  Protein representation learning is a challenging task that aims to capture
the structure and function of proteins from their amino acid sequences.
Previous methods largely ignored the fact that not all amino acids are equally
important for protein folding and activity. In this article, we propose a
neural clustering framework that can automatically discover the critical
components of a protein by considering both its primary and tertiary structure
information. Our framework treats a protein as a graph, where each node
represents an amino acid and each edge represents a spatial or sequential
connection between amino acids. We then apply an iterative clustering strategy
to group the nodes into clusters based on their 1D and 3D positions and assign
scores to each cluster. We select the highest-scoring clusters and use their
medoid nodes for the next iteration of clustering, until we obtain a
hierarchical and informative representation of the protein. We evaluate on four
protein-related tasks: protein fold classification, enzyme reaction
classification, gene ontology term prediction, and enzyme commission number
prediction. Experimental results demonstrate that our method achieves
state-of-the-art performance.
