ArticlePatterns (New York, N.Y.)2022
Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Sequence-based artificial intelligence-guided inhibitory peptides to counteract AAV neutralizing antibodies in gene therapy.Molecular therapy. Advances · 2026Article
- Protein language models enable accurate viral host range prediction.Scientific reports · 2026Article
- Optimizing computational prediction of shrimp-WSSV protein interactions through integration of homology- and topology-based predictions.Molecular genetics and genomics : MGG · 2026Article
- An efficient ranking deep neural network algorithm for the prediction of Ca2+ binding sites of the protein.PloS one · 2026Article
- Seq2Bind webserver for binding site prediction from sequences using fine-tuned protein language models.NAR genomics and bioinformatics · 2025Article
- Host-Microbe Interactions: Prospects of Machine Learning and Deep Learning Technologies in Animal Viral Disease Management.Veterinary sciences · 2025Review
- Review
- PLM-interact: extending protein language models to predict protein-protein interactions.Nature communications · 2025Article
- Recent progress and future challenges in structure-based protein-protein interaction prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- Article
- MSF-CPMP: A novel multi-source feature fusion model for prediction of cyclic peptide membrane permeability.Computational and structural biotechnology journal · 2025Article
- HBFormer: a single-stream framework based on hybrid attention mechanism for identification of human-virus protein-protein interactions.Bioinformatics (Oxford, England) · 2024Article
- Transformer models in biomedicine.BMC medical informatics and decision making · 2024Review
- SENSE-PPI reconstructs interactomes within, across, and between species at the genome scale.iScience · 2024Article
- Exploring Viral-Host Protein Interactions as Antiviral Therapies: A Computational Perspective.Microorganisms · 2024Review
- On the choice of negative examples for prediction of host-pathogen protein interactions.Frontiers in bioinformatics · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.
Indexed as
Identifiers
What OpenQuestion holds
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.