StruPair: A structure-only classifier and benchmark for multi-state protein design

Designing proteins with multiple functional states, including allosterically regulated switches, molecular sensors, and enzymes, requires generating physically plausible conformations that represent the distinct states needed to carry out the intended function. However, evaluating whether computationally generated structural states correspond to realistic and functionally meaningful conformational changes remains challenging, as commonly used structural similarity metrics do not fully capture the diverse modes and scales of conformational change observed in natural proteins. Here, we introduce StruPair, a structure-only framework for classifying and benchmarking protein conformational changes using structure pairs collected from experiments, molecular dynamics (MD) simulations, and computational design. We train a hybrid classifier that combines structural comparison features with graph neural network representations. The trained StruPair classifier assigns each structure pair to one of five ordered classes based on the extent of conformational change and the preservation or reorganization of secondary and tertiary structure. Using only paired backbone structures, without amino acid identities or sequence embeddings, allows StruPair to evaluate candidate pairs before sequence design or optimization..We provide the evaluation set and the trained classifier through a web interface and API.