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Open Access Research article

Towards an automatic classification of protein structural domains based on structural similarity

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 Laboratoire de Mathematique Informatique et Genome, INRA, Jouy-en-Josas, France

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Citation and License

BMC Bioinformatics 2008, 9:74  doi:10.1186/1471-2105-9-74

Published: 31 January 2008

Abstract

Background

Formal classification of a large collection of protein structures aids the understanding of evolutionary relationships among them. Classifications involving manual steps, such as SCOP and CATH, face the challenge of increasing volume of available structures. Automatic methods such as FSSP or Dali Domain Dictionary, yield divergent classifications, for reasons not yet fully investigated. One possible reason is that the pairwise similarity scores used in automatic classification do not adequately reflect the judgments made in manual classification. Another possibility is the difference between manual and automatic classification procedures. We explore the degree to which these two factors might affect the final classification.

Results

We use DALI, SHEBA and VAST pairwise scores on the SCOP C class domains, to investigate a variety of hierarchical clustering procedures. The constructed dendrogram is cut in a variety of ways to produce a partition, which is compared to the SCOP fold classification.

Ward's method dendrograms led to partitions closest to the SCOP fold classification. Dendrogram- or tree-cutting strategies fell into four categories according to the similarity of resulting partitions to the SCOP fold partition. Two strategies which optimize similarity to SCOP, gave an average of 72% true positives rate (TPR), at a 1% false positive rate. Cutting the largest size cluster at each step gave an average of 61% TPR which was one of the best strategies not making use of prior knowledge of SCOP. Cutting the longest branch at each step produced one of the worst strategies.

We also developed a method to detect irreducible differences between the best possible automatic partitions and SCOP, regardless of the cutting strategy. These differences are substantial. Visual examination of hard-to-classify proteins confirms our previous finding, that global structural similarity of domains is not the only criterion used in the SCOP classification.

Conclusion

Different clustering procedures give rise to different levels of agreement between automatic and manual protein classifications. None of the tested procedures completely eliminates the divergence between automatic and manual protein classifications. Achieving full agreement between these two approaches would apparently require additional information.