Publikasi Scopus 926 artikel (Per 14 Maret 2022)

Prasasty V.D., Hutagalung R.A., Gunadi R., Sofia D.Y., Rosmalena R., Yazid F., Sinaga E.
56019989700;57196436040;57223239895;57223238377;56891769500;57207890516;6503946360;
Prediction of human-Streptococcus pneumoniae protein-protein interactions using logistic regression
2021
Computational Biology and Chemistry
92
107492
Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta, 12930, Indonesia; Department of Biology, Faculty of Life Sciences, Universitas Surya, Tangerang, Banten 15143, Indonesia; Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia; Faculty of Biology, Universitas Nasional, Jakarta, 12520, Indonesia
Prasasty, V.D., Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta, 12930, Indonesia; Hutagalung, R.A., Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta, 12930, Indonesia; Gunadi, R., Department of Biology, Faculty of Life Sciences, Universitas Surya, Tangerang, Banten 15143, Indonesia; Sofia, D.Y., Department of Biology, Faculty of Life Sciences, Universitas Surya, Tangerang, Banten 15143, Indonesia; Rosmalena, R., Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia; Yazid, F., Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia; Sinaga, E., Faculty of Biology, Universitas Nasional, Jakarta, 12520, Indonesia
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. © 2021 Elsevier Ltd
Host-pathogen protein-protein interactions; Logistic regression; Network centrality; Pathway enrichment; Streptococcus pneumoniae
Forecasting; Logistic regression; Betweenness centrality; Degree centrality; Logistic Regression modeling; Network topology analysis; Protein-protein interactions; Resistant strains; Streptococcus pneumoniae; Virulence factors; Proteins; protein; protein binding; chemistry; host pathogen interaction; human; statistical model; Streptococcus pneumoniae; Host-Pathogen Interactions; Humans; Logistic Models; Protein Binding; Proteins; Streptococcus pneumoniae
Elsevier Ltd
14769271
33964803
Article
Q3
416
11737