ADEPTs: information necessary for subcellular distribution of eukaryotic sorting isozymes resides in domains missing from eubacterial and archaeal counterparts

Sorting isozymes are encoded by single genes, but the encoded proteins are distributed to multiple subcellular compartments. We surveyed the predicted protein sequences of several nucleic acid interacting sorting isozymes from the eukaryotic taxonomic domain and compared them with their homologs in the archaeal and eubacterial domains. Here, we summarize the data showing that the eukaryotic sorting isozymes often possess sequences not present in the archaeal and eubacterial counterparts and that the additional sequences can act to target the eukaryotic proteins to their appropriate subcellular locations. Therefore, we have named these protein domains ADEPTs (Additional Domains for Eukaryotic Protein Targeting). Identification of additional domains by phylogenetic comparisons should be generally useful for locating candidate sequences important for subcellular distribution of eukaryotic proteins.     

Detecting distant homologs using phylogenetic tree-based HMMs

It is often desired to identify further homologs of a family of biological sequences from the ever-growing sequence databases. Profile hidden Markov models excel at capturing the common statistical features of a group of biological sequences. With these common features, we can search the biological database and find new homologous sequences. Most general profile hidden Markov model methods, however, treat the evolutionary relationships between the sequences in a homologous group in an ad-hoc manner. We hereby introduce a method to incorporate phylogenetic information directly into hidden Markov models, and demonstrate that the resulting model performs better than most of the current multiple sequence-based methods for finding distant homologs.     

The C terminus of the nuclear protein NuMA: phylogenetic distribution and structure

The C terminus of the nuclear protein NuMA, NuMA-CT, has a well-known function in mitosis via its proximal segment, but it seems also involved in the control of differentiation. To further investigate the structure and function of NuMA, we exploited established computational techniques and tools to collate and characterize proteins with regions similar to the distal portion of NuMA-CT (NuMA-CTDP). The phylogenetic distribution of NuMA-CTDP was examined by PSI-BLAST- and TBLASTN-based analysis of genome and protein sequence databases. Proteins and open reading frames with a NuMA-CTDP-like region were found in a diverse set of vertebrate species including mammals, birds, amphibia, and early teleost fish. The potential structure of NuMA-CTDP was investigated by searching a database of protein sequences of known three-dimensional structure with a hidden Markov model (HMM) estimated using representative (human, frog, chicken, and pufferfish) sequences. The two highest scoring sequences that aligned to the HMM were the extracellular domains of beta3-integrin and Her2, suggesting that NuMA-CTDP may have a primarily beta fold structure. These data indicate that NuMA-CTDP may represent an important functional sequence conserved in vertebrates, where it may act as a receptor to coordinate cellular events.     

Predicted function of the vaccinia virus G5R protein

MOTIVATION: Of the approximately 200 proteins that have been identified for the vaccinia virus (VACV) genome, many are currently listed as having an unknown function, and seven of these are also found in all other poxvirus genomes that have been sequenced. The G5R protein of VACV is included in this list, and to date, very little is known about this essential and highly conserved protein. Conventional similarity searches of protein databases do not identify significantly similar proteins, and experimental approaches have been unsuccessful at determining protein function. RESULTS: Using HHsearch, a hidden Markov model (HMM) comparison search tool, the G5R protein was found to be similar to both human and archaeal flap endonucleases (FEN-1) with 96% probability. The G5R protein structure was subsequently successfully modeled using the Robetta protein structure prediction server with an archaeal FEN-1 as the template. The G5R model was then compared to the human FEN-1 crystal structure and was found to be structurally similar to human FEN-1 in both active site residues and DNA substrate binding regions.     

