title: T-RMSD: a fine-grained, structure-based classification method and its application to the functional characterization of TNF receptors.
abstract: 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.
authors.affiliation: EMBL/CRG Systems Biology Research Unit Center for Genomic Regulation, UPF, Barcelona, Catalunya, 08003, Spain. cedrik.magis@crg.es
authors.forename: Cedrik
authors.initials: C
authors.lastname: Magis
authors.forename: François
authors.initials: F
authors.lastname: Stricher
authors.forename: Almer M
authors.initials: AM
authors.lastname: van der Sloot
authors.forename: Luis
authors.initials: L
authors.lastname: Serrano
authors.forename: Cedric
authors.initials: C
authors.lastname: Notredame
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020559:Monomeric GTP-Binding Proteins / Q000737:chemistry / Q000145:classification / Q000378:metabolism; D017434:Protein Structure, Tertiary; D018124:Receptors, Tumor Necrosis Factor / Q000737:chemistry* / Q000145:classification* / Q000276:immunology
chemical_list: D018124:Receptors, Tumor Necrosis Factor; D020559:Monomeric GTP-Binding Proteins