CATH: increased structural coverage of functional space

By Ian Sillitoe, Nicola Bordin, N. Dawson, Vaishali P. Waman, P. Ashford, Harry M. Scholes, Camilla S. M. Pang, Laurel Woodridge, Clemens Rauer, Neeladri Sen and 8 more. Nucleic Acids Res., 2020

432 citations recorded by the source corpus.

Abstract CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.

Subfields: Biophysics

View Paper: https://www.semanticscholar.org/paper/8f36a3da7526b3d022c418ba89c42822c6652188

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In Biophysics

Citations in the corpus

InterPro in 2022 (2022, neutral): “Since our previous publication that described In-terPro 81.0 in 2020 (20), there have been 9 InterPro re-leases, integrating 10 member database updates: CDD (3.18), CATH-Gene3D (4.”
RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning (2022, neutral): “CATH(102) Protein structure classification (Class, Architecture,”
BeStSel: webserver for secondary structure and fold prediction for protein CD spectroscopy (2022, neutral): “Moreover, it is capable of fold prediction down to the topology / homology level of CATH protein fold classification (5).”
Illuminating protein space with a programmable generative model (2022, methodology): “Using TMalign [Zhang and Skolnick, 2005], they were each aligned to the 32k structurally conserved domains from CATHdb S40 set [Sillitoe et al., 2021] and filtered for TMscore greater than 0.”
Bilingual language model for protein sequence and structure (2024, positive): “This orthogonal information can be leveraged by concatenating the embeddings from amino acid-and predicted 3Di-sequences (ProstT5(cat)) improving over either method at all CATH levels.”
SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding (2021, methodology): “We randomly select 2065 domains from the CATH database [32] (daily snapshot: 20201021), and split them into training, validation, and test subsets of size 1665/200/200.”
ProGen2: Exploring the Boundaries of Protein Language Models (2022, methodology): “The P RO G EN 2-large model was ﬁnetuned for two epochs on 1M sequences, from Gene3D (Lewis et al., 2018) and CATH (Sillitoe et al., 2021), adopting a two-layer sandwich architecture (CATH 3.30).”
Scalable emulation of protein equilibrium ensembles with generative deep learning (2025, neutral): “A large fraction of training data, 46 ms, is dedicated to 1100 CATH domains, common building blocks of protein structure [33] (Sec.”
Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins (2022, neutral): “As an evaluation of the effectiveness of the DMPfold2 end-to-end model building procedure, we built three-dimensional (3D) models of 39 FM and FM/TBM domains from the CASP13 experiment.”