Protein Fold Classification at Scale: Benchmarking and Pretraining

Classifying protein topology is essential for deciphering biological function, but progress is held back by the lack of large-scale benchmarks that avoid duplicates and by models that do not scale well. We introduce TEDBench, a large-scale, non-redundant benchmark for protein fold classification constructed from the Encyclopedia of Domains (TED) and Foldseek-clustered AlphaFold structures. We show that on TEDBench, current protein representation learning methods either require very large models or fail to deliver strong performance. To address this challenge, we propose Masked Invariant Autoencoders (MiAE), a self-supervised framework for protein structure representation learning. MiAE uses an extremely high masking ratio of up to $90\%$ with an $\mathrm{SE(3)}$-invariant encoder and a lightweight decoder that reconstructs backbone coordinates from the latent representation and mask tokens. MiAE scales well and outperforms supervised counterparts and state-of-the-art baselines on TEDBench, establishing a strong recipe for protein fold classification. To test transfer beyond AlphaFold structures, we further benchmark on a curated dataset from experimental structures of CATH v4.4. TEDBench is available at https://github.com/BorgwardtLab/TEDBench.

Protein shape is closely tied to biological function, but evaluating AI methods for understanding protein structure has been difficult because existing benchmarks are limited in scale. This paper introduces TEDBench, a large benchmark for protein fold classification built from diverse predicted and experimental structures. It also presents MiAE, a method that learns useful protein-structure representations by reconstructing missing parts of protein backbones. MiAE performs strongly on TEDBench, showing that large-scale structure-based learning can improve how AI systems classify and represent protein folds.