title: Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.
abstract: 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).
authors.affiliation: Centre of New Technologies, University of Warsaw , Warsaw , Poland ; Institute of Computer Science, Polish Academy of Sciences , Warsaw , Poland.
authors.forename: Julian
authors.initials: J
authors.lastname: Zubek
authors.affiliation: Centre of New Technologies, University of Warsaw , Warsaw , Poland ; Institute of Computer Science, Polish Academy of Sciences , Warsaw , Poland.
authors.forename: Marcin
authors.initials: M
authors.lastname: Tatjewski
authors.affiliation: Faculty of Mathematics, Informatics and Mechanics, University of Warsaw , Warsaw , Poland.
authors.forename: Adam
authors.initials: A
authors.lastname: Boniecki
authors.affiliation: Faculty of Mathematics and Computer Science, Jagiellonian University , Cracow , Poland.
authors.forename: Maciej
authors.initials: M
authors.lastname: Mnich
authors.affiliation: Department of Computer Science and Engineering, Jadavpur University , Kolkata, West Bengal , India.
authors.forename: Subhadip
authors.initials: S
authors.lastname: Basu
authors.affiliation: Centre of New Technologies, University of Warsaw , Warsaw , Poland.
authors.forename: Dariusz
authors.initials: D
authors.lastname: Plewczynski
keywords: Interaction patches; Local sequence-structure segments; Machine learning; Multi-scale models; Physico-chemical indices; Protein interaction networks; Protein sequence; Protein-protein interactions; Sequence segments