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

TL;DR

PersGNN is a novel deep learning model that combines topological data analysis and geometric deep learning to improve structure-based protein function prediction, outperforming existing methods.

Contribution

This work introduces PersGNN, a hybrid model that integrates topological and geometric features for enhanced protein function prediction from structure data.

Findings

PersGNN achieves a 9.3% increase in AUPR over baseline models.

High F1 scores demonstrate transferability across gene ontology categories.

Hybrid approach outperforms individual techniques in structure-based prediction.

Abstract

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.