Learning from protein fitness landscapes: a review of mutability, epistasis, and evolution

Emily C. Hartman, Danielle Tullman-Ercek*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

Proteins carry out many diverse functions in nature and are increasingly used in non-native contexts, such as in medical or industrial applications. A wide array of synthetic biology techniques can be used both to study proteins in their native context and to identify new variants with useful properties for non-native functions. High-resolution protein fitness landscapes, generated via deep scanning mutagenesis, are an emerging technology that can be used to model evolution and identify useful variants. Interestingly, many differences exist between mutability quantified by evolutionary studies and deep scanning mutagenesis. Here, we review several contributing factors to this difference, highlighting epistasis, binding partners, and selection conditions as key contributors. Through this lens, we describe what can be learned, both about evolution and protein function more broadly, from fitness landscape studies.

Original languageEnglish (US)
Pages (from-to)25-31
Number of pages7
JournalCurrent Opinion in Systems Biology
Volume14
DOIs
StatePublished - Apr 2019

ASJC Scopus subject areas

  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Drug Discovery
  • Computer Science Applications
  • Applied Mathematics

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