SVM-SOM: protein-protein interaction prediction by an integrative profile


Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.

We propose a new idea to construct an integrative profile for every residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.

The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.


Software available:

 A simple matlab implement of our predictor is available here: SVM-SOM



 There are 2499 chains in 737 protein complexes in this work. The chains list here: chains in paper.

The description for protein complexes from 3DComplex can be viewed here: hetero-complexes description.


 If you would like to refer to this paper, please cite:

Peng Chen, Jinyan Li, "Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information", BMC Bioinformatics, 2010,11:402.[Full Paper]

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