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Unsupervised domain classification of AlphaFold2-predicted protein structures
Open accessPreprint — published as 10.1103/prxlife.3.023009 · Paper page
Abstract
A bstract: The release of the AlphaFold database, which contains 214 million predicted protein structures, represents a major leap forward for proteomics and its applications. However, lack of comprehensive protein annotation limits its accessibility and usability. Here, we present DPCstruct, an unsupervised clustering algorithm designed to provide domain-level classification of protein structures. Using structural predictions from AlphaFold2 and comprehensive all-against-all local alignments from Foldseek, DPCstruct identifies and groups recurrent structural motifs into domain clusters. When applied to the Foldseek Cluster database, a representative set of proteins from the AlphaFoldDB, DPCstruct successfully recovers the majority of protein folds catalogued in established databases such as SCOP and CATH. Out of the 28,246 clusters identified by DPCstruct, 24% have no structural or sequence similarity to known protein families. Supported by a modular and efficient implementation, classifying 15 million entries in less than 48 hours, DPCstruct is well suited for large-scale proteomics and metagenomics applications. It also facilitates the rapid incorporation of updates from the latest structural prediction tools, ensuring that the classification remains up-to-date. The DPCstruct pipeline and associated database are freely available in a dedicated repository, enhancing the navigation of the AlphaFoldDB through domain annotations and enabling rapid classification of other protein datasets.
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“The recent release of the AlphaFold database (AlphaFoldDB) [1, 2], containing predictions for nearly 214 million protein structures, marked a significant breakthrough in proteomics, with applications ranging from structure-based drug discovery to variant effect prediction [3, 4].”
BackgroundA structural biology community assessment of AlphaFold2 applications
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Sorted by citations
Fold or flop: quality assessment of AlphaFold predictions on whole proteomes
Sarti, E., & Cazals, F. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag190 · Cited by 5
“We note in passing that such neighbors are sufficient to robustly infer protein domains using a direct application of spectral clustering (Cazals et al. 2025).”
Characterizing the fragmentation of AlphaFold predictions
Cazals, F., & Sarti, E. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag186 · Cited by 2
“An enhanced unsupervised clustering algorithm designed aiming at reliably identifying structural domains has also been developed (Barone et al. 2025), resulting in meta-clusters consistent with clans in Pfam .”
Metadata
- Authors
- Barone, F., Laio, A., Punta, M., Cozzini, S., Ansuini, A., & Cazzaniga, A.
- Affiliations
- University of Trieste (IT)
- The Abdus Salam International Centre for Theoretical Physics (ICTP) (IT)
- IRCCS Ospedale San Raffaele (IT)
- AREA Science Park (IT)
- Corresponding author
- Barone, Federico
- Venue
- PRX Life
- Publisher
- openRxiv
- Published
- 2024-08-21
- Volume
- 3
- Issue
- 2
- ISSN
- 2835-8279 (ISSN-L)
- OpenAlex
- W4410775456
- Cited by
- 3
- References
- 49
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- PRX Life (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Funders
- NextGenerationEU (CUP J97G22000400006, CI IR0000028)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein domain classification, AlphaFold2, protein structure prediction, unsupervised clustering, structural motifs, protein fold, Foldseek, SCOP, CATH, protein annotation, metagenomics, proteomics
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