1
|
Gonçalves AAM, Ribeiro AJ, Resende CAA, Couto CAP, Gandra IB, Dos Santos Barcelos IC, da Silva JO, Machado JM, Silva KA, Silva LS, Dos Santos M, da Silva Lopes L, de Faria MT, Pereira SP, Xavier SR, Aragão MM, Candida-Puma MA, de Oliveira ICM, Souza AA, Nogueira LM, da Paz MC, Coelho EAF, Giunchetti RC, de Freitas SM, Chávez-Fumagalli MA, Nagem RAP, Galdino AS. Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis. Microb Cell Fact 2024; 23:145. [PMID: 38778337 PMCID: PMC11110257 DOI: 10.1186/s12934-024-02418-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.
Collapse
Affiliation(s)
- Ana Alice Maia Gonçalves
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Anna Julia Ribeiro
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Carlos Ananias Aparecido Resende
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Carolina Alves Petit Couto
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Isadora Braga Gandra
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Isabelle Caroline Dos Santos Barcelos
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Jonatas Oliveira da Silva
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Juliana Martins Machado
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Kamila Alves Silva
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Líria Souza Silva
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Michelli Dos Santos
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Lucas da Silva Lopes
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Mariana Teixeira de Faria
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Sabrina Paula Pereira
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Sandra Rodrigues Xavier
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Matheus Motta Aragão
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Mayron Antonio Candida-Puma
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa, 04000, Peru
| | | | - Amanda Araujo Souza
- Biophysics Laboratory, Institute of Biological Sciences, Department of Cell Biology, University of Brasilia, Brasília, 70910-900, Brazil
| | - Lais Moreira Nogueira
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Mariana Campos da Paz
- Bioactives and Nanobiotechnology Laboratory, Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Eduardo Antônio Ferraz Coelho
- Postgraduate Program in Health Sciences, Infectious Diseases and Tropical Medicine, Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, 30130-100, Brazil
| | - Rodolfo Cordeiro Giunchetti
- Laboratory of Biology of Cell Interactions, National Institute of Science and Technology on Tropical Diseases (INCT-DT), Department of Morphology, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Sonia Maria de Freitas
- Biophysics Laboratory, Institute of Biological Sciences, Department of Cell Biology, University of Brasilia, Brasília, 70910-900, Brazil
| | - Miguel Angel Chávez-Fumagalli
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa, 04000, Peru
| | - Ronaldo Alves Pinto Nagem
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Alexsandro Sobreira Galdino
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil.
| |
Collapse
|
2
|
Gonzalez-Sanchez B, Vega-Rodríguez MA, Santander-Jiménez S. A multi-objective butterfly optimization algorithm for protein encoding. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
|
3
|
Accuracy and data efficiency in deep learning models of protein expression. Nat Commun 2022; 13:7755. [PMID: 36517468 PMCID: PMC9751117 DOI: 10.1038/s41467-022-34902-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 11/10/2022] [Indexed: 12/23/2022] Open
Abstract
Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain optimization. But such models need large and costly training data that create steep entry barriers for many laboratories. Here we study the relation between accuracy and data efficiency in an atlas of machine learning models trained on datasets of varied size and sequence diversity. We show that deep learning can achieve good prediction accuracy with much smaller datasets than previously thought. We demonstrate that controlled sequence diversity leads to substantial gains in data efficiency and employed Explainable AI to show that convolutional neural networks can finely discriminate between input DNA sequences. Our results provide guidelines for designing genotype-phenotype screens that balance cost and quality of training data, thus helping promote the wider adoption of deep learning in the biotechnology sector.
Collapse
|
4
|
Controlling gene expression with deep generative design of regulatory DNA. Nat Commun 2022; 13:5099. [PMID: 36042233 PMCID: PMC9427793 DOI: 10.1038/s41467-022-32818-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 08/18/2022] [Indexed: 11/25/2022] Open
Abstract
Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue. Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Here the authors present EspressionGAN, a generative adversarial network that uses genomic and transcriptomic data to generate regulatory sequences.
Collapse
|
5
|
Leibovich Z, Gronau I. Optimal Design of Synthetic DNA Sequences Without Unwanted Binding Sites. J Comput Biol 2022; 29:974-986. [PMID: 35648072 DOI: 10.1089/cmb.2021.0417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Synthesizing DNA molecules by design has become an essential tool in molecular biology and is expected to become ubiquitous in the coming decade. Successful design of a synthetic DNA molecule often requires satisfying multiple objectives, some of which may conflict with others. One particularly important objective is the elimination of unwanted protein binding sites, which may interfere with the desired function of the synthesized molecule. While most design tools offer this fundamental capability, they do not follow a systematic approach that guarantees elimination of all unwanted sites whenever a feasible solution exists. Furthermore, the algorithms these tools use (when published) are often quite naive and inefficient. We present a formal description of the binding site elimination problem and suggest several efficient algorithms that eliminate unwanted patterns with minimum interference to the desired function of the synthesized sequence. These algorithms are simple, efficient, and flexible and, therefore, can be easily incorporated in all existing DNA design tools, enhancing their design capabilities.
