Building artificial genetic circuits to understand protein function
dc.contributor.author | Scott, L.H. | |
dc.contributor.author | Mathews, J.C. | |
dc.contributor.author | Filipovska, A. | |
dc.contributor.author | Rackham, Oliver | |
dc.contributor.editor | Shukla, AK | |
dc.date.accessioned | 2023-03-14T08:18:47Z | |
dc.date.available | 2023-03-14T08:18:47Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/90960 | |
dc.identifier.doi | 10.1016/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.language | English | |
dc.publisher | ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD | |
dc.relation.uri | https://research-repository.uwa.edu.au/files/81276020/Scott_et_al._Author_Manuscript.pdf | |
dc.relation.sponsoredby | http://purl.org/au-research/grants/arc/DP180101656 | |
dc.subject | Science & Technology | |
dc.subject | Life Sciences & Biomedicine | |
dc.subject | Biochemical Research Methods | |
dc.subject | Biochemistry & Molecular Biology | |
dc.subject | Cell Biology | |
dc.subject | YEAST | |
dc.subject | RESISTANCE | |
dc.subject | PARALLEL | |
dc.subject | TOOLKIT | |
dc.subject | SYSTEM | |
dc.subject | TRANSFORMATION | |
dc.subject | RECOGNITION | |
dc.subject | TIGECYCLINE | |
dc.subject | EXPRESSION | |
dc.subject | EFFICIENCY | |
dc.subject | Antibiotic resistance | |
dc.subject | Biosensor | |
dc.subject | Deep mutational scanning | |
dc.subject | Genetic circuit | |
dc.subject | Structure-function relationship | |
dc.subject | Synthetic biology | |
dc.subject | Gene Regulatory Networks | |
dc.subject | Mutant Proteins | |
dc.subject | Mutation | |
dc.subject | Proteins | |
dc.subject | Saccharomyces cerevisiae | |
dc.subject | Saccharomyces cerevisiae | |
dc.subject | Proteins | |
dc.subject | Mutation | |
dc.subject | Mutant Proteins | |
dc.subject | Gene Regulatory Networks | |
dc.title | Building artificial genetic circuits to understand protein function | |
dc.type | Book Chapter | |
dcterms.source.volume | 633 | |
dcterms.source.startPage | 231 | |
dcterms.source.endPage | 250 | |
dcterms.source.title | Methods in Enzymology | |
dcterms.source.series | Methods in Enzymology | |
dcterms.source.isbn | 9780128191286 | |
dc.date.updated | 2023-03-14T08:18:47Z | |
curtin.department | Curtin Medical School | |
curtin.accessStatus | Fulltext not available | |
curtin.faculty | Faculty of Health Sciences | |
curtin.contributor.orcid | Rackham, Oliver [0000-0002-5301-9624] | |
curtin.repositoryagreement | V3 |
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