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dc.contributor.authorScott, L.H.
dc.contributor.authorMathews, J.C.
dc.contributor.authorFilipovska, A.
dc.contributor.authorRackham, Oliver
dc.contributor.editorShukla, AK
dc.date.accessioned2023-03-14T08:18:47Z
dc.date.available2023-03-14T08:18:47Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/20.500.11937/90960
dc.identifier.doi10.1016/bs.mie.2019.11.003
dc.description.abstract

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.

dc.languageEnglish
dc.publisherACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD
dc.relation.urihttps://research-repository.uwa.edu.au/files/81276020/Scott_et_al._Author_Manuscript.pdf
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/DP180101656
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.subjectBiochemical Research Methods
dc.subjectBiochemistry & Molecular Biology
dc.subjectCell Biology
dc.subjectYEAST
dc.subjectRESISTANCE
dc.subjectPARALLEL
dc.subjectTOOLKIT
dc.subjectSYSTEM
dc.subjectTRANSFORMATION
dc.subjectRECOGNITION
dc.subjectTIGECYCLINE
dc.subjectEXPRESSION
dc.subjectEFFICIENCY
dc.subjectAntibiotic resistance
dc.subjectBiosensor
dc.subjectDeep mutational scanning
dc.subjectGenetic circuit
dc.subjectStructure-function relationship
dc.subjectSynthetic biology
dc.subjectGene Regulatory Networks
dc.subjectMutant Proteins
dc.subjectMutation
dc.subjectProteins
dc.subjectSaccharomyces cerevisiae
dc.subjectSaccharomyces cerevisiae
dc.subjectProteins
dc.subjectMutation
dc.subjectMutant Proteins
dc.subjectGene Regulatory Networks
dc.titleBuilding artificial genetic circuits to understand protein function
dc.typeBook Chapter
dcterms.source.volume633
dcterms.source.startPage231
dcterms.source.endPage250
dcterms.source.titleMethods in Enzymology
dcterms.source.seriesMethods in Enzymology
dcterms.source.isbn9780128191286
dc.date.updated2023-03-14T08:18:47Z
curtin.departmentCurtin Medical School
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Health Sciences
curtin.contributor.orcidRackham, Oliver [0000-0002-5301-9624]
curtin.repositoryagreementV3


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