Evidence map›Paper›PMID 14500881›Full record

ArticleProtein science : a publication of the Protein Society2003

Finding evolutionary relations beyond superfamilies: fold-based superfamilies.

Keiko Matsuda, Takaaki Nishioka, Kengo Kinoshita, Takeshi Kawabata, Nobuhiro Go

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Keiko MatsudaGraduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0101, Japan.
Takaaki Nishioka
Kengo Kinoshita
Takeshi Kawabata
Nobuhiro Go

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Superfamily classifications are based variably on similarity of sequences, global folds, local structures, or functions. We have examined the possibility of defining superfamilies purely from the viewpoint of the global fold/function relationship. For this purpose, we first classified protein domains according to the beta-sheet topology. We then introduced the concept of kinship relations among the classified beta-sheet topology by assuming that the major elementary event leading to creation of a new beta-sheet topology is either an addition or deletion of one beta-strand at the edge of an existing beta-sheet during the molecular evolution. Based on this kinship relation, a network of protein domains was constructed so that the distance between a pair of domains represents the number of evolutionary events that lead one from the other domain. We then mapped on it all known domains with a specific core chemical function (here taken, as an example, that involving ATP or its analogs). Careful analyses revealed that the domains are found distributed on the network as >20 mutually disjointed clusters. The proteins in each cluster are defined to form a fold-based superfamily. The results indicate that >20 ATP-binding protein superfamilies have been invented independently in the process of molecular evolution, and the conservative evolutionary diffusion of global folds and functions is the origin of the relationship between them.

Indexed as

Evolution, MolecularProtein FoldingAdenosine TriphosphateBinding SitesCatalysisCatalytic DomainDatabases, ProteinEnzymesModels, MolecularProtein ConformationProteinsProtein Structure, SecondaryProtein Structure, TertiaryStructural Homology, ProteinStructure-Activity RelationshipAdenosine TriphosphateEnzymesProteins

Identifiers

PMID14500881
PMCPMC2366925

What OpenQuestion holds

Textmetadata
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.