Published May 20, 2024 | Version v1
Journal article Open

Epistasis facilitates functional evolution in an ancient transcription factor

Description

A protein's genetic architecture – the set of causal rules by which its sequence produces its functions – also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest – excluding the vast majority of possible genotypes and evolutionary trajectories – and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor's specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor's capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.

Data availability

The coding scripts for running all analyses are located on Github (https://github.com/JoeThorntonLab/DBD.GeneticARchitecture copy archived at JoeThorntonLab, 2023). Initial and intermediate data files can be found at dryad (https://doi.org/10.5061/dryad.jsxksn0hk).

The following data sets were generated:

Metzger BPH Park Y Starr TN Thornton JW (2024) Dryad Digital Repository Epistasis facilitates functional evolution in an ancient transcription factor. https://datadryad.org/stash/dataset/doi:10.5061/dryad.jsxksn0hk

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Additional details

Identifiers

DOI
10.7554/eLife.88737.3
Other
oai:uchicago.tind.io:12089

Funding

National Institutes of Health
F32-GM122251
National Institutes of Health
R01-GM131128
National Institutes of Health
R01-GM121931
National Institutes of Health
R01-GM139007
Samsung
National Institutes of Health
R35-GM14533601

UChicago Information

Division(s)
Biological Sciences Division
Department(s)
Biochemistry and Molecular Biology, Biophysical Sciences, Ecology and Evolution, Genetics, Genomics, and Systems Biology, Human Genetics