Graph Neural Networks for the Prediction of Protein–Protein Interfaces

Binding site identification allows to determine the functionality and the quaternary structure of protein–protein complexes. Various approaches to this problem have been proposed without reaching a viable solution. Representing the interacting peptides as graphs, a correspondence graph describing their interaction can be built. Finding the maximum clique in the correspondence graph allows to identify the secondary structure elements belonging to the interaction site. Although the maximum clique problem is NP-complete, Graph Neural Networks make for an approximation tool that can solve the problem in affordable time. Our experimental results are promising and suggest that this direction should be explored further.