reliably in single cellular systems. Subsequent work will investigate the method’s ability to generate condition- and tissue-specific flux predictions in multicellular organisms.
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Affiliation(s)
- Dave Lee
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
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39
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Abstract
Systems biology models can be reused within new simulation scenarios, as parts of more complex models or as sources of biochemical knowledge. Reusability does not come by itself but has to be ensured while creating a model. Most important, models should be designed to remain valid in different contexts-for example, for different experimental conditions-and be published in a standardized and well-documented form. Creating reusable models is worthwhile, but it requires some efforts when a model is developed, implemented, documented, and published. Minimum requirements for published systems biology models have been formulated by the MIRIAM initiative. Main criteria are completeness of information and documentation, availability of machine-readable models in standard formats, and semantic annotations connecting the model elements with entries in biological Web resources. In this chapter, we discuss the assumptions behind bottom-up modeling; present important standards like MIRIAM, the Systems Biology Markup Language (SBML), and the Systems Biology Graphical Notation (SBGN); and describe software tools and services for handling semantic annotations. Finally, we show how standards can facilitate the construction of large metabolic network models.
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Affiliation(s)
- Falko Krause
- Max Planck Institute for Molecular Genetics, Dep. Computational Molecular Biology, Berlin, Germany
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40
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Swainston N, Smallbone K, Mendes P, Kell DB, Paton NW. The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks. J Integr Bioinform 2011. [DOI: 10.1515/jib-2011-186] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Summary The generation and use of metabolic network reconstructions has increased over recent years. The development of such reconstructions has typically involved a time-consuming, manual process. Recent work has shown that steps undertaken in reconstructing such metabolic networks are amenable to automation. The SuBliMinaL Toolbox (http://www.mcisb.org/subliminal/) facilitates the reconstruction process by providing a number of independent modules to perform common tasks, such as generating draft reconstructions, determining metabolite protonation state, mass and charge balancing reactions, suggesting intracellular compartmentalisation, adding transport reactions and a biomass function, and formatting the reconstruction to be used in third-party analysis packages. The individual modules manipulate reconstructions encoded in Systems Biology Markup Language (SBML), and can be chained to generate a reconstruction pipeline, or used individually during a manual curation process. This work describes the individual modules themselves, and a study in which the modules were used to develop a metabolic reconstruction of Saccharomyces cerevisiae from the existing data resources KEGG and MetaCyc. The automatically generated reconstruction is analysed for blocked reactions, and suggestions for future improvements to the toolbox are discussed.
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Affiliation(s)
- Neil Swainston
- 1Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, M1 7DN, United Kingdom of Great Britain and Northern Ireland
| | - Kieran Smallbone
- 1Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, M1 7DN, United Kingdom of Great Britain and Northern Ireland
| | - Pedro Mendes
- 2Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, M1 7DN, United Kingdom United States of America
- 3Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, United States of America
| | - Douglas B. Kell
- 1Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, M1 7DN, United Kingdom of Great Britain and Northern Ireland
| | - Norman W. Paton
- 1Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, M1 7DN, United Kingdom of Great Britain and Northern Ireland
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Thiele I, Hyduke DR, Steeb B, Fankam G, Allen DK, Bazzani S, Charusanti P, Chen FC, Fleming RMT, Hsiung CA, De Keersmaecker SCJ, Liao YC, Marchal K, Mo ML, Özdemir E, Raghunathan A, Reed JL, Shin SI, Sigurbjörnsdóttir S, Steinmann J, Sudarsan S, Swainston N, Thijs IM, Zengler K, Palsson BO, Adkins JN, Bumann D. A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2. BMC Syst Biol 2011; 5:8. [PMID: 21244678 PMCID: PMC3032673 DOI: 10.1186/1752-0509-5-8] [Citation(s) in RCA: 102] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2010] [Accepted: 01/18/2011] [Indexed: 01/05/2023]
Abstract
Background Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Results Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches. Conclusion Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.
