Predicting Fold Novelty Based on ProtoNet Hierarchical Classification

Publication Type

Journal Article

Publication Date

4-2005

Abstract

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. 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.

Discipline

Bioinformatics | Computer Sciences

Publication

Bioinformatics

Volume

21

Issue

7

First Page

1020

Last Page

1027

ISSN

1367-4803

Identifier

10.1093/bioinformatics/bti135

Publisher

Oxford

Additional URL

http://dx.doi.org/10.1093/bioinformatics/bti135

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