A functional hierarchical organization of the protein sequence space

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 b...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Noam Kaplan, Moriah Friedlich, Menachem Fromer, Michal Linial
• Keywords: BMCBI 2004, Comprehensive Functional Classification, Protein, Protein World