A robust method to detect structural and functional remote homologues.
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.
Shachar
Ori
O
School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel.
Linial
Michal
M
D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D015203:Reproducibility of Results; D012984:Software; D040681:Structural Homology, Protein; D013329:Structure-Activity Relationship
D011506:Proteins