Evidence map›Paper›PMID 11274469›Full record

ArticleProtein science : a publication of the Protein Society2001

Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor.

I Jacoboni, P L Martelli, P Fariselli, V De Pinto, R Casadio

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2001. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Article
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  3. Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021
    Review
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  12. Effects of climate change on Salmonella infections.Foodborne pathogens and disease · 2014
    Article
  13. TMBB-DB: a transmembrane β-barrel proteome database.Bioinformatics (Oxford, England) · 2012
    Article
  14. Article
  15. Article
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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.

I JacoboniLaboratory of Biocomputing, Centro Interdipartimentale per le Ricerche Biotecnologiche (CIRB), Bologna, Italy.
P L Martelli
P Fariselli
V De Pinto
R Casadio

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A method based on neural networks is trained and tested on a nonredundant set of beta-barrel membrane proteins known at atomic resolution with a jackknife procedure. The method predicts the topography of transmembrane beta strands with residue accuracy as high as 78% when evolutionary information is used as input to the network. Of the transmembrane beta-strands included in the training set, 93% are correctly assigned. The predictor includes an algorithm of model optimization, based on dynamic programming, that correctly models eight out of the 11 proteins present in the training/testing set. In addition, protein topology is assigned on the basis of the location of the longest loops in the models. We propose this as a general method to fill the gap of the prediction of beta-barrel membrane proteins.

Indexed as

Neural Networks, ComputerAlgorithmsBacterial Outer Membrane ProteinsDatabases, FactualEscherichia coliForecastingModels, BiologicalPorinsProtein Structure, SecondaryRhodopseudomonasBacterial Outer Membrane ProteinsPorins

Identifiers

PMID11274469
PMCPMC2373968

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.