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Affiliation(s)
- Ines Thiele
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
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Swainston N, Jameson D, Carroll K. A QconCAT informatics pipeline for the analysis, visualization and sharing of absolute quantitative proteomics data. Proteomics 2010; 11:329-33. [PMID: 21204260 DOI: 10.1002/pmic.201000454] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2010] [Revised: 09/29/2010] [Accepted: 10/18/2010] [Indexed: 11/08/2022]
Abstract
Absolute protein concentration determination is becoming increasingly important in a number of fields including diagnostics, biomarker discovery and systems biology modeling. The recently introduced quantification concatamer methodology provides a novel approach to performing such determinations, and it has been applied to both microbial and mammalian systems. While a number of software tools exist for performing analyses of quantitative data generated by related methodologies such as SILAC, there is currently no analysis package dedicated to the quantification concatamer approach. Furthermore, most tools that are currently available in the field of quantitative proteomics do not manage storage and dissemination of such data sets.
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Affiliation(s)
- Neil Swainston
- Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, UK.
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43
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Dobson PD, Smallbone K, Jameson D, Simeonidis E, Lanthaler K, Pir P, Lu C, Swainston N, Dunn WB, Fisher P, Hull D, Brown M, Oshota O, Stanford NJ, Kell DB, King RD, Oliver SG, Stevens RD, Mendes P. Further developments towards a genome-scale metabolic model of yeast. BMC Syst Biol 2010; 4:145. [PMID: 21029416 PMCID: PMC2988745 DOI: 10.1186/1752-0509-4-145] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2010] [Accepted: 10/28/2010] [Indexed: 12/15/2022]
Abstract
BACKGROUND To date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity. RESULTS We have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites--significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions. CONCLUSIONS We report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.
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Affiliation(s)
- Paul D Dobson
- School of Chemistry, The University of Manchester, Manchester M13 9PL, UK
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Radrich K, Tsuruoka Y, Dobson P, Gevorgyan A, Swainston N, Baart G, Schwartz JM. Integration of metabolic databases for the reconstruction of genome-scale metabolic networks. BMC Syst Biol 2010; 4:114. [PMID: 20712863 PMCID: PMC2930596 DOI: 10.1186/1752-0509-4-114] [Citation(s) in RCA: 72] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2010] [Accepted: 08/16/2010] [Indexed: 01/13/2023]
Abstract
BACKGROUND Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources. RESULTS In this article, we report a semi-automated methodology aimed at streamlining the process of metabolic network reconstruction by enabling the integration of different genome-wide databases of metabolic reactions. We present results obtained by applying this methodology to the metabolic network of the plant Arabidopsis thaliana. A systematic comparison of compounds and reactions between two genome-wide databases allowed us to obtain a high-quality core consensus reconstruction, which was validated for stoichiometric consistency. A lower level of consensus led to a larger reconstruction, which has a lower quality standard but provides a baseline for further manual curation. CONCLUSION This semi-automated methodology may be applied to other organisms and help to streamline the process of genome-scale network reconstruction in order to accelerate the transfer of such models to applications.
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Affiliation(s)
- Karin Radrich
- Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK
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45
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Swainston N, Golebiewski M, Messiha HL, Malys N, Kania R, Kengne S, Krebs O, Mir S, Sauer-Danzwith H, Smallbone K, Weidemann A, Wittig U, Kell DB, Mendes P, Müller W, Paton NW, Rojas I. Enzyme kinetics informatics: from instrument to browser. FEBS J 2010; 277:3769-79. [PMID: 20738395 DOI: 10.1111/j.1742-4658.2010.07778.x] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A limited number of publicly available resources provide access to enzyme kinetic parameters. These have been compiled through manual data mining of published papers, not from the original, raw experimental data from which the parameters were calculated. This is largely due to the lack of software or standards to support the capture, analysis, storage and dissemination of such experimental data. Introduced here is an integrative system to manage experimental enzyme kinetics data from instrument to browser. The approach is based on two interrelated databases: the existing SABIO-RK database, containing kinetic data and corresponding metadata, and the newly introduced experimental raw data repository, MeMo-RK. Both systems are publicly available by web browser and web service interfaces and are configurable to ensure privacy of unpublished data. Users of this system are provided with the ability to view both kinetic parameters and the experimental raw data from which they are calculated, providing increased confidence in the data. A data analysis and submission tool, the kineticswizard, has been developed to allow the experimentalist to perform data collection, analysis and submission to both data resources. The system is designed to be extensible, allowing integration with other manufacturer instruments covering a range of analytical techniques.
