Detecting protein complexes in protein interaction networks using Mapper and Graph Convolution Networks

Abstract

Protein complexes are groups of interacting proteins that are central to multiple biological processes. Studying protein complexes as well as their constituents can enhance our understanding of cellular functions and malfunctions, and thus leads to the development of more effective cures for diseases. High-throughput experimental techniques allow the generation of large-scale protein-protein interaction datasets. Accordingly, various computational approaches were proposed to predict protein complexes from protein-protein interaction networks in which nodes and edges represent proteins and their interactions, respectively. State-of-the-art approaches mainly rely on clustering static networks to identify complexes. However, since protein interactions are highly dynamic in nature, recent approaches seek to model such dynamics by typically integrating gene expression data and identifying protein complexes accordingly. We propose MComplex, a method that uses time-series gene expression with interaction data to generate a temporal network which is passed to a generative adversarial network that utilizes a graph convolutional network as generator. This creates embeddings which are then analyzed using a modified graph-based version of the Mapper algorithm to detect corresponding protein complexes. We test our approach on multiple benchmark datasets and compare identified complexes against gold-standard protein complex datasets. Our results show that MComplex outperforms existing methods in several evaluation aspects, namely recall and sensitivity as well as a composite score covering aggregated evaluation measures.