Journal
JOURNAL OF MOLECULAR BIOLOGY
Volume 400, Issue 3, Pages 605-617Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jmb.2010.05.012
Keywords
structural classification; functional classification; multiple sequence alignment; Tumor Necrosis Factor Receptor; Cysteine-rich Domain
Categories
Funding
- European Union (EU) [LSHC-CT-2006-037686]
- 3D REPERTOIRE [LSHG-CT-2005-512028]
- Spanish Ministry of Education and Science
- CRG
- Plan Nacional [BFU 00419]
- ICREA Funding Source: Custom
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This study addresses the relation between structural and functional similarity in proteins. We introduce a novel method named tree based on root mean square deviation (T-RMSD), which uses distance RMSD (dRMSD) variations to build fine-grained structure-based classifications of proteins. The main improvement of the T-RMSD over similar methods, such as Dali, is its capacity to produce the equivalent of a bootstrap value for each cluster node. We validated our approach on two domain families studied extensively for their role in many biological and pathological pathways: the small GTPase RAS superfamily and the cysteine-rich domains (CRDs) associated with the tumor necrosis factor receptors (TNFRs) family. Our analysis showed that T-RMSD is able to automatically recover and refine existing classifications. In the case of the small GTPase ARF subfamily, T-RMSD can distinguish GTP- from GDP-bound states, while in the case of CRDs it can identify two new subgroups associated with well defined functional features (ligand binding and formation of ligand pre-assembly complex). We show how hidden Markov models (HMMs) can be built on these new groups and propose a methodology to use these models simultaneously in order to do fine-grained functional genomic annotation without known 3D structures. T-RMSD, an open source freeware incorporated in the T-Coffee package, is available online. (C) 2010 Elsevier Ltd. All rights reserved.
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