Evidence map›Paper›PMID 9501159›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America1998

Computational learning reveals coiled coil-like motifs in histidine kinase linker domains.

M Singh, B Berger, P S Kim, J M Berger, A G Cochran

Open access · greenAbstract readComparative Study
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 1998. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

25 citing papers in PubMed, 78 citations in OpenAlex.

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  13. A conserved trimerization motif controls the topology of short coiled coils.Proceedings of the National Academy of Sciences of the United States of America · 2005
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  15. Diversity in chemotaxis mechanisms among the bacteria and archaea.Microbiology and molecular biology reviews : MMBR · 2004
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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 at 1 institution in 1 country.

M SinghCenter for Discrete Mathematics and Theoretical Computer Science (DIMACS) and Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.
B Berger
P S Kim
J M Berger
A G Cochran
Whitehead Institute for Biomedical Research · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent rapid growth of protein sequence databases is outpacing the capacity of researchers to biochemically and structurally characterize new proteins. Accordingly, new methods for recognition of motifs and homologies in protein primary sequences may be useful in determining how these proteins might function. We have applied such a method, an iterative learning algorithm, to analyze possible coiled coil domains in histidine kinase receptors. The potential coiled coils have not yet been structurally characterized in any histidine kinase, and they appear outside previously noted kinase homology regions. The learning algorithm uses a combination of established sequence patterns in known coiled coil proteins and histidine kinase sequence data to learn to recognize efficiently this coiled coil-like motif in the histidine kinases. The common appearance of the structural motif in a functionally important part of the receptors suggests hypotheses for kinase regulation and signal transduction.

Indexed as

AlgorithmsArtificial IntelligenceProtein Structure, TertiaryDatabases, FactualEvaluation Studies as TopicHistidine KinaseLikelihood FunctionsMembrane ProteinsProtein KinasesSignal TransductionHistidine KinaseMembrane ProteinsProtein Kinases

Identifiers

PMID9501159
PMCPMC19638
OpenAlexW2133466306

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.