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/.
Highlights
Enteropathogenic bacteria cause deadly diseases such as diarrhea, thyphoid and cholera resulting in high mortality rate, mostly among the children below 5 years [1,2]
While the PPIs in some bacteria such as Escherichia coli (E. coli) has been well studied through different experimental approaches such as tandem affinity purification followed by mass spectrometry (AP-MS) and yeast two-hybrid (Y2H), it remains unavailable in other pathogens such as Vibrio cholera (V. cholera), Salmonella Typhi
We selected a combination of features such as domain-domain association (DDA), degree and amino acid composition (AAC) features for predicting unknown PPIs of E. coli, which yields a sensitivity of 77%, specificity of 86% and accuracy of 82% on known E. coli dataset at a threshold value of 0.00
Summary
Enteropathogenic bacteria cause deadly diseases such as diarrhea, thyphoid and cholera resulting in high mortality rate, mostly among the children below 5 years [1,2]. Guo et al predicted PPIs of yeast with an accuracy of 88.09% using SVM and auto covariance features of protein sequence [10]. Shen et al employed SVM with a kernel functions and conjoint triad feature of protein sequences to predict PPIs in human [11]. Their best model achieved an average accuracy of 83.9%. Guo et al predicted PPIs in human, yeast, Drosophila, E. coli, and Caenorhabditis elegans using SVM [13] Their methods yield an average accuracy of 90.67%, 88.99%, 90.09%, 92.73%, and 97.51% respectively
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