Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training

TL;DR

GlycanAA is a novel hierarchical message passing model that captures atomic and backbone structures of glycans, significantly improving glycan property prediction through multi-scale pre-training on unlabeled data.

Contribution

The paper introduces GlycanAA, the first all-atom glycan modeling approach using hierarchical message passing and multi-scale pre-training, addressing limitations of previous backbone-only models.

Findings

GlycanAA outperforms existing glycan encoders in benchmarks.

PreGlycanAA further improves performance through pre-training.

The model effectively captures atomic-level interactions in glycans.

Abstract

Understanding the various properties of glycans with machine learning has shown some preliminary promise. However, previous methods mainly focused on modeling the backbone structure of glycans as graphs of monosaccharides (i.e., sugar units), while they neglected the atomic structures underlying each monosaccharide, which are actually important indicators of glycan properties. We fill this blank by introducing the GlycanAA model for All-Atom-wise Glycan modeling. GlycanAA models a glycan as a heterogeneous graph with monosaccharide nodes representing its global backbone structure and atom nodes representing its local atomic-level structures. Based on such a graph, GlycanAA performs hierarchical message passing to capture from local atomic-level interactions to global monosaccharide-level interactions. To further enhance model capability, we pre-train GlycanAA on a high-quality unlabeled glycan dataset, deriving the PreGlycanAA model. We design a multi-scale mask prediction algorithm to endow the model about different levels of dependencies in a glycan. Extensive benchmark results show the superiority of GlycanAA over existing glycan encoders and verify the further improvements achieved by PreGlycanAA. We maintain all resources at https://github.com/kasawa1234/GlycanAA