Literature DB >> 34137436

Protein engineering for natural product biosynthesis and synthetic biology applications.

Miles A Calzini1, Alexandra A Malico1, Melissa M Mitchler1, Gavin J Williams1,2.   

Abstract

As protein engineering grows more salient, many strategies have emerged to alter protein structure and function, with the goal of redesigning and optimizing natural product biosynthesis. Computational tools, including machine learning and molecular dynamics simulations, have enabled the rational mutagenesis of key catalytic residues for enhanced or altered biocatalysis. Semi-rational, directed evolution and microenvironment engineering strategies have optimized catalysis for native substrates and increased enzyme promiscuity beyond the scope of traditional rational approaches. These advances are made possible using novel high-throughput screens, including designer protein-based biosensors with engineered ligand specificity. Herein, we detail the most recent of these advances, focusing on polyketides, non-ribosomal peptides and isoprenoids, including their native biosynthetic logic to provide clarity for future applications of these technologies for natural product synthetic biology.
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  biosynthesis; natural products; peptides; polyketides; synthetic biology; terpenes

Mesh:

Substances:

Year:  2021        PMID: 34137436      PMCID: PMC8209613          DOI: 10.1093/protein/gzab015

Source DB:  PubMed          Journal:  Protein Eng Des Sel        ISSN: 1741-0126            Impact factor:   1.952


  62 in total

Review 1.  Natural Products as Sources of New Drugs from 1981 to 2014.

Authors:  David J Newman; Gordon M Cragg
Journal:  J Nat Prod       Date:  2016-02-07       Impact factor: 4.050

2.  Engineering the Substrate Specificity of a Modular Polyketide Synthase for Installation of Consecutive Non-Natural Extender Units.

Authors:  Edward Kalkreuter; Jared M CroweTipton; Andrew N Lowell; David H Sherman; Gavin J Williams
Journal:  J Am Chem Soc       Date:  2019-01-24       Impact factor: 15.419

3.  Fundamental Design Principles for Transcription-Factor-Based Metabolite Biosensors.

Authors:  Ahmad A Mannan; Di Liu; Fuzhong Zhang; Diego A Oyarzún
Journal:  ACS Synth Biol       Date:  2017-08-09       Impact factor: 5.110

Review 4.  Genetic Biosensor Design for Natural Product Biosynthesis in Microorganisms.

Authors:  Gazi Sakir Hossain; Mukesh Saini; Ryoma Miyake; Hua Ling; Matthew Wook Chang
Journal:  Trends Biotechnol       Date:  2020-04-28       Impact factor: 19.536

5.  Combining genetically-encoded biosensors with high throughput strain screening to maximize erythritol production in Yarrowia lipolytica.

Authors:  Xueliang Qiu; Peng Xu; Xinrui Zhao; Guocheng Du; Juan Zhang; Jianghua Li
Journal:  Metab Eng       Date:  2020-03-26       Impact factor: 9.783

6.  Enhancing Biosynthesis of a Ginsenoside Precursor by Self-Assembly of Two Key Enzymes in Pichia pastoris.

Authors:  Chengcheng Zhao; Xin Gao; Xinbin Liu; Yong Wang; Shengli Yang; Fengqing Wang; Yuhong Ren
Journal:  J Agric Food Chem       Date:  2016-04-21       Impact factor: 5.279

Review 7.  Two distinct pathways for essential metabolic precursors for isoprenoid biosynthesis.

Authors:  Tomohisa Kuzuyama; Haruo Seto
Journal:  Proc Jpn Acad Ser B Phys Biol Sci       Date:  2012       Impact factor: 3.493

8.  SIME: synthetic insight-based macrolide enumerator to generate the V1B library of 1 billion macrolides.

Authors:  Phyo Phyo Kyaw Zin; Gavin Williams; Denis Fourches
Journal:  J Cheminform       Date:  2020-04-10       Impact factor: 5.514

9.  Engineering of CYP76AH15 can improve activity and specificity towards forskolin biosynthesis in yeast.

Authors:  Victor Forman; Niels Bjerg-Jensen; Jane D Dyekjær; Birger Lindberg Møller; Irini Pateraki
Journal:  Microb Cell Fact       Date:  2018-11-19       Impact factor: 5.328

Review 10.  FoldX as Protein Engineering Tool: Better Than Random Based Approaches?

Authors:  Oliver Buß; Jens Rudat; Katrin Ochsenreither
Journal:  Comput Struct Biotechnol J       Date:  2018-02-03       Impact factor: 7.271

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