ArticlePLoS computational biology2012
Artificial neural networks trained to detect viral and phage structural proteins.
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.
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Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it, 106 citations in OpenAlex.
- Precision phage therapy in the AI/ML era: a systematic review of discovery-to-clinical translation evidence.Frontiers in microbiology · 2026Pooled it
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- StackPVP: a stacked ensemble classification framework for predicting phage virion proteins using integrated evolutionary features.Frontiers in microbiology · 2026Article
- Phage Encapsulation and Delivery Technology: A Strategy for Treating Drug-Resistant Pathogenic Microorganisms.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Empathi: embedding-based phage protein annotation tool by hierarchical assignment.Nature communications · 2025Article
- Fold first, ask later: structure-informed function annotation ofbioRxiv : the preprint server for biology · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- Elucidation of molecular function of phage protein responsible for optimization of host cell lysis.BMC microbiology · 2024Article
- Deep learning in structural bioinformatics: current applications and future perspectives.Briefings in bioinformatics · 2024Review
- Recent Applications of Artificial Intelligence in Discovery of New Antibacterial Agents.Advances and applications in bioinformatics and chemistry : AABC · 2024Review
- PhageScanner: a reconfigurable machine learning framework for bacteriophage genomic and metagenomic feature annotation.Frontiers in microbiology · 2024Article
- CompoundingViruses · 2023Article
- PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer.Bioinformatics (Oxford, England) · 2023Article
- PhageTailFinder: A tool for phage tail module detection and annotation.Frontiers in genetics · 2023Article
- Viruses direct carbon cycling in lake sediments under global change.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- DeePVP: Identification and classification of phage virion proteins using deep learning.GigaScience · 2022Article
- SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins.Scientific reports · 2022Article
- Article
- Bacteriophage Genetic Edition Using LSTM.Frontiers in bioinformatics · 2022Article
- Phage_UniR_LGBM: Phage Virion Proteins Classification with UniRep Features and LightGBM Model.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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.