Cache Domains That are Homologous to, but Different from PAS Domains Comprise the Largest Superfamily of Extracellular Sensors in Prokaryotes.

Cellular receptors usually contain a designated sensory domain that recognizes the signal. Per/Arnt/Sim (PAS) domains are ubiquitous sensors in thousands of species ranging from bacteria to humans. Although PAS domains were described as intracellular sensors, recent structural studies revealed PAS-like domains in extracytoplasmic regions in several transmembrane receptors. However, these structurally defined extracellular PAS-like domains do not match sequence-derived PAS domain models, and thus their distribution across the genomic landscape remains largely unknown. Here we show that structurally defined extracellular PAS-like domains belong to the Cache superfamily, which is homologous to, but distinct from the PAS superfamily. Our newly built computational models enabled identification of Cache domains in tens of thousands of signal transduction proteins including those from important pathogens and model organisms. Furthermore, we show that Cache domains comprise the dominant mode of extracellular sensing in prokaryotes.

Phylogenetic Analysis Supports a Link between DUF1220 Domain Number and Primate Brain Expansion.

The expansion of DUF1220 domain copy number during human evolution is a dramatic example of rapid and repeated domain duplication. Although patterns of expression, homology, and disease associations suggest a role in cortical development, this hypothesis has not been robustly tested using phylogenetic methods. Here, we estimate DUF1220 domain counts across 12 primate genomes using a nucleotide Hidden Markov Model. We then test a series of hypotheses designed to examine the potential evolutionary significance of DUF1220 copy number expansion. Our results suggest a robust association with brain size, and more specifically neocortex volume. In contradiction to previous hypotheses, we find a strong association with postnatal brain development but not with prenatal brain development. Our results provide further evidence of a conserved association between specific loci and brain size across primates, suggesting that human brain evolution may have occurred through a continuation of existing processes.

A Bayesian sampler for optimization of protein domain hierarchies.

The process of identifying and modeling functionally divergent subgroups for a specific protein domain class and arranging these subgroups hierarchically has, thus far, largely been done via manual curation. How to accomplish this automatically and optimally is an unsolved statistical and algorithmic problem that is addressed here via Markov chain Monte Carlo sampling. Taking as input a (typically very large) multiple-sequence alignment, the sampler creates and optimizes a hierarchy by adding and deleting leaf nodes, by moving nodes and subtrees up and down the hierarchy, by inserting or deleting internal nodes, and by redefining the sequences and conserved patterns associated with each node. All such operations are based on a probability distribution that models the conserved and divergent patterns defining each subgroup. When we view these patterns as sequence determinants of protein function, each node or subtree in such a hierarchy corresponds to a subgroup of sequences with similar biological properties. The sampler can be applied either de novo or to an existing hierarchy. When applied to 60 protein domains from multiple starting points in this way, it converged on similar solutions with nearly identical log-likelihood ratio scores, suggesting that it typically finds the optimal peak in the posterior probability distribution. Similarities and differences between independently generated, nearly optimal hierarchies for a given domain help distinguish robust from statistically uncertain features. Thus, a future application of the sampler is to provide confidence measures for various features of a domain hierarchy.

MetaDomain: a profile HMM-based protein domain classification tool for short sequences.

Protein homology search provides basis for functional profiling in metagenomic annotation. Profile HMM-based methods classify reads into annotated protein domain families and can achieve better sensitivity for remote protein homology search than pairwise sequence alignment. However, their sensitivity deteriorates with the decrease of read length. As a result, a large number of short reads cannot be classified into their native domain families. In this work, we introduce MetaDomain, a protein domain classification tool designed for short reads generated by next-generation sequencing technologies. MetaDomain uses relaxed position-specific score thresholds to align more reads to a profile HMM while using the distribution of alignment positions as an additional constraint to control false positive matches. In this work MetaDomain is applied to the transcriptomic data of a bacterial genome and a soil metagenomic data set. The experimental results show that it can achieve better sensitivity than the state-of-the-art profile HMM alignment tool in identifying encoded domains from short sequences. The source codes of MetaDomain are available at http://sourceforge.net/projects/metadomain/.

Assessment of protein domain fusions in human protein interaction networks prediction: application to the human kinetochore model.

In order to understand how biological systems function it is necessary to determine the interactions and associations between proteins. Some proteins, involved in a common biological process and encoded by separate genes in one organism, can be found fused within a single protein chain in other organisms. By detecting these triplets, a functional relationship can be established between the unfused proteins. Here we use a domain fusion prediction method to predict these protein interactions for the human interactome. We observed that gene fusion events are more related to physical interaction between proteins than to other weaker functional relationships such as participation in a common biological pathway. These results suggest that domain fusion is an appropriate method for predicting protein complexes. The most reliable fused domain predictions were used to build protein-protein interaction (PPI) networks. These predicted PPI network models showed the same topological features as real biological networks and different features from random behaviour. We built the PPI domain fusion sub-network model of the human kinetochore and observed that the majority of the predicted interactions have not yet been experimentally characterised in the publicly available PPI repositories. The study of the human kinetochore domain fusion sub-network reveals undiscovered kinetochore proteins with presumably relevant functions, such as hubs with many connections in the kinetochore sub-network. These results suggest that experimentally hidden regions in the predicted PPI networks contain key functional elements, associated with important functional areas, still undiscovered in the human interactome. Until novel experiments shed light on these hidden regions; domain fusion predictions provide a valuable approach for exploring them.

Characterization of W1745C and S1783A: 2 novel mutations causing defective collagen binding in the A3 domain of von Willebrand factor.

