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Updated: Oct 20, 2025
Analysis of Group IV Viral SSHHPS Using In Vitro and In Silico Methods
Published on: December 21, 2019
In silico predictions of protein interactions between Zika virus and human host
João Luiz de Lemos Padilha Pitta1, Crhisllane Rafaele Dos Santos Vasconcelos2, Gabriel da Luz Wallau3
1Microbiology Department, Aggeu Magalhães Institute-FIOCRUZ/PE, Recife, PE, Brasil.
Background:
The ZIKA virus (ZIKV) belongs to the Flaviviridae family, was first isolated in the 1940s, and remained underreported until its global threat in 2016, where drastic consequences were reported as Guillan-Barre syndrome and microcephaly in newborns. Understanding molecular interactions of ZIKV proteins during the host infection is important to develop treatments and prophylactic measures; however, large-scale experimental approaches normally used to detect protein-protein interaction (PPI) are onerous and labor-intensive. On the other hand, computational methods may overcome these challenges and guide traditional approaches on one or few protein molecules. The prediction of PPIs can be used to study host-parasite interactions at the protein level and reveal key pathways that allow viral infection.
Results:
Applying Random Forest and Support Vector Machine (SVM) algorithms, we performed predictions of PPI between two ZIKV strains and human proteomes. The consensus number of predictions of both algorithms was 17,223 pairs of proteins. Functional enrichment analyses were executed with the predicted networks to access the biological meanings of the protein interactions. Some pathways related to viral infection and neurological development were found for both ZIKV strains in the enrichment analysis, but the JAK-STAT pathway was observed only for strain PE243 when compared with the FSS13025 strain.
Conclusions:
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.
Insights
Computational methods predicted 17,223 Zika virus (ZIKV) protein-protein interactions (PPIs) with human cells. These ZIKV-host interactions are crucial for understanding viral infection, neurological development, and immune responses, guiding future research.
Area of Science:
- Virology
- Computational Biology
- Immunology
Background:
- The Zika virus (ZIKV), a Flaviviridae family member, poses a global health threat, causing severe outcomes like microcephaly and Guillain-Barre syndrome.
- Understanding ZIKV protein-protein interactions (PPIs) is vital for developing treatments, but experimental methods are labor-intensive.
- Computational approaches offer an efficient alternative to predict ZIKV-host PPIs and identify key infection pathways.
Purpose of the Study:
- To computationally predict protein-protein interactions (PPIs) between two ZIKV strains and human proteomes.
- To analyze the biological significance of predicted ZIKV-host interactions using functional enrichment analysis.
- To compare interaction networks between ZIKV strains and identify strain-specific pathways.
Main Methods:
- Random Forest and Support Vector Machine (SVM) algorithms were employed for PPI prediction.
- A consensus approach combined predictions from both algorithms.
- Functional enrichment analysis was performed on the predicted PPI networks.
Main Results:
- A total of 17,223 PPIs were predicted between ZIKV strains and human proteins.
- Enrichment analysis revealed pathways related to viral infection and neurological development for both strains.
- The JAK-STAT signaling pathway was uniquely identified for the ZIKV PE243 strain, suggesting a potentially stronger inflammatory response compared to strain FSS13025.
Conclusions:
- The computational prediction of ZIKV-host PPIs provides valuable insights into viral infection mechanisms.
- Enrichment analysis highlights the roles of ZIKV interactions in neuronal development and immune response.
- Differences in predicted interactions, particularly involving the JAK-STAT pathway in strain PE243, suggest strain-specific host responses and potential therapeutic targets.
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