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Affiliation(s)
- Neil Swainston
- Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.
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Smallbone K, Simeonidis E, Swainston N, Mendes P. Towards a genome-scale kinetic model of cellular metabolism. BMC Syst Biol 2010; 4:6. [PMID: 20109182 PMCID: PMC2829494 DOI: 10.1186/1752-0509-4-6] [Citation(s) in RCA: 120] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2009] [Accepted: 01/28/2010] [Indexed: 11/10/2022]
Abstract
Background Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour. Results We describe a method for building a parameterized genome-scale kinetic model of a metabolic network. Simplified linlog kinetics are used and the parameters are extracted from a kinetic model repository. We demonstrate our methodology by applying it to yeast metabolism. The resultant model has 956 metabolic reactions involving 820 metabolites, and, whilst approximative, has considerably broader remit than any existing models of its type. Control analysis is used to identify key steps within the system. Conclusions Our modelling framework may be considered a stepping-stone toward the long-term goal of a fully-parameterized model of yeast metabolism. The model is available in SBML format from the BioModels database (BioModels ID: MODEL1001200000) and at http://www.mcisb.org/resources/genomescale/.
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Affiliation(s)
- Kieran Smallbone
- Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester, M1 7DN, UK
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47
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Jameson D, Turner DA, Ankers J, Kennedy S, Ryan S, Swainston N, Griffiths T, Spiller DG, Oliver SG, White MRH, Kell DB, Paton NW. Information management for high content live cell imaging. BMC Bioinformatics 2009; 10:226. [PMID: 19622144 PMCID: PMC2723092 DOI: 10.1186/1471-2105-10-226] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2009] [Accepted: 07/21/2009] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND High content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. Whilst solutions exist for managing image data, they are primarily concerned with storage and retrieval of the images themselves and not the data derived from the images. There is therefore a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments. RESULTS We have designed and implemented a data model and information management solution for the data gathered through high content live cell imaging experiments. Many of the experiments to be stored measure the translocation of fluorescently labelled proteins from cytoplasm to nucleus in individual cells. The functionality of this database has been enhanced by the addition of an algorithm that automatically annotates results of these experiments with the timings of translocations and periods of any oscillatory translocations as they are uploaded to the repository. Testing has shown the algorithm to perform well with a variety of previously unseen data. CONCLUSION Our repository is a fully functional example of how high throughput imaging data may be effectively indexed and managed to address the requirements of end users. By implementing the automated analysis of experimental results, we have provided a clear impetus for individuals to ensure that their data forms part of that which is stored in the repository. Although focused on imaging, the solution provided is sufficiently generic to be applied to other functional proteomics and genomics experiments. The software is available from: fhttp://code.google.com/p/livecellim/
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Affiliation(s)
- Daniel Jameson
- Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK
| | - David A Turner
- Centre for Cell Imaging, School of Biological Sciences, Bioscience Research Building, Crown St., Liverpool, L69 7ZB, UK
| | - John Ankers
- Centre for Cell Imaging, School of Biological Sciences, Bioscience Research Building, Crown St., Liverpool, L69 7ZB, UK
| | - Stephnie Kennedy
- Centre for Cell Imaging, School of Biological Sciences, Bioscience Research Building, Crown St., Liverpool, L69 7ZB, UK
| | - Sheila Ryan
- Centre for Cell Imaging, School of Biological Sciences, Bioscience Research Building, Crown St., Liverpool, L69 7ZB, UK
| | - Neil Swainston
- Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK
| | - Tony Griffiths
- School of Computer Science, Kilburn Building, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - David G Spiller
- Centre for Cell Imaging, School of Biological Sciences, Bioscience Research Building, Crown St., Liverpool, L69 7ZB, UK
| | - Stephen G Oliver
- Department of Biochemistry, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Michael RH White
- Centre for Cell Imaging, School of Biological Sciences, Bioscience Research Building, Crown St., Liverpool, L69 7ZB, UK
| | - Douglas B Kell
- Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK
| | - Norman W Paton
- School of Computer Science, Kilburn Building, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
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48
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Abstract
SUMMARY The Systems Biology Markup Language (SBML) is an established community XML format for the markup of biochemical models. With the introduction of SBML level 2 version 3, specific model entities, such as species or reactions, can now be annotated using ontological terms. These annotations, which are encoded using the resource description framework (RDF), provide the facility to specify definite terms to individual components, allowing software to unambiguously identify such components and thus link the models to existing data resources. libSBML is an application programming interface library for the manipulation of SBML files. While libSBML provides the facilities for reading and writing such annotations from and to models, it is beyond the scope of libSBML to provide interpretation of these terms. The libAnnotationSBML library introduced here acts as a layer on top of libSBML linking SBML annotations to the web services that describe these ontological terms. Two applications that use this library are described: SbmlSynonymExtractor finds name synonyms of SBML model entities and SbmlReactionBalancer checks SBML files to determine whether specifed reactions are elementally balanced.
