Building artificial genetic circuits to understand protein function

Intrinsic protein properties that may not be apparent by only examining three-dimensional structures can be revealed by careful analysis of mutant protein variants. Deep mutational scanning is a technique that allows the functional analysis of millions of protein variants in a single experiment. To...

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Main Authors: Scott, L.H., Mathews, J.C., Filipovska, A., Rackham, Oliver
Other Authors: Shukla, AK
Format: Book Chapter
Language:English
Published: ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD 2020
Subjects:
Online Access:https://research-repository.uwa.edu.au/files/81276020/Scott_et_al._Author_Manuscript.pdf
http://hdl.handle.net/20.500.11937/90960
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author Scott, L.H.
Mathews, J.C.
Filipovska, A.
Rackham, Oliver
author2 Shukla, AK
author_facet Shukla, AK
Scott, L.H.
Mathews, J.C.
Filipovska, A.
Rackham, Oliver
author_sort Scott, L.H.
building Curtin Institutional Repository
collection Online Access
description Intrinsic protein properties that may not be apparent by only examining three-dimensional structures can be revealed by careful analysis of mutant protein variants. Deep mutational scanning is a technique that allows the functional analysis of millions of protein variants in a single experiment. To enable this high-throughput technique, the mutant genotype of protein variants must be coupled to a selectable function. This chapter outlines how artificial genetic circuits in the yeast Saccharomyces cerevisiae can maintain the genotype-phenotype link, thus enabling the general application of this approach. To do this, we describe how to engineer genetic selections in yeast, methods to construct mutant libraries, and how to analyze sequencing data. We investigate the structure-function relationships of the antimicrobial resistance protein TetX to illustrate this process. In doing so, we demonstrate that deep mutational scanning is a powerful method to dissect the importance of individual residues for the inactivation of antibiotic analogues, with consequences for the rational design of new drugs to combat antimicrobial resistance.
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institution Curtin University Malaysia
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spelling curtin-20.500.11937-909602023-06-13T08:47:36Z Building artificial genetic circuits to understand protein function Scott, L.H. Mathews, J.C. Filipovska, A. Rackham, Oliver Shukla, AK Science & Technology Life Sciences & Biomedicine Biochemical Research Methods Biochemistry & Molecular Biology Cell Biology YEAST RESISTANCE PARALLEL TOOLKIT SYSTEM TRANSFORMATION RECOGNITION TIGECYCLINE EXPRESSION EFFICIENCY Antibiotic resistance Biosensor Deep mutational scanning Genetic circuit Structure-function relationship Synthetic biology Gene Regulatory Networks Mutant Proteins Mutation Proteins Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins Mutation Mutant Proteins Gene Regulatory Networks Intrinsic protein properties that may not be apparent by only examining three-dimensional structures can be revealed by careful analysis of mutant protein variants. Deep mutational scanning is a technique that allows the functional analysis of millions of protein variants in a single experiment. To enable this high-throughput technique, the mutant genotype of protein variants must be coupled to a selectable function. This chapter outlines how artificial genetic circuits in the yeast Saccharomyces cerevisiae can maintain the genotype-phenotype link, thus enabling the general application of this approach. To do this, we describe how to engineer genetic selections in yeast, methods to construct mutant libraries, and how to analyze sequencing data. We investigate the structure-function relationships of the antimicrobial resistance protein TetX to illustrate this process. In doing so, we demonstrate that deep mutational scanning is a powerful method to dissect the importance of individual residues for the inactivation of antibiotic analogues, with consequences for the rational design of new drugs to combat antimicrobial resistance. 2020 Book Chapter http://hdl.handle.net/20.500.11937/90960 10.1016/bs.mie.2019.11.003 English https://research-repository.uwa.edu.au/files/81276020/Scott_et_al._Author_Manuscript.pdf http://purl.org/au-research/grants/arc/DP180101656 ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD restricted
spellingShingle Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Biochemistry & Molecular Biology
Cell Biology
YEAST
RESISTANCE
PARALLEL
TOOLKIT
SYSTEM
TRANSFORMATION
RECOGNITION
TIGECYCLINE
EXPRESSION
EFFICIENCY
Antibiotic resistance
Biosensor
Deep mutational scanning
Genetic circuit
Structure-function relationship
Synthetic biology
Gene Regulatory Networks
Mutant Proteins
Mutation
Proteins
Saccharomyces cerevisiae
Saccharomyces cerevisiae
Proteins
Mutation
Mutant Proteins
Gene Regulatory Networks
Scott, L.H.
Mathews, J.C.
Filipovska, A.
Rackham, Oliver
Building artificial genetic circuits to understand protein function
title Building artificial genetic circuits to understand protein function
title_full Building artificial genetic circuits to understand protein function
title_fullStr Building artificial genetic circuits to understand protein function
title_full_unstemmed Building artificial genetic circuits to understand protein function
title_short Building artificial genetic circuits to understand protein function
title_sort building artificial genetic circuits to understand protein function
topic Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Biochemistry & Molecular Biology
Cell Biology
YEAST
RESISTANCE
PARALLEL
TOOLKIT
SYSTEM
TRANSFORMATION
RECOGNITION
TIGECYCLINE
EXPRESSION
EFFICIENCY
Antibiotic resistance
Biosensor
Deep mutational scanning
Genetic circuit
Structure-function relationship
Synthetic biology
Gene Regulatory Networks
Mutant Proteins
Mutation
Proteins
Saccharomyces cerevisiae
Saccharomyces cerevisiae
Proteins
Mutation
Mutant Proteins
Gene Regulatory Networks
url https://research-repository.uwa.edu.au/files/81276020/Scott_et_al._Author_Manuscript.pdf
https://research-repository.uwa.edu.au/files/81276020/Scott_et_al._Author_Manuscript.pdf
http://hdl.handle.net/20.500.11937/90960