Investigation of 3 families with bleeding symptoms demonstrated a defect in the collagen-binding activity of von Willebrand factor (VWF) in association with a normal VWF multimeric pattern. Genetic analysis showed affected persons to be heterozygous for mutations in the A3 domain of VWF: S1731T, W1745C, and S1783A. One person showed compound heterozygosity for W1745C and R760H. W1745C and S1783A have not been reported previously. The mutations were reproduced by site-directed mutagenesis and mutant VWF expressed in HEK293T cells. Collagen-binding activity measured by immunosorbent assay varied according to collagen type: W1745C and S1783A were associated with a pronounced binding defect to both type I and type III collagen, whereas the principal abnormality in S1731T patients was a reduction in binding to type I collagen only. The multimer pattern and distribution of mutant proteins were indistinguishable from wild-type recombinant VWF, confirming that the defect in collagen binding resulted from the loss of affinity at the binding site and not impairment of high-molecular-weight multimer formation. Our findings demonstrate that mutations causing an abnormality in the binding of VWF to collagen may contribute to clinically significant bleeding symptoms. We propose that isolated collagen-binding defects are classified as a distinct subtype of von Willebrand disease.

Functional assessment of perforin C2 domain mutations illustrates the critical role for calcium-dependent lipid binding in perforin cytotoxic function.

Perforin-mediated lymphocyte cytotoxicity is critical for pathogen elimination and immune homeostasis. Perforin disruption of target cell membranes is hypothesized to require binding of a calcium-dependent, lipid-inserting, C2 domain. In a family affected by hemophagocytic lymphohistiocytosis, a severe inflammatory disorder caused by perforin deficiency, we identified 2 amino acid substitutions in the perforin C2 domain: T435M, a previously identified mutant with disputed pathogenicity, and Y438C, a novel substitution. Using biophysical modeling, we predicted that the T435M substitution, but not Y438C, would interfere with calcium binding and thus cytotoxic function. The capacity for cytotoxic function was tested after expression of the variant perforins in rat basophilic leukemia cells and murine cytotoxic T lymphocytes. As predicted, cells transduced with perforin-T435M lacked cytotoxicity, but those expressing perforin-Y438C displayed intact cytotoxic function. Using novel antibody-capture and liposome-binding assays, we found that both mutant perforins were secreted; however, only nonmutated and Y438C-substituted perforins were capable of calcium-dependent lipid binding. In addition, we found that perforin-Y438C was capable of mediating cytotoxicity without apparent proteolytic maturation. This study clearly demonstrates the pathogenicity of the T435M mutation and illustrates, for the first time, the critical role of the human perforin C2 domain for calcium-dependent, cytotoxic function.

Assessment of substrate-stabilizing factors for DnaK on the folding of aggregation-prone proteins.

Hydrophobic interactions between molecular chaperones and their nonnative substrates have been believed to be mainly responsible for both substrate recognition and stabilization against aggregation. However, the hydrophobic contact area between DnaK and its substrate proteins is very limited and other factors of DnaK for the substrate stabilization could not be excluded. Here, we covalently fused DnaK to the N-termini of aggregation-prone proteins in vivo. In the context of a fusion protein, DnaK has the ability to efficiently solubilize its linked proteins. The point mutation of the residue of DnaK critical for the substrate recognition and the deletion of the C-terminal substrate-binding domain did not have significant effect on the solubilizing ability of DnaK. The results imply that other factors of DnaK, distinct from the hydrophobic shielding of folding intermediates, also contributes to stabilization of its noncovalently bound substrates against aggregation. Elucidation of the nature of these factors would further enhance our understanding of the substrate stabilization of DnaK for expedited protein folding.

Estimation of amino acid residue substitution rates at local spatial regions and application in protein function inference: a Bayesian Monte Carlo approach.

The amino acid sequences of proteins provide rich information for inferring distant phylogenetic relationships and for predicting protein functions. Estimating the rate matrix of residue substitutions from amino acid sequences is also important because the rate matrix can be used to develop scoring matrices for sequence alignment. Here we use a continuous time Markov process to model the substitution rates of residues and develop a Bayesian Markov chain Monte Carlo method for rate estimation. We validate our method using simulated artificial protein sequences. Because different local regions such as binding surfaces and the protein interior core experience different selection pressures due to functional or stability constraints, we use our method to estimate the substitution rates of local regions. Our results show that the substitution rates are very different for residues in the buried core and residues on the solvent-exposed surfaces. In addition, the rest of the proteins on the binding surfaces also have very different substitution rates from residues. Based on these findings, we further develop a method for protein function prediction by surface matching using scoring matrices derived from estimated substitution rates for residues located on the binding surfaces. We show with examples that our method is effective in identifying functionally related proteins that have overall low sequence identity, a task known to be very challenging.

An evolutionary space-time model with varying among-site dependencies.

It is now widely accepted that sites in a protein do not undergo independent evolutionary processes. The underlying assumption is that proteins are composed of conserved and variable linear domains, and thus rates at neighboring sites are correlated. In this paper, we comprehensively examine the performance of an autocorrelation model of evolutionary rates in protein sequences. We further develop a model in which the level of correlation between rates at adjacent sites is not equal at all sites of the protein. High correlation is expected, for example, in linear functional domains. On the other hand, when we consider nonlinear functional regions (e.g., active sites), low correlation is expected because the interaction between distant sites imposes independence of rates in the linear sequence. Our model is based on a hidden Markov model, which accounts for autocorrelation at certain regions of the protein and rate independence at others. We study the differences between the novel model and models which assume either independence or a fixed level of dependence throughout the protein. Using a diverse set of protein data sets we show that the novel model better fits most data sets. We further analyze the potassium-channel protein family and illustrate the relationship between the dependence of rates at adjacent sites and the tertiary structure of the protein.