On inferring epistatic complexity in protein sequence-function landscapes

Clicks: 89
ID: 330660
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #49 of 392 articles by views in current genetics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 392 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract The extent to which epistatic interactions create complexity in protein sequence-function landscapes is highly contested. Numerous empirical studies have found evidence for complex landscapes with widespread high-order epistasis. However, recent work has argued that many of these empirical sequence-function landscapes are in fact much simpler and less epistatic than previously appreciated, based on analysis using a “reference-free” rather than “reference-based” framework for the inference of protein architecture. Here, we show that reference-free and reference-based frameworks are exactly equivalent when inferred using least-squares regression. Because the landscapes analyzed by these approaches are in fact identical, the different conclusions drawn based on the reference-free approach instead reflect different interpretations of the parameters describing the inferred landscapes. Thus, we argue that the choice of framework, the inference method, and the interpretation of the resulting parameters should be driven by the nature of the sequence-function dataset (e.g. the specific protein function being measured) and the motivating biological question (e.g. whether we are interested in explaining phenotypic variance, understanding biochemical properties, or analyzing evolutionary trajectories).
Reference Key
openalex_W7214790472 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Thomas Dupic, Angela M. Phillips, Michael M. Desai
Are you one of these authors? Claim your profile
Journal current genetics
Year 2026
DOI
10.1093/genetics/iyag250
URL
https://doi.org/10.1093/genetics/iyag250
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.