Persistent Manifold Learning of Protein Properties

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.

This work addresses a central problem in molecular biology and drug discovery: how to determine, from molecular structure alone, how tightly a molecule will bind to its target. Binding affinity governs therapeutic potency and selectivity, yet measuring it experimentally is slow and costly, making computational prediction essential for prioritizing candidates from large libraries. We apply our framework to two target classes ordinarily handled by separate methods: metalloenzymes, whose metal-coordinated active sites have long been exploited by drugs, and protein-protein interfaces, which regulate signaling and immune response yet resist conventional small molecules. Our results indicate that much of what determines binding strength is encoded in the shape of the interface itself, and that a single geometric description serves both classes without hand-tailored features. This suggests manifold-based representations may extend to other systems in which structure and sequence jointly determine function.

The core of our approach involves de Rham-Hodge theory on compact Riemannian manifolds with boundary, a foundational tool in differential geometry rarely applied to molecular data. Binding interfaces are modeled as sublevel sets of Gaussian density fields, producing a filtration of manifolds whose topological transitions occur at the critical values of the level set function, as governed by Morse theory. The topology of each manifold is recovered from the kernels of Hodge Laplacians under normal and tangential boundary conditions, whose dimensions are Betti numbers by the Hodge and Friedrichs theorems, while their nonzero spectra reflect geometry. Discretization proceeds through discrete exterior calculus on Cartesian grids, yielding Boundary-Induced Graph Laplacians whose spectral analysis reduces to the singular values of the discrete differentials. Extending this across the filtration leads to persistent Hodge Laplacians on evolving manifolds, combining differential geometry, algebraic topology, and spectral theory into a multiscale representation of molecular shape.