| Popis: |
A protein’s genetic architecture – the set of causal rules by which its sequence determines its specific functions – also determines the functional impacts of mutations and the protein’s evolutionary potential. Prior research has proposed that proteins’ genetic architecture is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has considered only the amino acid states present in two sequences of interest and the direct paths between them, but real proteins evolve in a multidimensional space of 20 possible amino acids per site. Moreover, almost all prior work has assayed the effect of sequence variation on a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impacts on the evolution of new functions. Here we develop a new logistic regression-based method to directly characterize the global causal rules of the genetic architecture of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We apply 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 and specificity 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. Epistasis also massively expands 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. |
