Prediction of human-Streptococcus pneumoniae protein-protein interactions using logistic regression. (June 2021)

• Record Type: Journal Article
• Authors: Prasasty, Vivitri Dewi; Hutagalung, Rory Anthony; Gunadi, Reinhart; Sofia, Dewi Yustika; Rosmalena, Rosmalena; Yazid, Fatmawaty; Sinaga, Ernawati
• Is Part Of: Computational biology and chemistry. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Host-pathogen protein-protein interactions -- Logistic regression -- Network centrality -- Pathway enrichment -- Streptococcus pneumoniae; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107492
• Languages: English
• ISSNs: 1476-9271

Abstract

Highlights: Domain-based prediction with the logistic regression generated more than 5000 potential HP-PPIs between S. pneumoniae and human. HP-PPIs between S. pneumoniae and human were involved in the immune system, particularly complement and phagocytosis pathways. Centrality analysis on S. pneumoniae proteins in the predicted HP-PPIs revealed β-galactosidase as a target for new drug or vaccine development. Abstract: Streptococcus pneumoniae is a major cause of mortality in children under five years old. In recent years, the emergence of antibiotic-resistant strains of S. pneumoniae increases the threat level of this pathogen. For that reason, the exploration of S. pneumoniae protein virulence factors should be considered in developing new drugs or vaccines, for instance by the analysis of host-pathogen protein-protein interactions (HP-PPIs). In this research, prediction of protein-protein interactions was performed with a logistic regression model with the number of protein domain occurrences as features. By utilizing HP-PPIs of three different pathogens as training data, the model achieved 57–77 % precision, 64–75 % recall, and 96–98 % specificity. Prediction of human- S. pneumoniae protein-protein interactions using the model yielded 5823 interactions involving thirty S. pneumoniae proteins and 324 human proteins. Pathway enrichment analysis showed that most of the pathways involved in the predicted interactions are immune system pathways. Network topology analysis revealed β-galactosidase (BgaA) as the most central among the S. pneumoniae proteins in the predicted HP-PPI networks, with a degree centrality of 1.0 and a betweenness centrality of 0.451853. Further experimental studies are required to validate the predicted interactions and examine their roles in S. pneumoniae infection.