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Bioinformatics. 2005 Apr 1;21(7):1020-7. Epub 2004 Nov 11.

Predicting fold novelty based on ProtoNet hierarchical classification.

Author information

1
Department of Biological Chemistry, Institute of Life Sciences Jerusalem 91904, Israel.

Abstract

MOTIVATION:

Structural genomics projects aim to solve a large number of protein structures with the ultimate objective of representing the entire protein space. The computational challenge is to identify and prioritize a small set of proteins with new, currently unknown, superfamilies or folds.

RESULTS:

We develop a method that assigns each protein a likelihood of it belonging to a new, yet undetermined, structural superfamily. The method relies on a variant of ProtoNet, an automatic hierarchical classification scheme of all protein sequences from SwissProt. Our results show that proteins that are remote from solved structures in the ProtoNet hierarchy are more likely to belong to new superfamilies. The results are validated against SCOP releases from recent years that account for about half of the solved structures known to date. We show that our new method and the representation of ProtoNet are superior in detecting new targets, compared to our previous method using ProtoMap classification. Furthermore, our method outperforms PSI-BLAST search in detecting potential new superfamilies.

PMID:
15539447
DOI:
10.1093/bioinformatics/bti135
[Indexed for MEDLINE]

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