DeepAutoPIN: An automorphism orbits based deep neural network for characterizing the organizational diversity of protein interactomes across the tree of life

TL;DR

This study analyzes protein interaction networks across the tree of life using automorphism orbit profiles and deep learning, revealing evolutionary constraints and phyla-specific architectural features.

Contribution

It introduces a novel approach combining automorphism orbit analysis with deep neural networks to characterize and differentiate protein interaction network architectures across diverse life forms.

Findings

Orbit usage profiles differ significantly across domains and phyla.

PIN wiring patterns are shaped by evolutionary constraints, not randomness.

Deep neural network achieves 85% accuracy in classifying networks based on orbit features.

Abstract

The enormous diversity of life forms thriving in drastically different environmental milieus involves a complex interplay among constituent proteins interacting with each other. However, the organizational principles characterizing the evolution of protein interaction networks (PINs) across the tree of life are largely unknown. Here we study 4,738 PINs belonging to 16 phyla to discover phyla-specific architectural features and examine if there are some evolutionary constraints imposed on the networks' topologies. We utilized positional information of a network's nodes by normalizing the frequencies of automorphism orbits appearing in graphlets of sizes 2-5. We report that orbit usage profiles (OUPs) of networks belonging to the three domains of life are contrastingly different not only at the domain level but also at the scale of phyla. Integrating the information related to protein families, domains, subcellular location, gene ontology, and pathways, our results indicate that wiring patterns of PINs in different phyla are not randomly generated rather they are shaped by evolutionary constraints imposed on them. There exist subtle but substantial variations in the wiring patterns of PINs that enable OUPs to differentiate among different superfamilies. A deep neural network was trained on differentially expressed orbits resulting in a prediction accuracy of 85%.