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Affiliation(s)
- Neil Swainston
- Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7DN, UK.
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49
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Brown M, Dunn WB, Dobson P, Patel Y, Winder CL, Francis-McIntyre S, Begley P, Carroll K, Broadhurst D, Tseng A, Swainston N, Spasic I, Goodacre R, Kell DB. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 2009; 134:1322-32. [PMID: 19562197 DOI: 10.1039/b901179j] [Citation(s) in RCA: 219] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The chemical identification of mass spectrometric signals in metabolomic applications is important to provide conversion of analytical data to biological knowledge about metabolic pathways. The complexity of electrospray mass spectrometric data acquired from a range of samples (serum, urine, yeast intracellular extracts, yeast metabolic footprints, placental tissue metabolic footprints) has been investigated and has defined the frequency of different ion types routinely detected. Although some ion types were expected (protonated and deprotonated peaks, isotope peaks, multiply charged peaks) others were not expected (sodium formate adduct ions). In parallel, the Manchester Metabolomics Database (MMD) has been constructed with data from genome scale metabolic reconstructions, HMDB, KEGG, Lipid Maps, BioCyc and DrugBank to provide knowledge on 42,687 endogenous and exogenous metabolite species. The combination of accurate mass data for a large collection of metabolites, theoretical isotope abundance data and knowledge of the different ion types detected provided a greater number of electrospray mass spectrometric signals which were putatively identified and with greater confidence in the samples studied. To provide definitive identification metabolite-specific mass spectral libraries for UPLC-MS and GC-MS have been constructed for 1,065 commercially available authentic standards. The MMD data are available at http://dbkgroup.org/MMD/.
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Affiliation(s)
- M Brown
- Bioanalytical Sciences Group, School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, UK M1 7DN.
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Abstract
Whilst the array of techniques available for quantitative proteomics continues to grow, the attendant bioinformatic software tools are similarly expanding in number. The data capture and analysis of such quantitative data is obviously crucial to the experiment and the methods used to process it will critically affect the quality of the data obtained. These tools must deal with a variety of issues, including identification of labelled and unlabelled peptide species, location of the corresponding MS scans in the experiment, construction of representative ion chromatograms, location of the true peptide ion chromatogram start and end, elimination of background signal in the mass spectrum and chromatogram and calculation of both peptide and protein ratios/abundances. A variety of tools and approaches are available, in part restricted by the nature of the experiment to be performed and available instrumentation. Currently, although there is no single consensus on precisely how to calculate protein and peptide abundances, many common themes have emerged which identify and reduce many of the key sources of error. These issues will be discussed, along with those relating to deposition of quantitative data. At present, mature data standards for quantitative proteomics are not yet available, although formats are beginning to emerge.
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Affiliation(s)
- King Wai Lau
- Faculty of Life Sciences, University of Manchester, M13 9PT, UK
- MBCMS, School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, UK
| | - Andrew R Jones
- Faculty of Life Sciences, University of Manchester, M13 9PT, UK
- School of Computer Science, Faculty of Engineering and Physical Sciences, University of Manchester, UK
| | - Neil Swainston
- Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, UK
| | | | - Simon J Hubbard
- Faculty of Life Sciences, University of Manchester, M13 9PT, UK
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