Using desolvation energies of structural domains to predict stability of protein complexes

Abstract

Employing domain knowledge for prediction of particular types of protein–protein interactions (PPIs) is a problem that has become increasingly important in the past few years, due to the fundamental role of domains in protein function. We propose a model to predict obligate and non-obligate protein interaction types using desolvation energies of structural domains that are present in the interfaces of protein complexes, which are extracted from the CATH database. The prediction is performed using several state-of-the-art classification techniques, including linear dimensionality reduction, a support vector machine based on sequential minimal optimization, naive Bayes, and k-nearest neighbour. Our results on two well-known datasets demonstrate that (a) domain-based features of higher levels of CATH, especially level 2, are more powerful and discriminative than features of other levels, and (b) properties taken from different levels of the CATH hierarchy yield higher accuracies than properties taken from each level of the hierarchy separately. Furthermore, analysis of structural properties suggests that domain–domain interactions that have at least a mainly-beta secondary structure in one sub-unit are more informative for predicting obligate and non-obligate PPIs.