Prediction and analysis of human-herpes simplex virus type 1 protein-protein interactions by integrating multiple methods

• Published in: Quantitative biology 2020-12, Vol.8 (4), p.312-324
• Main Authors: Lian, Xianyi, Yang, Xiaodi, Shao, Jiqi, Hou, Fujun, Yang, Shiping, Pan, Dongli, Zhang, Ziding
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Encephalitis; Herpes simplex; herpes simplex virus type 1; Herpes viruses; human-virus interaction; Learning algorithms; Life Sciences; Machine learning; Mathematical and Computational Biology; Neurodegenerative diseases; Peptide mapping; prediction; Protein interaction; protein-protein interaction; Proteins; Proteomes; Research Article
• ISSN: 2095-4689; 2095-4697
• DOI: 10.1007/s40484-020-0222-5

Background: Herpes simplex virus type 1 (HSV-1) is a ubiquitous infectious pathogen that widely affects human health. To decipher the complicated human-HSV-1 interactions, a comprehensive protein-protein interaction (PPI) network between human and HSV-1 is highly demanded. Methods: To complement the experimental identification of human-HSV-1 PPIs, an integrative strategy to predict proteome-wide PPIs between human and HSV-1 was developed. For each human-HSV-1 protein pair, four popular PPI inference methods, including interolog mapping, the domain-domain interaction-based method, the domain-motif interaction-based method, and the machine learning-based method, were optimally implemented to generate four interaction probability scores, which were further integrated into a final probability score. Results: As a result, a comprehensive high-confidence PPI network between human and HSV-1 was established, covering 10,432 interactions between 4,546 human proteins and 72 HSV-1 proteins. Functional and network analyses of the HSV-1 targeting proteins in the context of human interactome can recapitulate the known knowledge regarding the HSV-1 replication cycle, supporting the overall reliability of the predicted PPI network. Considering that HSV-1 infections are implicated in encephalitis and neurodegenerative diseases, we focused on exploring the biological significance of the brain-specific human-HSV-1 PPIs. In particular, the predicted interactions between HSV-1 proteins and Alzheimer’s-disease-related proteins were intensively investigated. Conclusion: The current work can provide testable hypotheses to assist in the mechanistic understanding of the human-HSV-1 relationship and the anti-HSV-1 pharmaceutical target discovery. To make the predicted PPI network and the datasets freely accessible to the scientific community, a user-friendly database browser was released at http://www.zzdlab.com/HintHSV/index.php.