Collapse
Affiliation(s)
- Zehavit Leibovich
- Efi Arazi School of Computer Science, Reichman University, Herzliya, Israel
| | - Ilan Gronau
- Efi Arazi School of Computer Science, Reichman University, Herzliya, Israel
| |
Collapse
|
6
|
Watts A, Sankaranarayanan S, Watts A, Raipuria RK. Optimizing protein expression in heterologous system: Strategies and tools. Meta Gene 2021. [DOI: 10.1016/j.mgene.2021.100899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
|
7
|
Zulkower V, Rosser S. DNA Chisel, a versatile sequence optimizer. Bioinformatics 2021; 36:4508-4509. [PMID: 32647895 DOI: 10.1093/bioinformatics/btaa558] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 02/26/2020] [Accepted: 07/01/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Accounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine. RESULTS DNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations. AVAILABILITY AND IMPLEMENTATION The framework is available as a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (see at https://github.com/Edinburgh-Genome-Foundry/DNAChisel for code and documentation). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Valentin Zulkower
- Edinburgh Genome Foundry, SynthSys, School of Biological Sciences, University of Edinburgh, Edinburgh EH93BF, UK
| | - Susan Rosser
- Edinburgh Genome Foundry, SynthSys, School of Biological Sciences, University of Edinburgh, Edinburgh EH93BF, UK
| |
Collapse
|
8
|
Gilman J, Walls L, Bandiera L, Menolascina F. Statistical Design of Experiments for Synthetic Biology. ACS Synth Biol 2021; 10:1-18. [PMID: 33406821 DOI: 10.1021/acssynbio.0c00385] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.
Collapse
Affiliation(s)
- James Gilman
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Laura Walls
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Lucia Bandiera
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Filippo Menolascina
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| |
Collapse
|
9
|
Şen A, Kargar K, Akgün E, Pınar MÇ. Codon optimization: a mathematical programing approach. Bioinformatics 2020; 36:4012-4020. [DOI: 10.1093/bioinformatics/btaa248] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 11/25/2019] [Accepted: 04/13/2020] [Indexed: 12/11/2022] Open
Abstract
AbstractMotivationSynthesizing proteins in heterologous hosts is an important tool in biotechnology. However, the genetic code is degenerate and the codon usage is biased in many organisms. Synonymous codon changes that are customized for each host organism may have a significant effect on the level of protein expression. This effect can be measured by using metrics, such as codon adaptation index, codon pair bias, relative codon bias and relative codon pair bias. Codon optimization is designing codons that improve one or more of these objectives. Currently available algorithms and software solutions either rely on heuristics without providing optimality guarantees or are very rigid in modeling different objective functions and restrictions.ResultsWe develop an effective mixed integer linear programing (MILP) formulation, which considers multiple objectives. Our numerical study shows that this formulation can be effectively used to generate (Pareto) optimal codon designs even for very long amino acid sequences using a standard commercial solver. We also show that one can obtain designs in the efficient frontier in reasonable solution times and incorporate other complex objectives, such as mRNA secondary structures in codon design using MILP formulations.Availability and implementationhttp://alpersen.bilkent.edu.tr/codonoptimization/CodonOptimization.zip.
Collapse
Affiliation(s)
- Alper Şen
- Department of Industrial Engineering, Bilkent University, Ankara 06800, Turkey
| | - Kamyar Kargar
- Department of Industrial Engineering, Bilkent University, Ankara 06800, Turkey
| | - Esma Akgün
- Department of Management Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Mustafa Ç Pınar
- Department of Industrial Engineering, Bilkent University, Ankara 06800, Turkey
| |
Collapse
|
10
|
Stepwise optimization of recombinant protein production in Escherichia coli utilizing computational and experimental approaches. Appl Microbiol Biotechnol 2020; 104:3253-3266. [DOI: 10.1007/s00253-020-10454-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 01/28/2020] [Accepted: 02/07/2020] [Indexed: 12/14/2022]
|
11
|
Gonzalez-Sanchez B, Vega-Rodríguez MA, Santander-Jiménez S. Multi-objective protein encoding: Redefinition of the problem, new problem-aware operators, and approach based on Variable Neighborhood Search. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.05.088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
12
|
Liu SS, Hockenberry AJ, Jewett MC, Amaral LAN. A novel framework for evaluating the performance of codon usage bias metrics. J R Soc Interface 2019; 15:rsif.2017.0667. [PMID: 29386398 DOI: 10.1098/rsif.2017.0667] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 01/04/2018] [Indexed: 11/12/2022] Open
Abstract
The unequal utilization of synonymous codons affects numerous cellular processes including translation rates, protein folding and mRNA degradation. In order to understand the biological impact of variable codon usage bias (CUB) between genes and genomes, it is crucial to be able to accurately measure CUB for a given sequence. A large number of metrics have been developed for this purpose, but there is currently no way of systematically testing the accuracy of individual metrics or knowing whether metrics provide consistent results. This lack of standardization can result in false-positive and false-negative findings if underpowered or inaccurate metrics are applied as tools for discovery. Here, we show that the choice of CUB metric impacts both the significance and measured effect sizes in numerous empirical datasets, raising questions about the generality of findings in published research. To bring about standardization, we developed a novel method to create synthetic protein-coding DNA sequences according to different models of codon usage. We use these benchmark sequences to identify the most accurate and robust metrics with regard to sequence length, GC content and amino acid heterogeneity. Finally, we show how our benchmark can aid the development of new metrics by providing feedback on its performance compared to the state of the art.
