ArticleProtein science : a publication of the Protein Society2024
Clustering protein functional families at large scale with hierarchical approaches.
Article in Protein science : a publication of the Protein Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- The protein-water H-bond network for bacterial response regulator REC domain activation.Journal of structural biology: X · 2026Article
- Structural plasticity drives convergent evolution and functional diversification of GNAT polyamine acetyltransferases.Structure (London, England : 1993) · 2026Article
- Sequence Conservation and Functional Significance of Charged Clusters in Mitochondria-Located Proteins of Green Plants.Journal of molecular evolution · 2026Article
- A million shades of green: understanding and harnessing plant metabolic diversity.The EMBO journal · 2025Review
- Clustering protein functional families at large scale with hierarchical approaches.Protein science : a publication of the Protein Society · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Proteins, fundamental to cellular activities, reveal their function and evolution through their structure and sequence. CATH functional families (FunFams) are coherent clusters of protein domain sequences in which the function is conserved across their members. The increasing volume and complexity of protein data enabled by large-scale repositories like MGnify or AlphaFold Database requires more powerful approaches that can scale to the size of these new resources. In this work, we introduce MARC and FRAN, two algorithms developed to build upon and address limitations of GeMMA/FunFHMMER, our original methods developed to classify proteins with related functions using a hierarchical approach. We also present CATH-eMMA, which uses embeddings or Foldseek distances to form relationship trees from distance matrices, reducing computational demands and handling various data types effectively. CATH-eMMA offers a highly robust and much faster tool for clustering protein functions on a large scale, providing a new tool for future studies in protein function and evolution.
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