title: Computational probing protein-protein interactions targeting small molecules.
abstract: BACKGROUND
  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 types 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 drug targets.
  
  
  RESULTS
  Here, we propose a machine learning method to predict PPI targets in a genomic-wide scale. Specifically, we develop a computational method, named as PrePPItar, to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs. First, we survey the databases 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 the association rules from the data. Second, 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 sequence. 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 are predictive for PPI target. Moreover, we can increase the PPI target 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.
  
  
  CONCLUSIONS
  In conclusion, PrePPItar can serve as a useful tool for PPI target discovery and provides a general heterogeneous data integrative framework.
  
  
  METHODS
  PrePPItar is available at http://doc.aporc.org/wiki/PrePPItar.
  
  
  BACKGROUND
  ycwang@nwipb.cas.cn or ywang@amss.ac.cn
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China.
authors.forename: Yong-Cui
authors.initials: YC
authors.lastname: Wang
authors.affiliation: Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China.
authors.forename: Shi-Long
authors.initials: SL
authors.lastname: Chen
authors.affiliation: College of Science, China Agricultural University, Beijing 100083, China and.
authors.forename: Nai-Yang
authors.initials: NY
authors.lastname: Deng
authors.affiliation: National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D004364:Pharmaceutical Preparations / Q000737:chemistry; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry / Q000187:drug effects; D020539:Sequence Analysis, Protein; D060388:Support Vector Machine
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins