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Understanding protein structure-function relationships is a key challenge incomputational biology, with applications across the biotechnology andpharmaceutical industries. While it is known that protein structure directlyimpacts protein function, many functional prediction tasks use only proteinsequence. In this work, we isolate protein structure to make functionalannotations for proteins in the Protein Data Bank in order to study theexpressiveness of different structure-based prediction schemes. We presentPersGNN - an end-to-end trainable deep learning model that combines graphrepresentation learning with topological data analysis to capture a complex setof both local and global structural features. While variations of thesetechniques have been successfully applied to proteins before, we demonstratethat our hybridized approach, PersGNN, outperforms either method on its own aswell 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 acrossdifferent gene ontology categories, indicating the transferability of thisapproach.

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction