Global self-organization of all known protein sequences reveals inherent biological signatures

Journal of Molecular Biology
M LinialG Yona

Abstract

A global classification of all currently known protein sequences is performed. Every protein sequence is partitioned into segments of 50 amino acid residues and a dynamic programming distance is calculated between each pair of segments. This space of segments is initially embedded into Euclidean space. The algorithm that we apply embeds every finite metric space into Euclidean space so that (1) the dimension of the host space is small, (2) the metric distortion is small. A novel self-organized, cross-validated clustering algorithm is then applied to the embedded space with Euclidean distances. We monitor the validity of our clustering by randomly splitting the data into two parts and performing an hierarchical clustering algorithm independently on each part. At every level of the hierarchy we cross-validate the clusters in one part with the clusters in the other. The resulting hierarchical tree of clusters offers a new representation of protein sequences and families, which compares favorably with the most updated classifications based on functional and structural data about proteins. Some of the known families clustered into well distinct clusters. Motifs and domains such as the zinc finger, EF hand, homeobox, EGF-like and oth...Continue Reading

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Citations

Jul 13, 2000·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·T JaakkolaD Haussler
May 23, 2002·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Jeremy Buhler, Martin Tompa
Apr 14, 2009·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Aleksandar Stojmirović, Yi-Kuo Yu
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Apr 23, 2004·Proteins·Jinfeng Liu, Burkhard Rost
Nov 18, 2003·Journal of Biomolecular Structure & Dynamics·Igor N BerezovskyEdward N Trifonov

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