title: Internal organization of large protein families: relationship between the sequence, structure, and function-based clustering.
abstract: The protein universe can be organized in families that group proteins sharing common ancestry. Such families display variable levels of structural and functional divergence, from homogenous families, where all members have the same function and very similar structure, to very divergent families, where large variations in function and structure are observed. For practical purposes of structure and function prediction, it would be beneficial to identify sub-groups of proteins with highly similar structures (iso-structural) and/or functions (iso-functional) within divergent protein families. We compared three algorithms in their ability to cluster large protein families and discuss whether any of these methods could reliably identify such iso-structural or iso-functional groups. We show that clustering using profile-sequence and profile-profile comparison methods closely reproduces clusters based on similarities between 3D structures or clusters of proteins with similar biological functions. In contrast, the still commonly used sequence-based methods with fixed thresholds result in vast overestimates of structural and functional diversity in protein families. As a result, these methods also overestimate the number of protein structures that have to be determined to fully characterize structural space of such families. The fact that one can build reliable models based on apparently distantly related templates is crucial for extracting maximal amount of information from new sequencing projects.
authors.affiliation: Joint Center for Structural Genomics, Center for Research in Biological Systems, University of California, San Diego, California 92093-0446, USA.
authors.forename: Xiao-Hui
authors.initials: XH
authors.lastname: Cai
authors.forename: Lukasz
authors.initials: L
authors.lastname: Jaroszewski
authors.forename: John
authors.initials: J
authors.lastname: Wooley
authors.forename: Adam
authors.initials: A
authors.lastname: Godzik
mesh_terms: D016000:Cluster Analysis; D030562:Databases, Protein; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins