PrePPItar: Machine learning framework to Predict PPI target for drug
- Version 0.0.1
- Last updated: Feb. 10, 2015
- Yong-Cui Wang, Shi-Long Chen, Nai-Yang Deng, and Yong Wang. Computational probing protein-protein interaction targeting small molecules. In submission.
With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different kinds of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel PPI inhibitors.
Here, we propose a machine learning method to predict novel PPI inhibitors in a genomic-wide scale. Specifically, we develop a computational method, named as PrePPItar, to Predict PPIs as particular drug targets by uncovering the potential associations between drugs and PPIs. Firstly, we survey the database and manually construct a gold-standard positive dataset for drug and PPI interactions. This effort leads to a dataset with 227 associations among 63 PPIs and 113 FDA-approved drugs and allows us to build models to learn from the data. Secondly, we characterize drugs by profiling in chemical structure, drug ATC-code annotation, and side effect space and represent PPI similarity by a symmetrical S-kernel based on protein amino acid sequences. Then the drugs and PPIs are correlated by Kronecker product kernel. Finally, a support vector machine (SVM), is trained to predict novel associations between drugs and PPIs. We validate our PrePPItar method on the well established gold-standard dataset by cross-validation. We find that all chemical structure, drug ATC-code, and side-effect information sources are predictive for PPI target. Moreover, we can increase the PPI targets prediction coverage by integrating multiple data sources. Follow-up database search and pathway analysis indicate that our new predictions are worthy of future experimental validation. Conclusion: In conclusion, PrePPItar can serve as a useful tool for PPI target discovery and provides a general heterogeneous data integrative framework.
This version of the program is in very preliminary stage and provided just for testing purpose. The program is still under development.
Category: Supplementary Software