CATH--a hierarchic classification of protein domain structures.

By Christine A. Orengo, A. Michie, S. Jones, David C. Jones, M. Swindells, Janet M. Thornton. 1997

Subfields: Computational Biology & Bioinformatics

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Citations in the corpus

Robust deep learning based protein sequence design using ProteinMPNN (2022, methodology): “7k high resolution single-chain structures from the PDB split based on the CATH (7) protein classification (see Methods).”
ECOD: An Evolutionary Classification of Protein Domains (2014, background): “Currently, the two leading structure classifications are SCOP (Structural Classification of Proteins) [4] and CATH (Class, Architecture, Topology, Homology) [5], both of which are widely used in analyzing protein sequence, structure, function, and evolution and in developing various bioinformatics t”
A series of PDB related databases for everyday needs (2010, background): “This might seem a bit surprising because there are not that many biological questions that require knowledge of a protein’s secondary structure to be answered, but in practice the secondary structure of a protein is an important tool for classiﬁ-cation and comparison purposes (see for examples the C”
Learning inverse folding from millions of predicted structures (2022, methodology): “To evaluate the generalization performance across different protein folds, we split the train, validation, and test data based on the CATH hierarchical classification of protein structures (Orengo et al., 1997) for both data sources.”
TM-align: a protein structure alignment algorithm based on the TM-score (2005, neutral): “For example, many authors use SCOP (1) or CATH (2) as the gold standard and assess the structural alignments based on the fold classi-ﬁcations in these databases (23–25).”
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins (2014, background): “…Positive Predictive Values (PPVs) by a log-linear function: PPV ¼ a logðbxþ cÞ; where a, b and c are adjustable parameters obtained by least-squares fitting to the observed PPVs when the training data are re-presented to the networks and binned according to neural network output score (x).”
More than the sum of their parts: On the evolution of proteins from peptides (2003, unknown): “This is the basis of their classification into families and superfamilies((17,18)) in a way analogous to the classification of organisms into genera and orders.”
Computational prediction of secondary and supersecondary structures. (2013, background): “The secondary structure, which is assigned from the tertiary structure, is used for a variety of applications, including visualization (61-63) and classification of the protein folds (64-67), and as a ground truth to develop and evaluate the secondary and SSS predictors.”
Reconstruction of Protein Backbones from the BriX Collection of Canonical Protein Fragments (2008, methodology): “database [7], the CATH database [8] or the FSSP classification [9,10].”
Intrinsic protein disorder in complete genomes. (2000, background): “Thus, associating a sequence with a particular structural family [25] or with a particular sequence motif [17] provides an avenue to predict function.”