Collapse
Affiliation(s)
- Sophia S Liu
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
| | - Adam J Hockenberry
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA.,Interdisciplinary Program in Biological Sciences, Northwestern University, Evanston, IL, USA
| | - Michael C Jewett
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA .,Interdisciplinary Program in Biological Sciences, Northwestern University, Evanston, IL, USA.,Center for Synthetic Biology, Northwestern University, Evanston, IL, USA.,Simpson Querrey BioNanotechnology Institute, Northwestern University, Evanston, IL, USA.,Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
| | - Luís A N Amaral
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA .,Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA.,Department of Physics and Astronomy, Northwestern University, Evanston, IL, USA
| |
Collapse
|
13
|
Kent R, Dixon N. Systematic Evaluation of Genetic and Environmental Factors Affecting Performance of Translational Riboswitches. ACS Synth Biol 2019; 8:884-901. [PMID: 30897329 PMCID: PMC6492952 DOI: 10.1021/acssynbio.9b00017] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Indexed: 12/11/2022]
Abstract
Since their discovery, riboswitches have been attractive tools for the user-controlled regulation of gene expression in bacterial systems. Riboswitches facilitate small molecule mediated fine-tuning of protein expression, making these tools of great use to the synthetic biology community. However, the use of riboswitches is often restricted due to context dependent performance and limited dynamic range. Here, we report the drastic improvement of a previously developed orthogonal riboswitch achieved through in vivo functional selection and optimization of flanking coding and noncoding sequences. The behavior of the derived riboswitches was mapped under a wide array of growth and induction conditions, using a structured Design of Experiments approach. This approach successfully improved the maximal protein expression levels 8.2-fold relative to the original riboswitches, and the dynamic range was improved to afford riboswitch dependent control of 80-fold. The optimized orthogonal riboswitch was then integrated downstream of four endogenous stress promoters, responsive to phosphate starvation, hyperosmotic stress, redox stress, and carbon starvation. These responsive stress promoter-riboswitch devices were demonstrated to allow for tuning of protein expression up to ∼650-fold in response to both environmental and cellular stress responses and riboswitch dependent attenuation. We envisage that these riboswitch stress responsive devices will be useful tools for the construction of advanced genetic circuits, bioprocessing, and protein expression.
Collapse
Affiliation(s)
- R. Kent
- Manchester Institute of Biotechnology,
School of Chemistry, University of Manchester, Manchester M13 9PL, United Kingdom
| | - N. Dixon
- Manchester Institute of Biotechnology,
School of Chemistry, University of Manchester, Manchester M13 9PL, United Kingdom
| |
Collapse
|
14
|
de Ridder D. Artificial intelligence in the lab: ask not what your computer can do for you. Microb Biotechnol 2019; 12:38-40. [PMID: 30246499 PMCID: PMC6302702 DOI: 10.1111/1751-7915.13317] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 08/30/2018] [Indexed: 02/05/2023] Open
Affiliation(s)
- Dick de Ridder
- Bioinformatics GroupWageningen University & ResearchDroevendaalsesteeg 16708 PB WageningenThe Netherlands
| |
Collapse
|
15
|
Multi-Objective Artificial Bee Colony for designing multiple genes encoding the same protein. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.10.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
|
16
|
Cambray G, Guimaraes JC, Arkin AP. Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli. Nat Biotechnol 2018; 36:1005-1015. [DOI: 10.1038/nbt.4238] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 08/02/2018] [Indexed: 01/01/2023]
|
17
|
Webster GR, Teh AYH, Ma JKC. Synthetic gene design-The rationale for codon optimization and implications for molecular pharming in plants. Biotechnol Bioeng 2016; 114:492-502. [PMID: 27618314 DOI: 10.1002/bit.26183] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 08/10/2016] [Accepted: 09/05/2016] [Indexed: 12/14/2022]
Abstract
