PRETZEL: A Benchmark for Cyclic Peptide Structure Prediction

Macrocyclic peptides have drawn growing attention as a therapeutic modality, and co-folding models are increasingly applied to predicting their structures. Yet published evaluations rely on small, loosely curated target sets that differ from paper to paper, so reported results are not comparable, and most report accuracy through pose RMSD alone, without checking whether the predicted molecule is chemically correct. We introduce PRETZEL (Prediction of Ring-Enclosed Topology: Zero-shot Evaluation of cycLic peptides), a benchmark of 285 curated cyclic peptide–protein complexes spanning three cyclization chemistries, all post-dating the training cutoff of every model evaluated. PRETZEL comprises three components: PRETZEL-SET, an evaluation set annotated by cyclization chemistry and chemical novelty; PRETZEL-TWIST, a specification layer that compiles a single canonical macrocycle description into each engine's native input format and verifies the returned structure; and PRETZEL-SALT, an evaluation suite pairing standard pose metrics with chemical validity checks for bond distance, bond angles, dihedrals, and ring closure. Using these, we evaluate four engines: AlphaFold3, Boltz-2, Protenix-v2, and HighFold3. Protenix-v2 achieves the best median RMSD at 9.05 Å, with 19.3% of predictions under 2 Å and 37.5% under 5 Å. Performance varies by cyclization chemistry: models handle head-to-tail macrocycles relatively well but degrade on stapled targets. Chemical validity is even weaker, with at most 8.1% of predictions having a fully valid set of inter-residue bonds, and no engine closes every macrocycle in more than 80.7% of its predictions, which indicates that pose accuracy alone is an insufficient criterion for these models. These failures share a cause: co-folding models place macrocycles without representing them, treating cyclization and non-canonical chemistry as annotations layered onto a residue sequence rather than as first-class structure. PRETZEL is released with all inputs, predictions, and evaluation code.