  Graphs as a type of data structure have recently attracted significant
attention. Representation learning of geometric graphs has achieved great
success in many fields including molecular, social, and financial networks. It
is natural to present proteins as graphs in which nodes represent the residues
and edges represent the pairwise interactions between residues. However, 3D
protein structures have rarely been studied as graphs directly. The challenges
include: 1) Proteins are complex macromolecules composed of thousands of atoms
making them much harder to model than micro-molecules. 2) Capturing the
long-range pairwise relations for protein structure modeling remains
under-explored. 3) Few studies have focused on learning the different
attributes of proteins together. To address the above challenges, we propose a
new graph neural network architecture to represent the proteins as 3D graphs
and predict both distance geometric graph representation and dihedral geometric
graph representation together. This gives a significant advantage because this
network opens a new path from the sequence to structure. We conducted extensive
experiments on four different datasets and demonstrated the effectiveness of
the proposed method.
