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The simplicity of protein sequence-function relationships

Affiliations
  • 1Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA.
  • 2Center for RNA Research, Institute for Basic Science, Seoul, Republic of Korea.
  • 3Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.
  • 4Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
  • 5Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA. joet1@uchicago.edu.
  • 6Department of Human Genetics, University of Chicago, Chicago, IL, USA. joet1@uchicago.edu.

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September 11, 2024

Abstract

How complex are the rules by which a protein’s sequence determines its function? High-order epistatic interactions among residues are thought to be pervasive, suggesting an idiosyncratic and unpredictable sequence-function relationship. But many prior studies may have overestimated epistasis, because they analyzed sequence-function relationships relative to a single reference sequence-which causes measurement noise and local idiosyncrasies to snowball into high-order epistasis-or they did not fully account for global nonlinearities. Here we present a reference-free method that jointly infers specific epistatic interactions and global nonlinearity using a bird’s-eye view of sequence space. This technique yields the simplest explanation of sequence-function relationships and is more robust than existing methods to measurement noise, missing data, and model misspecification. We reanalyze 20 experimental datasets and find that context-independent amino acid effects and pairwise interactions, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of phenotypic variance and over 92% in every case. Only a tiny fraction of genotypes are strongly affected by higher-order epistasis. Sequence-function relationships are also sparse: a miniscule fraction of amino acids and interactions account for 90% of phenotypic variance. Sequence-function causality across these datasets is therefore simple, opening the way for tractable approaches to characterize proteins’ genetic architecture.

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