Thank you for the nice comments. Most figures are made with “soft” or “flat” lighting with gainsboro color for the protein :)

Folddisco accurately detects discontinuous motifs like zinc fingers and segment-based motifs, previously requiring separate tools. Additionally, we built a SCOPe benchmark by sampling conserved residues from families and measuring the recall up to the first false positive. 3/9

Folddisco builds indexes faster and smaller than previous tools: indexing AFDB50 (53M structures) takes only ~24h vs. ~20 days (extrapolated) for pyScoMotif. Querying a zinc-finger motif across AFDB50 takes just ~13s, up to 48x faster than pyScoMotif. 4/9

Folddisco can annotate proteins: querying a canonical zinc-finger uncovers an uncharacterized oyster protein and metagenomic proteins. It also detects partial catalytic metal sites in E. coli peptide deformylase. All of these hits would be missed by Foldseek or sequence aligners. 5/9

Folddisco can distinguish functional states. We searched GPCR activation motifs (CWxP, NPxxY, DRY), clearly separating active/inactive states. A search in the AFDB shows ~53% active, closely mirroring experimental PDB 54%, suggesting AlphaFold might follow its training conformation distribution. 6/9

Folddisco can be applied for PPI interface searches. When querying an interface between antibody chains (gray/black), it successfully identifies matching interfaces within monomeric antibody fragments (cyan), showcasing its potential to detect novel interaction partners and interfaces. 7/9

We provide a user-friendly Folddisco webserver, enabling instant structural motif searches in PDB, AFDB-Proteomes, AFDB50 (available later today), and ESMatlas (ESM30). Explore it here: search.foldseek.com/folddisco 8/9

We identified 11,941 novel multi-domain combinations. We found membrane-associated domains (e.g., TonB dependent receptor, highlighting domain recombination rather than new folds as a driver of structural innovation. 5/n

ESMatlas uses MGnify environmental labels. Leveraging this, we computed the lowest common biomes per structural cluster, revealing protein adaptations unique to specific environments, especially extreme ones like hyperthermal, hypersaline, and glaciers. 3/n

It's a big collaborative effort by @jingiyeo.bsky.social @yewonhan.bsky.social @nbordin.bsky.social, Andy Lau, Shaun M. Kandathil, @hbkgenomics.bsky.social, Eli Levy Karin, @milot.bsky.social David T. Jones and Christine Orengo.
Visit our #RECOMB2025 poster (719) & talk (1 pm at B145 on April 29).