GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein Representation Learning

TL;DR

GOProteinGNN is a novel graph-based model that integrates protein knowledge graphs with amino acid sequences to produce enriched, context-aware protein representations, improving performance on various biological tasks.

Contribution

It introduces a new architecture that combines protein knowledge graphs with language models, learning the entire graph during training for more comprehensive protein representations.

Findings

Outperforms previous protein representation methods on multiple tasks

Effectively captures complex protein relationships and functional annotations

Establishes state-of-the-art results in protein representation learning

Abstract

Proteins play a vital role in biological processes and are indispensable for living organisms. Accurate representation of proteins is crucial, especially in drug development. Recently, there has been a notable increase in interest in utilizing machine learning and deep learning techniques for unsupervised learning of protein representations. However, these approaches often focus solely on the amino acid sequence of proteins and lack factual knowledge about proteins and their interactions, thus limiting their performance. In this study, we present GOProteinGNN, a novel architecture that enhances protein language models by integrating protein knowledge graph information during the creation of amino acid level representations. Our approach allows for the integration of information at both the individual amino acid level and the entire protein level, enabling a comprehensive and effective learning process through graph-based learning. By doing so, we can capture complex relationships and dependencies between proteins and their functional annotations, resulting in more robust and contextually enriched protein representations. Unlike previous methods, GOProteinGNN uniquely learns the entire protein knowledge graph during training, which allows it to capture broader relational nuances and dependencies beyond mere triplets as done in previous work. We perform a comprehensive evaluation on several downstream tasks demonstrating that GOProteinGNN consistently outperforms previous methods, showcasing its effectiveness and establishing it as a state-of-the-art solution for protein representation learning.