In silico predictions of protein interactions between Zika virus and human host.
João Luiz de Lemos Padilha PittaCrhisllane Rafaele Dos Santos VasconcelosGabriel da Luz WallauTulio de Lima CamposAntonio Mauro RezendePublished in: PeerJ (2021)
The consensus network of PPI predictions made by Random Forest and SVM algorithms allowed an enrichment analysis that corroborates many aspects of ZIKV infection. The enrichment results are mainly related to viral infection, neuronal development, and immune response, and presented differences among the two compared ZIKV strains. Strain PE243 presented more predicted interactions between proteins from the JAK-STAT signaling pathway, which could lead to a more inflammatory immune response when compared with the FSS13025 strain. These results show that the methodology employed in this study can potentially reveal new interactions between the ZIKV and human cells.
Keyphrases
- zika virus
- immune response
- dengue virus
- signaling pathway
- aedes aegypti
- endothelial cells
- protein protein
- machine learning
- escherichia coli
- climate change
- toll like receptor
- pi k akt
- molecular docking
- oxidative stress
- deep learning
- single cell
- induced pluripotent stem cells
- blood brain barrier
- pluripotent stem cells
- binding protein
- amino acid
- endoplasmic reticulum stress