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This study introduces persistent manifold learning (PML), a novel computational framework that characterizes biomolecular binding interfaces as multiscale manifolds to enhance prediction accuracy of binding affinities. By employing Boundary-Induced Graph Laplacian to extract topological invariants and spectral information, the method captures both the geometric and topological features of binding interfaces. The PML framework demonstrates superior performance over existing state-of-the-art techniques on metalloprotein-ligand and protein-protein interaction benchmarks, highlighting its effectiveness in addressing a significant challenge in biomolecular research.
PML outperforms existing methods by capturing the intricate geometry and topology of biomolecular binding interfaces, revealing new insights into protein interactions.
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.