BioBlobs: Differentiable Graph Partitioning for Protein Representation Learning

• URL: http://arxiv.org/abs/2510.01632v1
• Date: Thu, 02 Oct 2025 03:25:02 GMT
• Title: BioBlobs: Differentiable Graph Partitioning for Protein Representation Learning
• Authors: Xin Wang, Carlos Oliver,
• Abstract summary: We introduce BioBlobs, a plug-and-play module that represents proteins by dynamically partitioning structures into flexibly-sized substructures ("blobs")<n>The resulting blobs are quantized into a shared and interpretable codebook, yielding a discrete vocabulary of function-relevant protein substructures used to compute protein embeddings.<n>We show that BioBlobs representations improve the performance of widely used protein encoders such as GVP-GNN across various PRL tasks.
• Score: 3.6641231031729173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Protein function is driven by coherent substructures which vary in size and topology, yet current protein representation learning models (PRL) distort these signals by relying on rigid substructures such as k-hop and fixed radius neighbourhoods. We introduce BioBlobs, a plug-and-play, fully differentiable module that represents proteins by dynamically partitioning structures into flexibly-sized, non-overlapping substructures ("blobs"). The resulting blobs are quantized into a shared and interpretable codebook, yielding a discrete vocabulary of function-relevant protein substructures used to compute protein embeddings. We show that BioBlobs representations improve the performance of widely used protein encoders such as GVP-GNN across various PRL tasks. Our approach highlights the value of architectures that directly capture function-relevant protein substructures, enabling both improved predictive performance and mechanistic insight into protein function.

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