Evidence map›Paper›PMID 18442400›Full record

ArticleBMC bioinformatics2008

An efficient visualization tool for the analysis of protein mutation matrices.

Maria Pamela C David, Carlo M Lapid, Vincent Ricardo M Daria

Abstract read
In one paragraph

Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Maria Pamela C DavidComputational Science Research Center, University of the Philippines, Diliman 1101, Philippines. maria_pamela.david@up.edu.ph
Carlo M Lapid
Vincent Ricardo M Daria

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is useful to develop a tool that would effectively describe protein mutation matrices specifically geared towards the identification of mutations that produce either wanted or unwanted effects, such as an increase or decrease in affinity, or a predisposition towards misfolding. Here, we describe a tool where such mutations are efficiently identified, categorized and visualized. To categorize the mutations, amino acids in a mutation matrix are arranged according to one of three sets of physicochemical characteristics, namely hydrophilicity, size and polarizability, and charge and polarity. The magnitude and frequencies of mutations for an alignment are subsequently described using color information and scaling factors.

resultsTo illustrate the capabilities of our approach, the technique is used to visualize and to compare mutation patterns in evolving sequences with diametrically opposite characteristics. Results show the emergence of distinct patterns not immediately discernible from the raw matrices.

conclusionOur technique enables effective categorization and visualization of mutations by using specifically-arranged mutation matrices. This tool has a number of possible applications in protein engineering, notably in simplifying the identification of mutations and/or mutation trends that are associated with specific engineered protein characteristics and behavior.

Indexed as

Audiovisual AidsMutationUser-Computer InterfaceAmino AcidsAmyloidAnimalsAntibodiesComputer GraphicsDatabases, ProteinElectric CapacitanceHemagglutininsHumansHydrophobic and Hydrophilic InteractionsIodide PeroxidaseMutagenesisProtein FoldingAmino AcidsAmyloidAntibodiesHemagglutininsIodide PeroxidaseProteinsReceptors, Odorant

Identifiers

PMID18442400
PMCPMC2390542

What OpenQuestion holds

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LicenceCC BY
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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.