In silico predictions of protein interactions between Zika virus and human host

João Luiz de Lemos Padilha Pitta; Crhisllane Rafaele Dos Santos Vasconcelos; Gabriel da Luz Wallau; Túlio de Lima Campos; Antonio Mauro Rezende

doi:10.7717/peerj.11770

In silico predictions of protein interactions between Zika virus and human host

PeerJ. 2021 Aug 24:9:e11770. doi: 10.7717/peerj.11770. eCollection 2021.

Authors

João Luiz de Lemos Padilha Pitta^#¹, Crhisllane Rafaele Dos Santos Vasconcelos^#², Gabriel da Luz Wallau³, Túlio de Lima Campos⁴, Antonio Mauro Rezende¹

Affiliations

¹ Microbiology Department, Aggeu Magalhães Institute-FIOCRUZ/PE, Recife, PE, Brasil.
² Genetics Department, Federal University of Pernambuco, Recife, PE, Brasil.
³ Entomology Department, Aggeu Magalhães Institute-FIOCRUZ/PE, Recife, PE, Brasil.
⁴ Bioinformatics Platform, Aggeu Magalhães Institute-FIOCRUZ/PE, Recife, PE, Brasil.

^# Contributed equally.

Abstract

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.

Keywords: Machine learning; Metabolic pathway; Protein-protein interactions; Zika virus.

Grants and funding

This work was supported by Fundação Oswaldo Cruz (FIOCRUZ). The computational platform used to apply the methodology described in this study was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.