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Multiple new prokaryotic C-terminal protein-sorting signals were found that reprise the tripartite architecture shared by LPXTG and PEP-CTERM: motif, TM helix, basic cluster. Defining hidden Markov models were constructed for all. PGF-CTERM occurs in 29 archaeal species, some of which have more than 50 proteins that share the domain. PGF-CTERM proteins include the major cell surface protein in Halobacterium, a glycoprotein with a partially characterized diphytanylglyceryl phosphate linkage near its C terminus. Comparative genomics identifies a distant exosortase homolog, designated archaeosortase A (ArtA), as the likely protein-processing enzyme for PGF-CTERM. Proteomics suggests that the PGF-CTERM region is removed. Additional systems include VPXXXP-CTERM/archeaosortase B in two of the same archaea and PEF-CTERM/archaeosortase C in four others. Bacterial exosortases often fall into subfamilies that partner with very different cohorts of extracellular polymeric substance biosynthesis proteins; several species have multiple systems. Variant systems include the VPDSG-CTERM/exosortase C system unique to certain members of the phylum Verrucomicrobia, VPLPA-CTERM/exosortase D in several alpha- and deltaproteobacterial species, and a dedicated (single-target) VPEID-CTERM/exosortase E system in alphaproteobacteria. Exosortase-related families XrtF in the class Flavobacteria and XrtG in Gram-positive bacteria mark distinctive conserved gene neighborhoods. A picture emerges of an ancient and now well-differentiated superfamily of deeply membrane-embedded protein-processing enzymes. Their target proteins are destined to transit cellular membranes during their biosynthesis, during which most undergo additional posttranslational modifications such as glycosylation.

Background: Phylogenetic profiling is a technique of scoring co-occurrence between a protein family and some other trait, usually another protein family, across a set of taxonomic groups. In spite of several refinements in recent years, the technique still invites significant improvement. To be its most effective, a phylogenetic profiling algorithm must be able to examine co-occurrences among protein families whose boundaries are uncertain within large homologous protein superfamilies., Results: Partial Phylogenetic Profiling (PPP) is an iterative algorithm that scores a given taxonomic profile against the taxonomic distribution of families for all proteins in a genome. The method works through optimizing the boundary of each protein family, rather than by relying on prebuilt protein families or fixed sequence similarity thresholds. Double Partial Phylogenetic Profiling (DPPP) is a related procedure that begins with a single sequence and searches for optimal granularities for its surrounding protein family in order to generate the best query profiles for PPP. We present ProPhylo, a high-performance software package for phylogenetic profiling studies through creating individually optimized protein family boundaries. ProPhylo provides precomputed databases for immediate use and tools for manipulating the taxonomic profiles used as queries., Conclusion: ProPhylo results show universal markers of methanogenesis, a new DNA phosphorothioation-dependent restriction enzyme, and efficacy in guiding protein family construction. The software and the associated databases are freely available under the open source Perl Artistic License from ftp://ftp.jcvi.org/pub/data/ppp/.

TIGRFAMs is a collection of protein family definitions built to aid in high-throughput annotation of specific protein functions. Each family is based on a hidden Markov model (HMM), where both cutoff scores and membership in the seed alignment are chosen so that the HMMs can classify numerous proteins according to their specific molecular functions. Most TIGRFAMs models describe 'equivalog' families, where both orthology and lateral gene transfer may be part of the evolutionary history, but where a single molecular function has been conserved. The Genome Properties system contains a queriable set of metabolic reconstructions, genome metrics and extractions of information from the scientific literature. Its genome-by-genome assertions of whether or not specific structures, pathways or systems are present provide high-level conceptual descriptions of genomic content. These assertions enable comparative genomics, provide a meaningful biological context to aid in manual annotation, support assignments of Gene Ontology (GO) biological process terms and help validate HMM-based predictions of protein function. The Genome Properties system is particularly useful as a generator of phylogenetic profiles, through which new protein family functions may be discovered. The TIGRFAMs and Genome Properties systems can be accessed at http://www.tigr.org/TIGRFAMs and http://www.tigr.org/Genome_Properties.