Evidence map›Paper›PMID 22927809›Full record

ArticlePLoS computational biology2012

Artificial neural networks trained to detect viral and phage structural proteins.

Victor Seguritan, Nelson Alves, Michael Arnoult, Amy Raymond, Don Lorimer, Alex B Burgin, Peter Salamon, Anca M Segall

Open access · goldAbstract readValidation Study
In one paragraph

Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 2 pooled it
4.4field-weighted citation impact, top 6% 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

57 citing papers in PubMed, 2 syntheses or guidelines pooled it, 106 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Fold first, ask later: structure-informed function annotation ofbioRxiv : the preprint server for biology · 2025
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  7. A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025
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  9. Review
  10. Recent Applications of Artificial Intelligence in Discovery of New Antibacterial Agents.Advances and applications in bioinformatics and chemistry : AABC · 2024
    Review
  11. Article
  12. CompoundingViruses · 2023
    Article
  13. Article
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  15. Viruses direct carbon cycling in lake sediments under global change.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  16. Article
  17. Article
  18. Article
  19. Bacteriophage Genetic Edition Using LSTM.Frontiers in bioinformatics · 2022
    Article
  20. 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

8 authors at 2 institutions in 2 countries.

Victor SeguritanProgram of Computational Science, San Diego State University, San Diego, California, United States of America.
Nelson Alves
Michael Arnoult
Amy Raymond
Don Lorimer
Alex B Burgin
Peter Salamon
Anca M Segall
San Diego State University · USUniversidade Federal do Rio de Janeiro · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phages play critical roles in the survival and pathogenicity of their hosts, via lysogenic conversion factors, and in nutrient redistribution, via cell lysis. Analyses of phage- and viral-encoded genes in environmental samples provide insights into the physiological impact of viruses on microbial communities and human health. However, phage ORFs are extremely diverse of which over 70% of them are dissimilar to any genes with annotated functions in GenBank. Better identification of viruses would also aid in better detection and diagnosis of disease, in vaccine development, and generally in better understanding the physiological potential of any environment. In contrast to enzymes, viral structural protein function can be much more challenging to detect from sequence data because of low sequence conservation, few known conserved catalytic sites or sequence domains, and relatively limited experimental data. We have designed a method of predicting phage structural protein sequences that uses Artificial Neural Networks (ANNs). First, we trained ANNs to classify viral structural proteins using amino acid frequency; these correctly classify a large fraction of test cases with a high degree of specificity and sensitivity. Subsequently, we added estimates of protein isoelectric points as a feature to ANNs that classify specialized families of proteins, namely major capsid and tail proteins. As expected, these more specialized ANNs are more accurate than the structural ANNs. To experimentally validate the ANN predictions, several ORFs with no significant similarities to known sequences that are ANN-predicted structural proteins were examined by transmission electron microscopy. Some of these self-assembled into structures strongly resembling virion structures. Thus, our ANNs are new tools for identifying phage and potential prophage structural proteins that are difficult or impossible to detect by other bioinformatic analysis. The networks will be valuable when sequence is available but in vitro propagation of the phage may not be practical or possible.

Indexed as

Neural Networks, ComputerBacteriophagesGenes, ViralOpen Reading FramesViral ProteinsViral Proteins

Identifiers

PMID22927809
PMCPMC3426561
OpenAlexW1980571326

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.