  Understanding protein structure-function relationships is a key challenge in
computational biology, with applications across the biotechnology and
pharmaceutical industries. While it is known that protein structure directly
impacts protein function, many functional prediction tasks use only protein
sequence. In this work, we isolate protein structure to make functional
annotations for proteins in the Protein Data Bank in order to study the
expressiveness of different structure-based prediction schemes. We present
PersGNN - an end-to-end trainable deep learning model that combines graph
representation learning with topological data analysis to capture a complex set
of both local and global structural features. While variations of these
techniques have been successfully applied to proteins before, we demonstrate
that our hybridized approach, PersGNN, outperforms either method on its own as
well as a baseline neural network that learns from the same information.
PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR)
compared to the best individual model, as well as high F1 scores across
different gene ontology categories, indicating the transferability of this
approach.
