CATH Tools

Overview

Protein structure comparison tools such as SSAP, as used by the Orengo Group in curating CATH.

Executable DOWNLOADS (for Linux/Mac; chmod them to be executable)	Docs	Code	Extras repo

Tools

	cath-cluster Complete-linkage cluster arbitrary data.
	cath-map-clusters Map names from previous clusters to new clusters based on (the overlaps between) their members (which may be specified as regions within a parent sequence). Renumber any clusters with no equivalents.
	cath-resolve-hits Collapse a list of domain matches to your query sequence(s) down to the non-overlapping subset (ie domain architecture) that maximises the sum of the hits' scores.
	cath-ssap Structurally align a pair of proteins.
	cath-superpose Superpose two or more protein structures using an existing alignment.

Extra Tools

• build-test Perform the cath-tools tests (which should all pass, albeit with a few warnings)
• cath-assign-domains Use an SVM model on SSAP+PRC data to form a plan for assigning the domains to CATH superfamilies/folds
• cath-refine-align Iteratively refine an existing alignment by attempting to optimise SSAP score
• cath-score-align Score an existing alignment using structural data

Authors

The SSAP algorithm (cath-ssap) was devised by Christine A Orengo and William R Taylor.

Please cite: Protein Structure Alignment, Taylor and Orengo, Journal of Molecular Biology 208, 1-22, PMID: 2769748. (PubMed, Elsevier)

Since then, many people have contributed to this code, most notably:

• Tony E Lewis (2011–…)
• Oliver C Redfern (~2003–2011)
• James E Bray, Ian Sillitoe (~2000–2003)
• Andrew C R Martin (considerable edits around 2001)

Acknowledgements

cath-ssap typically uses DSSP, either by reading DSSP files or via its own implementation of the DSSP algorithms.

cath-cluster uses Fionn Murtagh's reciprocal-nearest-neighbour algorithm (see Multidimensional clustering algorithms, volume 4 of Compstat Lectures. Physica-Verlag, Würzburg/ Wien, 1985. ISBN 3-7051-0008-4) as described and refined in Daniel Müllner's Modern hierarchical, agglomerative clustering algorithms (2011, arXiv:1109.2378).

Feedback

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