Chapter25: Graph Neural Networks in Predicting Protein Function and Interactions

Anowarul Kabir, George Mason University, akabir4@gmu.edu
Amarda Shehu, George Mason University, amarda@gmu.edu

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Abstract

Graph Neural Networks (GNNs) are becoming increasingly popular and powerful tools in molecular modeling research due to their ability to operate over non-Euclidean data, such as graphs. Because of their ability to embed both the inherent structure and preserve the semantic information in a graph, GNNs are advancing diverse molecular structure-function studies. In this chapter, we focus on GNNaided studies that bring together one or more protein-centric sources of data with the goal of elucidating protein function. We provide a short survey on GNNs and their most successful, recent variants designed to tackle the related problems of predicting the biological function and molecular interactions of protein molecules. We review the latest methodological advances, discoveries, as well as open challenges promising to spur further research.

Contents

  • From Protein Interactions to Function: An Introduction
    • Enter Stage Left: Protein-Protein Interaction Networks
    • Problem Formulation(s), Assumptions, and Noise: A Historical Perspective
    • Shallow Machine Learning Models over the Years
    • Enter Stage Right: GNNs
  • Highlighted Case Studies
    • Case Study 1: Prediction of Protein-Protein and Protein-Drug Interactions: The Link Prediction Problem
    • Case Study 2: Prediction of Protein Function and Functionally-important Residues
    • Case Study 3: From Representation Learning to Multirelational Link Prediction in Biological Networks with Graph Autoencoders
  • Future directions

Citation

@incollection{GNNBook-ch25-kabir,
author = "Kabir, Anowarul and Shehu, Amarda",
editor = "Wu, Lingfei and Cui, Peng and Pei, Jian and Zhao, Liang",
title = "Graph Neural Networks in Predicting Protein Function and Interactions",
booktitle = "Graph Neural Networks: Foundations, Frontiers, and Applications",
year = "2022",
publisher = "Springer Singapore",
address = "Singapore",
pages = "541--556",
}

A. Kabir and A. Shehu, “Graph neural networks in predicting protein function and interactions,” in Graph Neural Networks: Foundations, Frontiers, and Applications, L. Wu, P. Cui, J. Pei, and L. Zhao, Eds. Singapore: Springer Singapore, 2022, pp. 541–556.