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Open Access Research article

ROC and confusion analysis of structure comparison methods identify the main causes of divergence from manual protein classification

Vichetra Sam1, Chin-Hsien Tai2, Jean Garnier13, Jean-Francois Gibrat3, Byungkook Lee2 and Peter J Munson1*

Author affiliations

1 Mathematical and Statistical Computing Laboratory, DCB, CIT, NIH, DHHS, Bethesda, MD, USA

2 Laboratory of Molecular Biology, CCR, NCI, NIH, DHHS, Bethesda, MD, USA

3 Mathematique Informatique et Genome, INRA, Jouy-en-Josas, France

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Citation and License

BMC Bioinformatics 2006, 7:206  doi:10.1186/1471-2105-7-206

Published: 13 April 2006

Abstract

Background

Current classification of protein folds are based, ultimately, on visual inspection of similarities. Previous attempts to use computerized structure comparison methods show only partial agreement with curated databases, but have failed to provide detailed statistical and structural analysis of the causes of these divergences.

Results

We construct a map of similarities/dissimilarities among manually defined protein folds, using a score cutoff value determined by means of the Receiver Operating Characteristics curve. It identifies folds which appear to overlap or to be "confused" with each other by two distinct similarity measures. It also identifies folds which appear inhomogeneous in that they contain apparently dissimilar domains, as measured by both similarity measures. At a low (1%) false positive rate, 25 to 38% of domain pairs in the same SCOP folds do not appear similar. Our results suggest either that some of these folds are defined using criteria other than purely structural consideration or that the similarity measures used do not recognize some relevant aspects of structural similarity in certain cases. Specifically, variations of the "common core" of some folds are severe enough to defeat attempts to automatically detect structural similarity and/or to lead to false detection of similarity between domains in distinct folds. Structures in some folds vary greatly in size because they contain varying numbers of a repeating unit, while similarity scores are quite sensitive to size differences. Structures in different folds may contain similar substructures, which produce false positives. Finally, the common core within a structure may be too small relative to the entire structure, to be recognized as the basis of similarity to another.

Conclusion

A detailed analysis of the entire available protein fold space by two automated similarity methods reveals the extent and the nature of the divergence between the automatically determined similarity/dissimilarity and the manual fold type classifications. Some of the observed divergences can probably be addressed with better structure comparison methods and better automatic, intelligent classification procedures. Others may be intrinsic to the problem, suggesting a continuous rather than discrete protein fold space.