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Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.

Zubek J, Tatjewski M, Boniecki A, Mnich M, Basu S, Plewczynski D - PeerJ (2015)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre of New Technologies, University of Warsaw , Warsaw , Poland ; Institute of Computer Science, Polish Academy of Sciences , Warsaw , Poland.

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).

No MeSH data available.


Schematic depiction of our two-stage ensemble method.
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fig-1: Schematic depiction of our two-stage ensemble method.

Mentions: In this paper, we focus on protein–protein interactions. We develop ensemble learning method for identification of proteins binary interactions by predicting residue-residue interactions. Moreover, we demonstrate how to integrate sequence information from the lower scales into the higher scale machine learning predictor. In our approach, binary interaction between two proteins is predicted by considering all possible interactions between their sequence segments using level-I predictor. An output of this phase is the matrix of scores with the size corresponding to the proteins’ lengths. Given the threshold on the likelihoods, this represent the whole network of all possible residue contacts that could be made during the complex formation. Later, we transform the scores matrix into a fixed length input vector suitable for further statistical data analysis (aggregated values over columns, diagonals, etc.), and we identify the network properties (e.g., sizes of connected components) using interaction graph. This data is used by the level-II predictor, which integrates information similarly to a human expert. Figure 1 presents this pipeline graphically.


Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.

Zubek J, Tatjewski M, Boniecki A, Mnich M, Basu S, Plewczynski D - PeerJ (2015)

Schematic depiction of our two-stage ensemble method.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493684&req=5

fig-1: Schematic depiction of our two-stage ensemble method.
Mentions: In this paper, we focus on protein–protein interactions. We develop ensemble learning method for identification of proteins binary interactions by predicting residue-residue interactions. Moreover, we demonstrate how to integrate sequence information from the lower scales into the higher scale machine learning predictor. In our approach, binary interaction between two proteins is predicted by considering all possible interactions between their sequence segments using level-I predictor. An output of this phase is the matrix of scores with the size corresponding to the proteins’ lengths. Given the threshold on the likelihoods, this represent the whole network of all possible residue contacts that could be made during the complex formation. Later, we transform the scores matrix into a fixed length input vector suitable for further statistical data analysis (aggregated values over columns, diagonals, etc.), and we identify the network properties (e.g., sizes of connected components) using interaction graph. This data is used by the level-II predictor, which integrates information similarly to a human expert. Figure 1 presents this pipeline graphically.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre of New Technologies, University of Warsaw , Warsaw , Poland ; Institute of Computer Science, Polish Academy of Sciences , Warsaw , Poland.

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).

No MeSH data available.