A rugged binding landscape unifies static and dynamic paradigms in protein-protein interactions

Abstract

Predicting protein-protein interaction (PPI) affinities from structural data remains a challenge. Although binding funnel theory describes the formation of native complexes, the topography of the funnel bottom and its influence on affinity are often overlooked. Using two well-controlled nanobody-antigen datasets as model systems, we demonstrate that PPI can exist in both static and dynamic binding paradigms. These two nanobody series adopt nearly identical binding poses toward their respective antigens yet exhibit diverse affinities, each representing a distinct paradigm: a static paradigm in which affinity can be ranked by Rosetta scoring of co-crystal structures alone, and a dynamic paradigm in which affinity can only be ranked using molecular dynamics-sampled ensembles. The two paradigms differ in interfacial dynamics. In the dynamic series, the relative motion between binding partners ({Delta}RMSF) is temperature-sensitive, with optimal affinity correlation at 298 K. In the static series, {Delta}RMSF is minimal and insensitive to temperature. Local frustration analysis establishes a mechanistic bridge between interfacial dynamics and landscape topology. Dynamic interfaces exhibit increased frustration upon thermal sampling, facilitating sampling of functionally relevant microstates, whereas static interfaces show minimal frustration changes across temperatures. The temperature-dependent frustration difference mirrors {Delta}RMSF sensitivity, confirming local frustration as a determinant of interfacial dynamics. Furthermore, static interfaces are characterized by a higher density of canonical hotspot residues, while dynamic interfaces utilize interfacial ruggedness to modulate affinity. Together, these results demonstrate that conserved binding modes can encode different energy landscapes and provide a practical framework for determining when ensemble-based sampling is required for accurate affinity prediction.