TY - JOUR UR - https://doi.org/10.7717/peerj.1041 DO - 10.7717/peerj.1041 TI - Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae AU - Zubek,Julian AU - Tatjewski,Marcin AU - Boniecki,Adam AU - Mnich,Maciej AU - Basu,Subhadip AU - Plewczynski,Dariusz A2 - Wilke,Claus DA - 2015/07/02 PY - 2015 KW - Protein-protein interactions KW - Protein interaction networks KW - Multi-scale models KW - Protein sequence KW - Machine learning KW - Physico-chemical indices KW - Interaction patches KW - Sequence segments KW - Local sequence-structure segments AB - Accurate identification of protein–protein interactions (PPI) is the key step in understanding proteins’ biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein–protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein–protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent). VL - 3 SP - e1041 T2 - PeerJ JO - PeerJ J2 - PeerJ SN - 2167-8359 ER -