Persistent Manifold Learning of Protein Properties

Xingjian Xu; Zhe Su; Guo-Wei Wei; Chunmei Wang

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arXiv · 2607.25115

Persistent Manifold Learning of Protein Properties

Xingjian Xu·Zhe Su·Guo-Wei Wei·Chunmei Wang

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Abstract

Predicting how tightly two biomolecules bind remains a major challenge, in part because different interaction classes present dissimilar interfaces, from compact metal-coordinated pockets to broad, featureless protein surfaces. We introduce persistent manifold learning (PML), a novel computational framework that describes a binding interface as a family of multiscale manifolds. Boundary-Induced Graph Laplacian, a discrete realization of de Rham-Hodge theory, then extracts topological invariants together with nonharmonic spectral information, capturing the geometry of an interface as well as its topology. These manifold embeddings are combined with protein and molecular language model representations and paired with gradient boosting decision trees. Our PML outperforms state-of-the-art methods on metalloprotein-ligand and protein-protein benchmarks.

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BibTeX RIS

Xingjian Xu, Zhe Su, Guo-Wei Wei, Chunmei Wang. 2026-07-27. Persistent Manifold Learning of Protein Properties. https://arxiv.org/abs/2607.25115

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