Structural relationships between proteins help infer function and evolutionary history. Fast structural search tools such as Foldseek enable large-scale retrieval but often miss remote structural homologs. We address this gap in two stages.

First, we learn a compact retrieval embedding from ESM2 representations using DALI-derived structural similarity as contrastive supervision. This injects structural signal during training while keeping inference sequence-only, and yields a direct retrieval baseline of 0.5918 pooled AUPRC on a SCOPe fold-level benchmark against the AlphaFold Database v2 (AFDB2), substantially above Foldseek (0.3828). Second, we introduce ISS-Priority, a propagation-based retrieval method that searches the learned embedding space as a graph and uses selective DALI validation to guide frontier expansion. Because runtime is controlled by the DALI validation budget, users can trade speed for recall. By recovering indirect structural hits through validated intermediate neighbors, ISS-Priority raises pooled AUPRC to 0.6128.

Together, these results show that combining DALI-supervised representation learning with propagation-based retrieval extends structure-aware search beyond direct retrieval alone.

Co-authors: Liisa Holm