Learning Hierarchical Protein Representations via Complete 3D Graph Networks (2207.12600v2)

Abstract: We consider representation learning for proteins with 3D structures. We build 3D graphs based on protein structures and develop graph networks to learn their representations. Depending on the levels of details that we wish to capture, protein representations can be computed at different levels, \emph{e.g.}, the amino acid, backbone, or all-atom levels. Importantly, there exist hierarchical relations among different levels. In this work, we propose to develop a novel hierarchical graph network, known as ProNet, to capture the relations. Our ProNet is very flexible and can be used to compute protein representations at different levels of granularity. By treating each amino acid as a node in graph modeling as well as harnessing the inherent hierarchies, our ProNet is more effective and efficient than existing methods. We also show that, given a base 3D graph network that is complete, our ProNet representations are also complete at all levels. Experimental results show that ProNet outperforms recent methods on most datasets. In addition, results indicate that different downstream tasks may require representations at different levels. Our code is publicly available as part of the DIG library (\url{https://github.com/divelab/DIG}).

• The paper introduces ProNet, a novel graph network that leverages complete 3D protein structures to learn hierarchical representations.
• It employs multi-level granularity by modeling proteins at the amino acid, backbone, and all-atom levels for nuanced structural insight.
• Experimental results show enhanced performance in fold classification, ligand binding, and protein interaction tasks.

Learning Hierarchical Protein Representations via Complete 3D Graph Networks

This paper addresses the problem of protein representation learning, emphasizing the use of three-dimensional (3D) structural data. Traditional methods often overlook the intricate hierarchical nature of proteins, which can be crucial for understanding their functions and interactions. To this end, the authors propose a novel graph network-based model named ProNet, which leverages the natural hierarchy in protein structures to create more accurate and efficient representations.

Key Innovations

1. Hierarchical Representation Learning: The paper introduces a method for modeling proteins at varying levels of granularity: amino acid level, backbone level, and all-atom level. This hierarchy captures the intrinsic 3D structure of proteins, allowing for more nuanced protein modeling.
2. Complete Geometric Representations: ProNet incorporates complete geometric representations at each level to fully capture 3D protein structures. Such complete representations are crucial for generating accurate and distinct models that are invariant to transformations such as rotations and translations.
3. Efficiency and Flexibility: By treating each amino acid as a node, ProNet maintains computational efficiency while effectively integrating the hierarchical relations within proteins. This design choice significantly reduces complexity compared to methods that treat individual atoms as nodes.
4. Experimental Validation: ProNet demonstrates superior performance over existing methods across a broad suite of tasks, including protein fold and function prediction, protein-ligand binding affinity prediction, and protein-protein interaction prediction. These results suggest that different downstream tasks benefit from representations at different hierarchical levels.

Numerical Insights

The model's efficacy is underscored by its performance metrics. For instance, in terms of accuracy, ProNet outperforms baseline models on most datasets. Specifically, ProNet-backbone achieves remarkable results in fold classification tasks, highlighting the importance of backbone-level details in understanding protein functions. Meanwhile, ProNet-all-atom shows significant enhancement in ligand binding and protein interaction tasks, illustrating that capturing side chain information can be crucial for interaction-based tasks.

Implications and Future Directions

The implications of this work are multifaceted. Practically, ProNet offers a flexible and powerful tool for diverse bioinformatics applications, from drug discovery to protein engineering. Theoretically, it sets a precedent for leveraging hierarchical structures in other domains of computational biology.

Looking forward, potential developments could include extending the hierarchical framework to encompass dynamic structural changes in proteins, which are relevant in many biological processes. Additionally, integrating other modalities such as genomic data could further enhance predictive performance.

In conclusion, ProNet represents a methodical advancement in protein representation learning by combining robust geometric foundations with a keen understanding of protein structure. This approach not only advances the state-of-the-art but also opens new avenues for research in protein science and computational biology.