ProtFinder: finding subcellular locations of proteins using protein interaction networks

Abstract

Protein subcellular localization prediction plays a crucial role in improving our understandings of different diseases and consequently assists in building drug targeting and drug development pipelines. Proteins are known to co-exist at multiple subcellular locations which make the task of prediction extremely challenging. A protein interaction network is a graph that captures interactions between different proteins. It is safe to assume that if two proteins are interacting, they must share some subcellular locations. With this regard, we propose ProtFinder - the first deep learning-based model that exclusively relies on protein interaction networks to predict the multiple subcellular locations of proteins. We also integrate biological priors like the cellular component of Gene Ontology to make ProtFinder a more biology-aware intelligent system. ProtFinder is trained and tested using the STRING and BioPlex databases whereas the annotations of proteins are obtained from the Human Protein Atlas. Our model gives an AUC-ROC score of 90.00% and an MCC score of 83.42% on a held-out set of proteins. We also apply ProtFinder to annotate proteins that currently do not have confident location annotations. We observe that ProtFinder is able to confirm some of these unreliable location annotations, while in some cases complementing the existing databases with novel location annotations.