Viewing protein fitness landscapes through a next-gen lens

dc.contributor.author Boucher, Jeffrey I.
dc.contributor.author Cote, Pamela
dc.contributor.author Flynn, Julia
dc.contributor.author Jiang, Li
dc.contributor.author Laban, Aneth
dc.contributor.author Mishra, Parul
dc.contributor.author Roscoe, Benjamin P.
dc.contributor.author Bolon, Daniel N.A.
dc.date.accessioned 2022-03-27T01:03:22Z
dc.date.available 2022-03-27T01:03:22Z
dc.date.issued 2014-10-01
dc.description.abstract High-throughput sequencing has enabled many powerful approaches in biological research. Here, we review sequencing approaches to measure frequency changes within engineered mutational libraries subject to selection. These analyses can provide direct estimates of biochemical and fitness effects for all individual mutations across entire genes (and likely compact genomes in the near future) in genetically tractable systems such as microbes, viruses, and mammalian cells. The effects of mutations on experimental fitness can be assessed using sequencing to monitor time-dependent changes in mutant frequency during bulk competitions. The impact of mutations on biochemical functions can be determined using reporters or other means of separating variants based on individual activities (e.g., binding affinity for a partner molecule can be interrogated using surface display of libraries of mutant proteins and isolation of bound and unbound populations). The comprehensive investigation of mutant effects on both biochemical function and experimental fitness provide promising new avenues to investigate the connections between biochemistry, cell physiology, and evolution. We summarize recent findings from systematic mutational analyses; describe how they relate to a field rich in both theory and experimentation; and highlight how they may contribute to ongoing and future research into protein structure–function relationships, systems-level descriptions of cell physiology, and population-genetic inferences on the relative contributions of selection and drift.
dc.identifier.citation Genetics. v.198(2)
dc.identifier.issn 00166731
dc.identifier.uri 10.1534/genetics.114.168351
dc.identifier.uri https://academic.oup.com/genetics/article/198/2/461/5935945
dc.identifier.uri https://dspace.uohyd.ac.in/handle/1/4014
dc.title Viewing protein fitness landscapes through a next-gen lens
dc.type Journal. Article
dspace.entity.type
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