Predicting fold novelty based on ProtoNet hierarchical classification

Ilona Kifer, Ori Sasson, Michal Linial*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageAmerican English
Pages (from-to)1020-1027
Number of pages8
Issue number7
StatePublished - 1 Apr 2005

Bibliographical note

Funding Information:
We thank Nati Linial and Elon Portugaly for their valuable suggestions, ideas and fruitful discussions throughout this study. The authors wish to thank the outstanding ProtoNet team and Alex Savenok for ProTarget web design. This study was partially supported by the CESG consortium (NIMSG, NIH) and the European SPINE consortium. I.K. is a fellow student of SCCB—The Sudarsky Center for Computational Biology in the Hebrew University of Jerusalem.


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