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A robust method to detect structural and functional remote homologues
Author(s) -
Shachar Ori,
Linial Michal
Publication year - 2004
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.20235
Subject(s) - partition (number theory) , computer science , divergence (linguistics) , sequence (biology) , artificial intelligence , tree (set theory) , task (project management) , space (punctuation) , structural classification of proteins database , machine learning , data mining , computational biology , protein structure , biology , mathematics , genetics , engineering , mathematical analysis , philosophy , linguistics , biochemistry , systems engineering , combinatorics , operating system
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 superfamilies. Proteins 2004. © 2004 Wiley‐Liss, Inc.