Fig. 2.

Overview of our Protein Interface Network (PInet) approach for predicting interaction regions on pairs of proteins. PInet consumes two 5 dimensional point clouds representing geometry and physicochemical properties of each protein surface, and performs a semantic segmentation on all points from both point clouds simultaneously. It first processes each point cloud separately. For each protein, a Spatial Transformation Network renders the surface point clouds invariant to rigid-body transformations. Then a multi-layer perceptron (MLP) extracts local surface features. These local surface features are then aggregated into a global protein feature vector. With each protein thus processed, the protein local surface features and global protein features from both proteins are concatenated in order to be segmented by another MLP. The trainable weights for canonical transformation, local and global feature extraction are shared for the two proteins