  Machine learning (ML) is revolutionizing protein structural analysis,
including an important subproblem of predicting protein residue contact maps,
i.e., which amino-acid residues are in close spatial proximity given the
amino-acid sequence of a protein. Despite recent progresses in ML-based protein
contact prediction, predicting contacts with a wide range of distances
(commonly classified into short-, medium- and long-range contacts) remains a
challenge. Here, we propose a multiscale graph neural network (GNN) based
approach taking a cue from multiscale physics simulations, in which a standard
pipeline involving a recurrent neural network (RNN) is augmented with three
GNNs to refine predictive capability for short-, medium- and long-range residue
contacts, respectively. Test results on the ProteinNet dataset show improved
accuracy for contacts of all ranges using the proposed multiscale RNN+GNN
approach over the conventional approach, including the most challenging case of
long-range contact prediction.
