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Biological Protein-protein Interaction Prediction using Binding Free Energies and Linear Dimensionality Reduction

Biological Protein-protein Interaction Prediction using Binding Free Energies and Linear Dimensionality Reduction

This video was recorded at 5th IAPR International Conference on Pattern Recognition in Bioinformatics, Nijmegen 2010. An important issue in understanding and classifying protein-protein interactions (PPI) is to characterize their interfaces in order to discriminate between transient and obligate complexes. We propose a classification approach to discriminate between these two types of complexes. Our approach uses contact and binding free energies of the residues present in the interaction, which are the input features for the classifiers. A total of 282 features are extracted for each complex, and the classification is performed via recently proposed dimensionality reduction (LDR) methods, including the well-know Fisher's discriminant analysis and two heteroscedastic approaches. The results on a standard benchmark of transient and obligate protein complexes show that LDR approaches achieve a very high classification accuracy (over 78%), outperforming various support vector machines and nearest-neighbor classifiers. An additional insight on the proposed approach and experiments on different subsets of features shows that solvation energies can be used in the classification, leading to a performance comparable to using the full binding free energies of the interaction.

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