Degeneracy in the genetic code allows multiple codon sequences to encode the same protein. Codon usage bias in genes is the term given to the preferred use of particular synonymous codons. Synonymous codon substitutions had been regarded as "silent" as the primary structure of the protein was not affected; however, it is now accepted that synonymous substitutions can have a significant effect on heterologous protein expression. Codon optimization, the process of altering codons within the gene sequence to improve recombinant protein expression, has become widely practised. Multiple inter-linked factors affecting protein expression need to be taken into consideration when optimizing a gene sequence. Over the years, various computer programmes have been developed to aid in the gene sequence optimization process. However, as the rulebook for altering codon usage to affect protein expression is still not completely understood, it is difficult to predict which strategy, if any, will design the "optimal" gene sequence. In this review, codon usage bias and factors affecting codon selection will be discussed and the evidence for codon optimization impact will be reviewed for recombinant protein expression using plants as a case study. These developments will be relevant to all recombinant expression systems; however, molecular pharming in plants is an area which has consistently encountered difficulties with low levels of recombinant protein expression, and should benefit from an evidence based rational approach to synthetic gene design. Biotechnol. Bioeng. 2017;114: 492-502. © 2016 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Gina R Webster
- Molecular Immunology Unit, Institute for Infection and Immunity, St. George's University of London, SW17 0RE, London, UK
| | - Audrey Y-H Teh
- Molecular Immunology Unit, Institute for Infection and Immunity, St. George's University of London, SW17 0RE, London, UK
| | - Julian K-C Ma
- Molecular Immunology Unit, Institute for Infection and Immunity, St. George's University of London, SW17 0RE, London, UK
| |
Collapse
|
18
|
Critical reflections on synthetic gene design for recombinant protein expression. Curr Opin Struct Biol 2016; 38:155-62. [DOI: 10.1016/j.sbi.2016.07.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Revised: 06/29/2016] [Accepted: 07/06/2016] [Indexed: 11/17/2022]
|
19
|
Abstract
A central challenge in the field of metabolic engineering is the efficient identification of a metabolic pathway genotype that maximizes specific productivity over a robust range of process conditions. Here we review current methods for optimizing specific productivity of metabolic pathways in living cells. New tools for library generation, computational analysis of pathway sequence-flux space, and high-throughput screening and selection techniques are discussed.
Collapse
Affiliation(s)
- Justin R Klesmith
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Timothy A Whitehead
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA; Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA
| |
Collapse
|
20
|
The Art of Gene Redesign and Recombinant Protein Production: Approaches and Perspectives. TOPICS IN MEDICINAL CHEMISTRY 2016. [DOI: 10.1007/7355_2016_2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
21
|
Qi H, Li BZ, Zhang WQ, Liu D, Yuan YJ. Modularization of genetic elements promotes synthetic metabolic engineering. Biotechnol Adv 2015; 33:1412-9. [DOI: 10.1016/j.biotechadv.2015.04.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2014] [Revised: 01/12/2015] [Accepted: 04/05/2015] [Indexed: 01/24/2023]
|
22
|
Gould N, Hendy O, Papamichail D. Computational tools and algorithms for designing customized synthetic genes. Front Bioeng Biotechnol 2014; 2:41. [PMID: 25340050 PMCID: PMC4186344 DOI: 10.3389/fbioe.2014.00041] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Accepted: 09/16/2014] [Indexed: 11/13/2022] Open
Abstract
Advances in DNA synthesis have enabled the construction of artificial genes, gene circuits, and genomes of bacterial scale. Freedom in de novo design of synthetic constructs provides significant power in studying the impact of mutations in sequence features, and verifying hypotheses on the functional information that is encoded in nucleic and amino acids. To aid this goal, a large number of software tools of variable sophistication have been implemented, enabling the design of synthetic genes for sequence optimization based on rationally defined properties. The first generation of tools dealt predominantly with singular objectives such as codon usage optimization and unique restriction site incorporation. Recent years have seen the emergence of sequence design tools that aim to evolve sequences toward combinations of objectives. The design of optimal protein-coding sequences adhering to multiple objectives is computationally hard, and most tools rely on heuristics to sample the vast sequence design space. In this review, we study some of the algorithmic issues behind gene optimization and the approaches that different tools have adopted to redesign genes and optimize desired coding features. We utilize test cases to demonstrate the efficiency of each approach, as well as identify their strengths and limitations.
Collapse
Affiliation(s)
- Nathan Gould
- Department of Computer Science, The College of New Jersey , Ewing, NJ , USA
| | - Oliver Hendy
- Department of Biology, The College of New Jersey , Ewing, NJ , USA
| | | |
Collapse
|