A robust method to detect structural and functional remote homologues

Proteins
Ori Shachar, M Linial

Abstract

With currently available sequence data, it is feasible to conduct extensive comparisons among large sets of protein sequences. It is still a much more challenging task to partition the protein space into structurally and functionally related families solely based on sequence comparisons. The ProtoNet system automatically generates a treelike classification of the whole protein space. It stands to reason that this classification reflects evolutionary relationships, both close and remote. In this article, we examine this hypothesis. We present a semiautomatic procedure that singles out certain inner nodes in the ProtoNet tree that should ideally correspond to structurally and functionally defined protein families. We compare the performance of this method against several expert systems. Some of the competing methods incorporate additional extraneous information on protein structure or on enzymatic activities. The ProtoNet-based method performs at least as well as any of the methods with which it was compared. This article illustrates the ProtoNet-based method on several evolutionarily diverse families. Using this new method, an evolutionary divergence scheme can be proposed for a large number of structural and functional related ...Continue Reading

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Citations

Nov 13, 2004·Bioinformatics·Ilona KiferMichal Linial
Dec 21, 2004·Nucleic Acids Research·Noam KaplanMichal Linial
Dec 15, 2004·BMC Bioinformatics·Noam KaplanMichal Linial
Feb 14, 2006·Proteins·Iris Bahir, Michal Linial
May 5, 2006·Protein Science : a Publication of the Protein Society·Ori SassonMichal Linial
Aug 17, 2011·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Ulavappa B Angadi, M Venkatesulu
Jan 13, 2006·Trends in Pharmacological Sciences·Ora Schueler-FurmanMichal Linial

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