Decoupling Topology from Geometry: Detecting Large-Scale Conformational Changes via Conformational Scanning

Protein function is fundamentally driven by structural dynamics, yet the majority of structural bioinformatics treats proteins as static rigid bodies. While Molecular Dynamics (MD) simulations attempt to capture these motions, they are computationally prohibitive for exploring large-scale conformational changes, such as domain movements or allostery, which occur on timescales often inaccessible to standard simulation. However, the Protein Data Bank (PDB) contains a latent wealth of dynamic information in the form of redundant entries proteins solved in multiple distinct conformational states. Detecting these "shape-shifting" pairs remains challenging because standard structural alignment algorithms (e.g., TM-align) rely on rigid-body superposition, which fails when substantial geometric rearrangement occurs. In this study, we introduce a high-throughput method to systematically mine the PDB for proteins that share identical topology but exhibit divergent tertiary conformations. By utilizing a coarse-grained Secondary Structure Element (SSE) representation, we decouple topological connectivity from geometric rigidity, allowing for the detection of conformational homologues that share low global structural similarity despite high predicted structural similarity. We applied this "conformational scanning" across the entire RCSB database, identifying a curated dataset of proteins undergoing significant structural rearrangements. This work bridges the gap between static structural data and dynamic function, providing a critical "ground truth" dataset for benchmarking data-driven protein design and checking the plausibility of generative structure models.