title: A functional hierarchical organization of the protein sequence space.
abstract: BACKGROUND
  It is a major challenge of computational biology to provide a comprehensive functional classification of all known proteins. Most existing methods seek recurrent patterns in known proteins based on manually-validated alignments of known protein families. Such methods can achieve high sensitivity, but are limited by the necessary manual labor. This makes our current view of the protein world incomplete and biased. This paper concerns ProtoNet, a automatic unsupervised global clustering system that generates a hierarchical tree of over 1,000,000 proteins, based solely on sequence similarity.
  
  
  RESULTS
  In this paper we show that ProtoNet correctly captures functional and structural aspects of the protein world. Furthermore, a novel feature is an automatic procedure that reduces the tree to 12% its original size. This procedure utilizes only parameters intrinsic to the clustering process. Despite the substantial reduction in size, the system's predictive power concerning biological functions is hardly affected. We then carry out an automatic comparison with existing functional protein annotations. Consequently, 78% of the clusters in the compressed tree (5,300 clusters) get assigned a biological function with a high confidence. The clustering and compression processes are unsupervised, and robust.
  
  
  CONCLUSIONS
  We present an automatically generated unbiased method that provides a hierarchical classification of all currently known proteins.
authors.affiliation: Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel. kaplann@cc.huji.ac.il
authors.forename: Noam
authors.initials: N
authors.lastname: Kaplan
authors.forename: Moriah
authors.initials: M
authors.lastname: Friedlich
authors.forename: Menachem
authors.initials: M
authors.lastname: Fromer
authors.forename: Michal
authors.initials: M
authors.lastname: Linial
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001331:Automation; D002412:Cations; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D016247:Information Storage and Retrieval; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D040681:Structural Homology, Protein
chemical_list: D002412:Cations; D011506:Proteins