Evidence map›Paper›PMID 17983264›Full record

ArticlePLoS computational biology2007

Detecting coevolution in and among protein domains.

Chen-Hsiang Yeang, David Haussler

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed
6.4field-weighted citation impact, top 2% of its field
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

92 citing papers in PubMed, 172 citations in OpenAlex.

  1. Review
  2. Protein Structural Phylogenetics.Genome biology and evolution · 2025
    Review
  3. Article
  4. Article
  5. A Novel Information-Theory-Based Genetic Distance That Approximates Phenotypic Differences.Journal of computational biology : a journal of computational molecular cell biology · 2023
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Evolutionary conservation of RNA sequence and structure.Wiley interdisciplinary reviews. RNA · 2021
    Review
  11. Article
  12. Article
  13. Article
  14. Simultaneous Bayesian inference of phylogeny and molecular coevolution.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

32 more citing papers are in PubMed but not listed here.

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

2 authors at 2 institutions in 1 country.

Chen-Hsiang YeangSimons Center for Systems Biology, Institute for Advanced Study, Princeton, New Jersey, United States of America. chyeang@ias.edu
David Haussler
Institute for Advanced Study · USUniversity of California, Santa Cruz · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Correlated changes of nucleic or amino acids have provided strong information about the structures and interactions of molecules. Despite the rich literature in coevolutionary sequence analysis, previous methods often have to trade off between generality, simplicity, phylogenetic information, and specific knowledge about interactions. Furthermore, despite the evidence of coevolution in selected protein families, a comprehensive screening of coevolution among all protein domains is still lacking. We propose an augmented continuous-time Markov process model for sequence coevolution. The model can handle different types of interactions, incorporate phylogenetic information and sequence substitution, has only one extra free parameter, and requires no knowledge about interaction rules. We employ this model to large-scale screenings on the entire protein domain database (Pfam). Strikingly, with 0.1 trillion tests executed, the majority of the inferred coevolving protein domains are functionally related, and the coevolving amino acid residues are spatially coupled. Moreover, many of the coevolving positions are located at functionally important sites of proteins/protein complexes, such as the subunit linkers of superoxide dismutase, the tRNA binding sites of ribosomes, the DNA binding region of RNA polymerase, and the active and ligand binding sites of various enzymes. The results suggest sequence coevolution manifests structural and functional constraints of proteins. The intricate relations between sequence coevolution and various selective constraints are worth pursuing at a deeper level.

Indexed as

Evolution, MolecularModels, ChemicalAmino Acid SequenceBase SequenceComputer SimulationModels, MolecularMolecular Sequence DataProtein ConformationProteinsProtein Structure, TertiarySequence AlignmentSequence Analysis, ProteinStructure-Activity RelationshipProteins

Identifiers

PMID17983264
PMCPMC2098842
OpenAlexW2116087481

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.