Evidence map›Paper›PMID 14739328›Full record

ArticleProtein science : a publication of the Protein Society2004

Automated selection of positions determining functional specificity of proteins by comparative analysis of orthologous groups in protein families.

Olga V Kalinina, Andrey A Mironov, Mikhail S Gelfand, Aleksandra B Rakhmaninova

Open access · bronzeAbstract readComparative Study
In one paragraph

Article in Protein science : a publication of the Protein Society, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

62 citing papers in PubMed, 115 citations in OpenAlex.

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2 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

4 authors at 2 institutions in 1 country.

Olga V KalininaState Scientific Center GosNIIGenetika, 1st Dorozhny pr., 1, Moscow 113545, Russia.
Andrey A Mironov
Mikhail S Gelfand
Aleksandra B Rakhmaninova
Academician Pilyugin Center · RUGenetika · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing volume of genomic data opens new possibilities for analysis of protein function. We introduce a method for automated selection of residues that determine the functional specificity of proteins with a common general function (the specificity-determining positions [SDP] prediction method). Such residues are assumed to be conserved within groups of orthologs (that may be assumed to have the same specificity) and to vary between paralogs. Thus, considering a multiple sequence alignment of a protein family divided into orthologous groups, one can select positions where the distribution of amino acids correlates with this division. Unlike previously published techniques, the introduced method directly takes into account nonuniformity of amino acid substitution frequencies. In addition, it does not require setting arbitrary thresholds. Instead, a formal procedure for threshold selection using the Bernoulli estimator is implemented. We tested the SDP prediction method on the LacI family of bacterial transcription factors and a sample of bacterial water and glycerol transporters belonging to the major intrinsic protein (MIP) family. In both cases, the comparison with available experimental and structural data strongly supported our predictions.

Indexed as

AutomationCatalytic DomainComputational BiologyComputer SimulationEntropyGlycerolModels, MolecularPhylogenyProteinsSubstrate SpecificityGlycerolProteins

Identifiers

PMID14739328
PMCPMC2286703
OpenAlexW2023385944

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.