Prediction of Intra-Species Protein-Protein Interactions in Enteropathogens Facilitating Systems Biology Study

Abstract

Protein-protein interactions in Escherichia coli (E. coli) has been studied extensively using high throughput methods such as tandem affinity purification followed by mass spectrometry and yeast two-hybrid method. This can in turn be used to understand the mechanisms of bacterial cellular processes. However, experimental characterization of such huge amount of interactions data is not available for other important enteropathogens. Here, we propose a support vector machine (SVM)-based prediction model using the known PPIs data of E. coli that can be used to predict PPIs in other enteropathogens, such as Vibrio cholerae, Salmonella Typhi, Shigella flexneri and Yersinia entrocolitica. Different features such as domain-domain association (DDA), network topology, and sequence information were used in developing the SVM model. The proposed model using DDA, degree and amino acid composition features has achieved an accuracy of 82% and 62% on 5-fold cross validation and blind E. coli datasets, respectively. The predicted interactions were validated by Gene Ontology (GO) semantic similarity measure and String PPIs database (experimental PPIs only). Finally, we have developed a user-friendly webserver named EnPPIpred to predict intra-species PPIs in enteropathogens, which will be of great help for the experimental biologists. The webserver EnPPIpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/EnPPIpred/.

Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks

“…Uniformly choosing random protein pairs excluding experimental interactions produces an unbiased estimate of the distribution of negatives in the prediction of protein-protein interactions [ 38 ]. Hence, this procedure is a common practice to generate negative datasets containing at most a negligible fraction of interacting proteins [ 39 – 41 ]. FiberDock calculates several binding energy scores, including attractive and repulsive van de Waals forces, the atomic contact energy, partial electrostatics, hydrogen and disulfide bonds, π-stacking, and aliphatic interactions.…”

Assessing and Improving Protein-Protein Interaction Prediction in E. coli

“…[46] also showed weaker results for E. coli compared to other non-bacterial species. More work in improving SVM classification using bacterial PPIs was also conducted in 2015 24 [70], [71]. More recently, state-of-the-art DNNs have been used for PPI predictions and tested on E. coli data presenting high accuracy (>96%) results [72]- [74].…”