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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.


ROC AUC scores of level-I predictor trained on secondary structure for different extraction window sizes.Random Forest was used as the classifier.
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fig-4: ROC AUC scores of level-I predictor trained on secondary structure for different extraction window sizes.Random Forest was used as the classifier.

Mentions: The final decision was selecting the optimal extraction window size. Figure 4 presents level-I ROC AUC score against window size. After analysing the plot we decided to keep size 21 as it provided good performance and it was previously used in other publications. In that way we fixed extraction window size to the same value as the maximal interaction distance, used to define positive residue interactions.


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)

ROC AUC scores of level-I predictor trained on secondary structure for different extraction window sizes.Random Forest was used as the classifier.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-4: ROC AUC scores of level-I predictor trained on secondary structure for different extraction window sizes.Random Forest was used as the classifier.
Mentions: The final decision was selecting the optimal extraction window size. Figure 4 presents level-I ROC AUC score against window size. After analysing the plot we decided to keep size 21 as it provided good performance and it was previously used in other publications. In that way we fixed extraction window size to the same value as the maximal interaction distance, used to define positive residue interactions.

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.