Evidence map›Paper›PMID 38352387›Full record

ArticlebioRxiv : the preprint server for biology2024

Protein sequence landscapes are not so simple: on reference-free versus reference-based inference.

Thomas Dupic, Angela M Phillips, Michael M Desai

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Gauge fixing for sequence-function relationships.PLoS computational biology · 2025
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Thomas DupicDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA.ORCID 0000-0003-1803-1617
Angela M PhillipsDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco CA.
Michael M DesaiDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA.ORCID 0000-0002-9581-1150

Funding

Microbial Adaptation and the Statistics of Epistasis and PleiotropyR01GM104239 · NIGMS · HARVARD UNIVERSITY · PI DESAI, MICHAEL M · 2013 to 2025
$4.5M
NIGMS NIH HHS R01 GM104239
6 · The paper itself

Abstract

In a recent preprint, Park, Metzger, and Thornton reanalyze 20 empirical protein sequence-function landscapes using a "reference-free analysis" (RFA) method they recently developed. They argue that these empirical landscapes are simpler and less epistatic than earlier work suggested, and attribute the difference to limitations of the methods used in the original analyses of these landscapes, which they claim are more sensitive to measurement noise, missing data, and other artifacts. Here, we show that these claims are incorrect. Instead, we find that the RFA method introduced by Park et al. is exactly equivalent to the reference-based least-squares methods used in the original analysis of many of these empirical landscapes (and also equivalent to a Hadamard-based approach they implement). Because the reanalyzed and original landscapes are in fact identical, the different conclusions drawn by Park et al. instead reflect different interpretations of the parameters describing the inferred landscapes; we argue that these do not support the conclusion that epistasis plays only a small role in protein sequence-function landscapes.

Identifiers

PMID38352387
PMCPMC10862727

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.