Using protein structural information in evolutionary inference: transmembrane proteins

We present a model of amino acid sequence evolution based on a hidden Markov model that extends to transmembrane proteins previous methods that incorporate protein structural information into phylogenetics. Our model aims to give a better understanding of processes of molecular evolution and to extract structural information from multiple alignments of transmembrane sequences and use such information to improve phylogenetic analyses. This should be of value in phylogenetic studies of transmembrane proteins: for example, mitochondrial proteins have acquired a special importance in phylogenetics and are mostly transmembrane proteins. The improvement in fit to example data sets of our new model relative to less complex models of amino acid sequence evolution is statistically tested. To further illustrate the potential utility of our method, phylogeny estimation is performed on primate CCR5 receptor sequences, sequences of l and m subunits of the light reaction center in purple bacteria, guinea pig sequences with respect to lagomorph and rodent sequences of calcitonin receptor and K-substance receptor, and cetacean sequences of cytochrome b.     

Uncovering the human methyltransferasome

We present a comprehensive analysis of the human methyltransferasome. Primary sequences, predicted secondary structures, and solved crystal structures of known methyltransferases were analyzed by hidden Markov models, Fisher-based statistical matrices, and fold recognition prediction-based threading algorithms to create a model, or profile, of each methyltransferase superfamily. These profiles were used to scan the human proteome database and detect novel methyltransferases. 208 proteins in the human genome are now identified as known or putative methyltransferases, including 38 proteins that were not annotated previously. To date, 30% of these proteins have been linked to disease states. Possible substrates of methylation for all of the SET domain and SPOUT methyltransferases as well as 100 of the 131 seven-β-strand methyltransferases were surmised from sequence similarity clusters based on alignments of the substrate-specific domains.     

Members of the immunoglobulin superfamily in bacteria.

We report a prediction that two prokaryotic proteins contain immunoglobulin superfamily domains. Immunoglobulin-like folds have been identified previously in prokaryotic proteins, but these share no recognizable sequence similarity with eukaryotic immunoglobulin superfamily (IgSF) folds, and may be the result of the physics and chemistry of proteins favoring certain common folds. In contrast, the prokaryotic proteins identified have sequences whose match to the immunoglobulin superfamily can be detected by hidden Markov modeling, BLASTP matches, key residue analysis, and secondary structure predictions. We propose that these prokaryotic immunoglobulin-like domains are almost certain to be related by divergence from a common ancestor to eukaryotic immunoglobulin superfamily domains.     

Depicting a protein's two faces: GPCR classification by phylogenetic tree-based HMMs

Related proteins with similar biological functions generally share common features, allowing us to extract the common sequence features. These common features enable us to build statistical models that can be used to classify proteins, to predict new members, and to study the sequence-function relationship of this protein function group. Although evolution underlies the basis of multiple sequence analysis methods, most methods ignore phylogenetic relationships and the evolutionary process in building these statistical models. Previously we have shown that a phylogenetic tree-based profile hidden Markov model (T-HMM) is superior in generating a profile for a group of similar proteins. In this study we used the method to generate common features of G protein-coupled receptors (GPCRs). The profile generated by T-HMM gives high accuracy in GPCR function classification, both by ligand and by coupled G protein.     

Functional analysis of the translation factor aIF2/5B in the thermophilic archaeon Sulfolobus solfataricus

The protein IF2/eIF5B is one of the few translation initiation factors shared by all three primary domains of life (bacteria, archaea, eukarya). Despite its phylogenetic conservation, the factor is known to present marked functional divergences in the bacteria and the eukarya. In this work, the function in translation of the archaeal homologue (aIF2/5B) has been analysed in detail for the first time using a variety of in vitro assays. The results revealed that the protein is a ribosome-dependent GTPase which strongly stimulates the binding of initiator tRNA to the ribosomes even in the absence of other factors. In agreement with this finding, aIF2/5B enhances the translation of both leadered and leaderless mRNAs when expressed in a cell-free protein-synthesizing system. Moreover, the degree of functional conservation of the IF2-like factors in the archaeal and bacterial lineages was investigated by analysing the behaviour of 'chimeric' proteins produced by swapping domains between the Sulfolobus solfataricus aIF2/5B factor and the IF2 protein of the thermophilic bacterium Bacillus stearothermophilus. Beside evidencing similarities and differences between the archaeal and bacterial factors, these experiments have provided insight into the common role played by the IF2/5B proteins in all extant cells.