Recombinant expression of insoluble enzymes in <i>Escherichia coli</i>: a systematic review of experimental design and its manufacturing implications

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Recombinant enzyme expression in Escherichia coli is one of the most popular methods to produce bulk concentrations of protein product. However, this method is often limited by the inadvertent formation of inclusion bodies. Our analysis systematically reviews literature from 2010 to 2021 and details the methods and strategies researchers have utilized for expression of difficult to express (DtE), industrially relevant recombinant enzymes in E. coli expression strains. Our review identifies an absence of a coherent strategy with disparate practices being used to promote solubility. We discuss the potential to approach recombinant expression systematically, with the aid of modern bioinformatics, modelling, and ‘omics’ based systems-level analysis techniques to provide a structured, holistic approach. Our analysis also identifies potential gaps in the methods used to report metadata in publications and the impact on the reproducibility and growth of the research in this field.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01698-w.

Related collections

Most cited references 176

Record: found
Abstract: found
Article: not found

Basic local alignment search tool.

Stephen F Altschul, Warren Gish, Webb Miller … (1990)

A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.

0 comments Cited 8977 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

0 comments Cited 8862 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

The EMBL-EBI search and sequence analysis tools APIs in 2019

Fábio Madeira, Young Park, Joon Lee … (2019)

Abstract The EMBL-EBI provides free access to popular bioinformatics sequence analysis applications as well as to a full-featured text search engine with powerful cross-referencing and data retrieval capabilities. Access to these services is provided via user-friendly web interfaces and via established RESTful and SOAP Web Services APIs (https://www.ebi.ac.uk/seqdb/confluence/display/JDSAT/EMBL-EBI+Web+Services+APIs+-+Data+Retrieval). Both systems have been developed with the same core principles that allow them to integrate an ever-increasing volume of biological data, making them an integral part of many popular data resources provided at the EMBL-EBI. Here, we describe the latest improvements made to the frameworks which enhance the interconnectivity between public EMBL-EBI resources and ultimately enhance biological data discoverability, accessibility, interoperability and reusability.

0 comments Cited 1619 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Duygu Dikicioglu:

ORCID: http://orcid.org/0000-0002-3018-4790

d.dikcioglu@ucl.ac.uk

Journal

Journal ID (nlm-ta): Microb Cell Fact

Journal ID (iso-abbrev): Microb Cell Fact

Title: Microbial Cell Factories

Publisher: BioMed Central (London )

ISSN (Electronic): 1475-2859

Publication date (Electronic): 30 October 2021

Publication date PMC-release: 30 October 2021

Publication date Collection: 2021

Volume: 20

Electronic Location Identifier: 208

Affiliations

[1 ]GRID grid.5335.0, ISNI 0000000121885934, Department of Chemical Engineering and Biotechnology, , University of Cambridge, ; Cambridge, CB3 0AS UK

[2 ]GRID grid.83440.3b, ISNI 0000000121901201, Department of Biochemical Engineering, , University College London, ; London, WC1E 6BT UK

Author information

Duygu Dikicioglu http://orcid.org/0000-0002-3018-4790

Article

Publisher ID: 1698

DOI: 10.1186/s12934-021-01698-w

PMC ID: 8557517

PubMed ID: 34717620

SO-VID: ce26c61e-8fe3-42eb-b550-53246925fafa

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 19 August 2021

Date accepted : 22 October 2021

Funding

Funded by: Johnson Matthey Cambridge

Custom metadata

ScienceOpen disciplines: Biotechnology

Keywords: escherichia coli,recombinant industrial enzymes,difficult to express enzymes,inclusion bodies,systems biology,solubility

Data availability:

ScienceOpen disciplines: Biotechnology

Keywords: escherichia coli, recombinant industrial enzymes, difficult to express enzymes, inclusion bodies, systems biology, solubility

Recombinant expression of insoluble enzymes in Escherichia coli: a systematic review of experimental design and its manufacturing implications

Read this article at

Abstract

Supplementary Information

Related collections

iGEM

Most cited references 176

Basic local alignment search tool.

Highly accurate protein structure prediction with AlphaFold

The EMBL-EBI search and sequence analysis tools APIs in 2019

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 147

Cited by 6

Most referenced authors 4,066