Multi-Scale Representation Learning on Proteins

TL;DR

This paper presents a multi-scale graph-based approach for protein representation learning, capturing surface, structure, and sequence details, leading to improved performance on ligand binding and function prediction tasks with enhanced efficiency.

Contribution

It introduces a novel multi-scale graph construction for proteins, integrating surface, structure, and sequence information, and demonstrates its effectiveness on key biological prediction tasks.

Findings

Outperforms baselines on ligand binding affinity regression.

Achieves near top performance on protein function prediction with fewer parameters.

Uses superpixels to improve memory efficiency with minimal performance loss.

Abstract

Proteins are fundamental biological entities mediating key roles in cellular function and disease. This paper introduces a multi-scale graph construction of a protein -- HoloProt -- connecting surface to structure and sequence. The surface captures coarser details of the protein, while sequence as primary component and structure -- comprising secondary and tertiary components -- capture finer details. Our graph encoder then learns a multi-scale representation by allowing each level to integrate the encoding from level(s) below with the graph at that level. We test the learned representation on different tasks, (i.) ligand binding affinity (regression), and (ii.) protein function prediction (classification). On the regression task, contrary to previous methods, our model performs consistently and reliably across different dataset splits, outperforming all baselines on most splits. On the classification task, it achieves a performance close to the top-performing model while using 10x fewer parameters. To improve the memory efficiency of our construction, we segment the multiplex protein surface manifold into molecular superpixels and substitute the surface with these superpixels at little to no performance loss.