ArticleBMC genomics2018
A generalized approach to predicting protein-protein interactions between virus and host.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Disclosing pathogenic processes of monkeypox (MPOX) and drug repurposing through host-transcriptomics analysis.PLoS neglected tropical diseases · 2026Article
- Article
- In-silico discovery of type-2 diabetes-causing host key genes that are associated with the complexity of monkeypox and repurposing common drugs.Briefings in bioinformatics · 2025Article
- A graph neural network-based approach for predicting SARS-CoV-2-human protein interactions from multiview data.PloS one · 2025Article
- Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.Cancer informatics · 2025Review
- Article
- Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes.Genomics, proteomics & bioinformatics · 2024Review
- Computational analysis of pathogen-host interactome for fast and low-risk in-silico drug repurposing in emerging viral threats like Mpox.Scientific reports · 2024Article
- Exploring Viral-Host Protein Interactions as Antiviral Therapies: A Computational Perspective.Microorganisms · 2024Review
- Kernel Bayesian nonlinear matrix factorization based on variational inference for human-virus protein-protein interaction prediction.Scientific reports · 2024Article
- Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions.bioRxiv : the preprint server for biology · 2024Article
- Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality.Biomolecules · 2024Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction.PloS one · 2023Article
- Machine Learning Methods for Virus-Host Protein-Protein Interaction Prediction.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Recent developments of sequence-based prediction of protein-protein interactions.Biophysical reviews · 2022Review
- Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings.Patterns (New York, N.Y.) · 2022Article
- Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention-based neural networks.Computational and structural biotechnology journal · 2022Article
- MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.Frontiers in medicine · 2022Article
- On the choice of negative examples for prediction of host-pathogen protein interactions.Frontiers in bioinformatics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundViral infection involves a large number of protein-protein interactions (PPIs) between virus and its host. These interactions range from the initial binding of viral coat proteins to host membrane receptor to the hijacking the host transcription machinery by viral proteins. Therefore, identifying PPIs between virus and its host helps understand the mechanism of viral infections and design antiviral drugs. Many computational methods have been developed to predict PPIs, but most of them are intended for PPIs within a species rather than PPIs across different species such as PPIs between virus and host.
resultsIn this study, we developed a prediction model of virus-host PPIs, which is applicable to new viruses and hosts. We tested the prediction model on independent datasets of virus-host PPIs, which were not used in training the model. Despite a low sequence similarity between proteins in training datasets and target proteins in test datasets, the prediction model showed a high performance comparable to the best performance of other methods for single virus-host PPIs.
conclusionsOur method will be particularly useful to find PPIs between host and new viruses for which little information is available. The program and support data are available at http://bclab.inha.ac.kr/VirusHostPPI .
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