title: Defining the fold space of membrane proteins: the CAMPS database.
abstract: Recent progress in structure determination techniques has led to a significant growth in the number of known membrane protein structures, and the first structural genomics projects focusing on membrane proteins have been initiated, warranting an investigation of appropriate bioinformatics strategies for optimal structural target selection for these molecules. What determines a membrane protein fold? How many membrane structures need to be solved to provide sufficient structural coverage of the membrane protein sequence space? We present the CAMPS database (Computational Analysis of the Membrane Protein Space) containing almost 45,000 proteins with three or more predicted transmembrane helices (TMH) from 120 bacterial species. This large set of membrane proteins was subjected to single-linkage clustering using only sequence alignments covering at least 40% of the TMH present in a given family. This process yielded 266 sequence clusters with at least 15 members, roughly corresponding to membrane structural folds, sufficiently structurally homogeneous in terms of the variation of TMH number between individual sequences. These clusters were further subdivided into functionally homogeneous subclusters according to the COG (Clusters of Orthologous Groups) system as well as more stringently defined families sharing at least 30% identity. The CAMPS sequence clusters are thus designed to reflect three main levels of interest for structural genomics: fold, function, and modeling distance. We present a library of Hidden Markov Models (HMM) derived from sequence alignments of TMH at these three levels of sequence similarity. Given that 24 out of 266 clusters corresponding to membrane folds already have associated known structures, we estimate that 242 additional new structures, one for each remaining cluster, would provide structural coverage at the fold level of roughly 70% of prokaryotic membrane proteins belonging to the currently most populated families.
authors.affiliation: Department of Genome Oriented Bioinformatics, Technische Universität München, Freising, Germany.
authors.forename: Antonio J
authors.initials: AJ
authors.lastname: Martin-Galiano
authors.forename: Dmitrij
authors.initials: D
authors.lastname: Frishman
mesh_terms: D001426:Bacterial Proteins / Q000737:chemistry / Q000145:classification; D030562:Databases, Protein; D023281:Genomics; D008390:Markov Chains; D008565:Membrane Proteins / Q000737:chemistry* / Q000145:classification; D017510:Protein Folding; D017433:Protein Structure, Secondary; D016415:Sequence Alignment
chemical_list: D001426:Bacterial Proteins; D008565:Membrane Proteins