Publication details

AlphaFind v2: similarity search in AlphaFold DB and TED domains across structural contexts

Authors

SLANINÁKOVÁ Terézia ROŠINEC Adrián ČILLÍK Jakub KŘENEK Aleš GREŠOVÁ Katarína PORUBSKÁ Jana MARŠÁLKOVÁ Eva OĽHA Jaroslav PROCHÁZKA David HEJTMÁNEK Lukáš DOHNAL Vlastislav BERKA Karel SVOBODOVÁ Radka ANTOL Matej

Year of publication 2026
Type Article in Periodical
Magazine / Source NUCLEIC ACIDS RESEARCH
MU Faculty or unit

Institute of Computer Science

Citation
web
Doi https://doi.org/10.1093/nar/gkag372
Keywords Protein structure similarity; protein structure search; AlphaFold DB; TED: The Encyclopedia of Domains; vector embeddings; AlphaFind; similarity search
Description The availability of large-scale protein structure collections enables structure-based analysis of their function and evolution beyond what is possible from sequence alone. However, applying three-dimensional structure comparison at scale remains computationally demanding and limits practical exploration of large experimental and predicted collections. This creates a need for fast, structure-based search methods that retain biological relevance while enabling large-scale exploration. In this paper, we present AlphaFind v2, an application for finding structurally similar proteins in the AlphaFold Database (https://alphafold.ebi.ac.uk/) of predicted structures. AlphaFind v2 uses fast pre-filtering via state-of-the-art protein embeddings that preserve structural information, followed by refinement with US-align. The application presents multiple complementary search modes, including (i) search over full protein chains, (ii) search aware of the AlphaFold pLDDT metric, restricting similarity computation to the most stable and structurally relevant regions, (iii) search over protein domains from the TED database (https://ted.cathdb.info/), and (iv) a multidomain search mode, combining multiple chain-level domain matches within a single score and alignment. The application accepts protein identifiers and returns similar proteins with metrics, rich metadata, and interactive superpositions. AlphaFind v2 additionally allows searching within an organism or CATH label and matches the proteins with experimental structures. AlphaFind v2 is accessible at https://alphafind.ics.muni.cz/.
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