Evidence map›Paper›PMID 24403851›Full record

ArticleCurrent genomics2013

Genome-wide Membrane Protein Structure Prediction.

Stefano Piccoli, Eda Suku, Marianna Garonzi, Alejandro Giorgetti

Abstract read
In one paragraph

Article in Current genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

4 authors.

Stefano PiccoliApplied Bioinformatics Group, Dept. of Biotechnology, University of Verona, strada Le grazie 15, 37134, Verona, Italy;
Eda SukuApplied Bioinformatics Group, Dept. of Biotechnology, University of Verona, strada Le grazie 15, 37134, Verona, Italy;
Marianna GaronziApplied Bioinformatics Group, Dept. of Biotechnology, University of Verona, strada Le grazie 15, 37134, Verona, Italy;
Alejandro GiorgettiApplied Bioinformatics Group, Dept. of Biotechnology, University of Verona, strada Le grazie 15, 37134, Verona, Italy; ; German Research School for Simulation Sciences, Juelich, Germany; ; Center for Biomedical Computing (CBMC), University of Verona, strada Le grazie 8, 37134, Verona, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transmembrane proteins allow cells to extensively communicate with the external world in a very accurate and specific way. They form principal nodes in several signaling pathways and attract large interest in therapeutic intervention, as the majority pharmaceutical compounds target membrane proteins. Thus, according to the current genome annotation methods, a detailed structural/functional characterization at the protein level of each of the elements codified in the genome is also required. The extreme difficulty in obtaining high-resolution three-dimensional structures, calls for computational approaches. Here we review to which extent the efforts made in the last few years, combining the structural characterization of membrane proteins with protein bioinformatics techniques, could help describing membrane proteins at a genome-wide scale. In particular we analyze the use of comparative modeling techniques as a way of overcoming the lack of high-resolution three-dimensional structures in the human membrane proteome.

Indexed as

Genome-wide scale analysisHomology modelingHuman membrane proteomeMembrane protein.Multitasking approachProtein structural bioinformatics

Identifiers

PMID24403851
PMCPMC3763683

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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.