A model to predict and analyze protein-protein interaction types using electrostatic energies

Authors:
Gokul Vasudev;Luis Rueda
Affiliations:
School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada;School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada
Venue:
BIBM '12 Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year:
2012

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Abstract

Identification and analysis of types of protein-protein interactions (PPI) is an important problem in molecular biology because of their key role in many biological processes in living cells. We propose a model to predict and analyze protein interaction types using electrostatic energies as properties to distinguish between obligate and non-obligate interactions. Our prediction approach uses electrostatic energies for pairs of atoms and amino acids present in interfaces where the interaction occurs. Our results confirm that electrostatic energy is an important property to predict obligate and non obligate protein interaction types achieving accuracy of over 96% on two well known datasets. The classifiers used are support vector machines and linear dimensionality reduction.