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index [2024/10/15 11:42]
sillitoe [Contact us] 		index [2024/10/15 15:43] (current)
vwaman
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 Ensembl are scanned against CATH HMMs to predict domain sequence boundaries and Ensembl are scanned against CATH HMMs to predict domain sequence boundaries and
 make homologous superfamily assignments. make homologous superfamily assignments.
 +
 +== Recognition as a Global Core BioData Resource ==
 +CATH has been recognized as a Global Core BioData Resource (GCBR) by the Global Biodata Consortium. This endorsement reflects the database's significance as a reliable and comprehensive resource for protein structure classification in the life sciences community.
 +
 +
 +===== Expansion in CATH structural data from AlphaFold Database =====
 +
 +We are pleased to announce the release of CATH v4.4 (October 2024 ; https://beta.cathdb.info/), the latest update to the CATH (Class, Architecture, Topology, Homology) structural classification database. This release includes an up-to-date classification of PDB structures as well as over 90 million domain models from The Encyclopedia of Domains (TED) (https://ted.cathdb.info).
 +
 +== Integration of domains from The Encyclopedia of Domains (TED) ==
 +CATH v4.4 incorporates approximately ~600.000 newly classified domain structures from the Protein Data Bank (PDB) and maps over 90 million predicted domain structures from the Encyclopedia of Domains (TED) resource into CATH superfamilies—a joint effort between the Jones group (UCL Computer Science) and the Orengo group (UCL Structural and Molecular Biology). This integration has resulted in a 180-fold increase in structural information for CATH superfamilies.
 +
 +The inclusion of TED data has expanded the number of superfamilies from 5,841 to 6,573, folds from 1,349 to 2,081, and architectures from 41 to 77. It is important to note that the TED data comprises predicted structures, and these new folds and architectures remain hypothetical until experimentally confirmed.
 +
 +Advancements in Domain Segmentation and Classification: 
 +To manage the substantial volume of data from AlphaFold Protein Structure Database, our automated domain segmentation workflow has been enhanced. We have integrated a faster and more accurate in-house deep-learning approach called Chainsaw, along with the publicly available methods Merizo and UniDoc. For homologue detection and verification, we predicted CATH superfamilies using a deep-learning tool based on embeddings from protein language models, CATHe, and expanded our suite of protein structure comparison tools to include Foldseek. Domains from PDB and TED with a homology assignment were further validated using Hidden Markov Model matching with strict overlaps and manual curation. These advancements enable the classification of domains into CATH superfamilies using evidence from multiple independent approaches, including both structural and sequence-based methods.
 +
 +Expansion of Functional Families (FunFams): 
 +Within superfamilies, CATH further subclassifies domains into coherent sets of sequences where functions are conserved, called Functional Families (FunFams). We updated the sequences in FunFams to UniProt release 2024_02, achieving a 276% increase in FunFam coverage. Additionally, the mapping of TED structural domains has resulted in a fourfold increase in FunFams with structural information, increasing the number of FunFams with at least one high-quality structural representative to 73,215.
 +This expansion enhances our ability to analyze conserved residues within protein families and to identify putative functional sites, contributing to a deeper understanding of protein function and evolution.
 +
 +Identification of Novel Folds and Architectures: 
 +Analysis of TED data has led to the identification of 479 new folds and 34 new architectures, including structures such as the Alpha-propeller, Beta hairpin barrel, and Alpha-Beta flower. These new categories are currently hypothetical and await experimental confirmation.
 +
 +Future Directions: 
 +The extensive data integrated into CATH v4.4 presents opportunities for further exploration of protein structures and evolutionary relationships. Ongoing efforts will focus on refining algorithms and workflows to improve domain boundary assignments, particularly in complex structures such as repeats and proteins with large interfaces.
 +
 +
  
 ===== CATH Releases ===== ===== CATH Releases =====

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