CATH: an expanded resource to predict protein function through structure and sequence

By N. Dawson, Tony E. Lewis, Sayoni Das, Jonathan G. Lees, David A. Lee, P. Ashford, Christine A. Orengo, Ian Sillitoe. 2016

377 citations recorded by the source corpus.

The latest version of the CATH-Gene3D protein structure classification database has recently been released (version 4.1, http://www.cathdb.info). The resource comprises over 300 000 domain structures and over 53 million protein domains classified into 2737 homologous superfamilies, doubling the number of predicted protein domains in the previous version. The daily-updated CATH-B, which contains our very latest domain assignment data, provides putative classifications for over 100 000 additional protein domains. This article describes developments to the CATH-Gene3D resource over the last two years since the publication in 2015, including: significant increases to our structural and sequence coverage; expansion of the functional families in CATH; building a support vector machine (SVM) to automatically assign domains to superfamilies; improved search facilities to return alignments of query sequences against multiple sequence alignments; the redesign of the web pages and download site.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models (2022, background): “2 S95 domains [27, 10] from the Protein Data Bank (PDB) [6].”
The Pfam protein families database in 2019 (2018, methodology): “We aim to ensure that Pfam entries and clan relationships are consistent with other structural classifications [e.g. CATH (10), SCOP (11)], and entries are consistent with each other within a clan, ideally having the same Pfam type and models of comparable sizes.”
SWISS-MODEL: homology modelling of protein structures and complexes (2018, neutral): “We include links to the RCSB (33), PDBsum (40), PDBe (41), CATH (42) and SwissDock (43).”
Protein sequence design with a learned potential (2022, methodology): “We use a 3D convolutional neural network as our classi ﬁ er θ , training the model on X-ray crystal structures of CATH 4.2 S95 domains 37 – 39 , with train and test set domains separated at the topology level.”
End-to-end differentiable learning of protein structure (2018, methodology): “We next used the CATH database (Dawson et al., 2017), which hierarchically classifies proteins into structural families, to partition data points into CATH classes and visualize their distribution in RGN space.”
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) (2019, methodology): “These data are all obtained by collecting statistics from PDB 9 and CATH.”
HMMER web server: 2018 update (2018, methodology): “The existing profile HMM libraries (Pfam (2), CATH-Gene3D (16), TIGRFAMs (17), Superfamily (18) and PIRSF (19)) have been supplemented by those of the TreeFam (20) database of phylogenetic trees.”
RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences (2020, background): “• CATH hierarchical classification of protein domain structures (https://www.cathdb.info).”
A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures (2018, background): “0) was downloaded on February 11, 2018 (70).”
Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “Naturally occurring proteins with the same fold topology can have distinct functions because of ﬁ ne-tuned differences in the precise geometries of structural elements (74, 75).”
PDBe: improved findability of macromolecular structure data in the PDB (2019, methodology): “For example, the SIFTS project (9) brings together PDB structures with protein sequence data and annotations from UniProtKB (10), Pfam (11) and InterPro (12), structure domains from CATH (13) and SCOP (14), and, more recently, started incorporating genetic variation data from Ensembl (15) and putati”
Rosetta FunFolDes – A general framework for the computational design of functional proteins (2018, methodology): “Each target case was composed of two structures of the same CATH superfamily(Dawson et al., 2017).”
Recent developments in deep learning applied to protein structure prediction (2019, background): “In fact, a convolutional network can be made even more robust by 81,82 and ECOD 83-85 can be used for this very purpose, as some studies have wisely done.”
ATOM3D: Tasks On Molecules in Three Dimensions (2020, background): “2 topology classes [Dawson et al., 2017], as described in [Anand et al.”