CREST - a large and diverse superfamily of putative transmembrane hydrolases

<p>Abstract</p> <p>Background</p> <p>A number of membrane-spanning proteins possess enzymatic activity and catalyze important reactions involving proteins, lipids or other substrates located within or near lipid bilayers. Alkaline ceramidases are seven-transmembrane proteins that hydrolyze the amide bond in ceramide to form sphingosine. Recently, a group of putative transmembrane receptors called progestin and adipoQ receptors (PAQRs) were found to be distantly related to alkaline ceramidases, raising the possibility that they may also function as membrane enzymes.</p> <p>Results</p> <p>Using sensitive similarity search methods, we identified statistically significant sequence similarities among several transmembrane protein families including alkaline ceramidases and PAQRs. They were unified into a large and diverse superfamily of putative membrane-bound hydrolases called CREST (alkaline ceramidase, PAQR receptor, Per1, SID-1 and TMEM8). The CREST superfamily embraces a plethora of cellular functions and biochemical activities, including putative lipid-modifying enzymes such as ceramidases and the Per1 family of putative phospholipases involved in lipid remodeling of GPI-anchored proteins, putative hormone receptors, bacterial hemolysins, the TMEM8 family of putative tumor suppressors, and the SID-1 family of putative double-stranded RNA transporters involved in RNA interference. Extensive similarity searches and clustering analysis also revealed several groups of proteins with unknown function in the CREST superfamily. Members of the CREST superfamily share seven predicted core transmembrane segments with several conserved sequence motifs.</p> <p>Conclusions</p> <p>Universal conservation of a set of histidine and aspartate residues across all groups in the CREST superfamily, coupled with independent discoveries of hydrolase activities in alkaline ceramidases and the Per1 family as well as results from previous mutational studies of Per1, suggests that the majority of CREST members are metal-dependent hydrolases.</p> <p>Reviewers</p> <p>This article was reviewed by Kira S. Markarova, Igor B. Zhulin and Rob Knight.</p

Reconstruction of ancestral protein sequences and its applications

BACKGROUND: Modern-day proteins were selected during long evolutionary history as descendants of ancient life forms. In silico reconstruction of such ancestral protein sequences facilitates our understanding of evolutionary processes, protein classification and biological function. Additionally, reconstructed ancestral protein sequences could serve to fill in sequence space thus aiding remote homology inference. RESULTS: We developed ANCESCON, a package for distance-based phylogenetic inference and reconstruction of ancestral protein sequences that takes into account the observed variation of evolutionary rates between positions that more precisely describes the evolution of protein families. To improve the accuracy of evolutionary distance estimation and ancestral sequence reconstruction, two approaches are proposed to estimate position-specific evolutionary rates. Comparisons show that at large evolutionary distances our method gives more accurate ancestral sequence reconstruction than PAML, PHYLIP and PAUP*. We apply the reconstructed ancestral sequences to homology inference and functional site prediction. We show that the usage of hypothetical ancestors together with the present day sequences improves profile-based sequence similarity searches; and that ancestral sequence reconstruction methods can be used to predict positions with functional specificity. CONCLUSIONS: As a computational tool to reconstruct ancestral protein sequences from a given multiple sequence alignment, ANCESCON shows high accuracy in tests and helps detection of remote homologs and prediction of functional sites. ANCESCON is freely available for non-commercial use. Pre-compiled versions for several platforms can be downloaded from

PROMALS web server for accurate multiple protein sequence alignments

Multiple sequence alignments are essential in homology inference, structure modeling, functional prediction and phylogenetic analysis. We developed a web server that constructs multiple protein sequence alignments using PROMALS, a progressive method that improves alignment quality by using additional homologs from PSI-BLAST searches and secondary structure predictions from PSIPRED. PROMALS shows higher alignment accuracy than other advanced methods, such as MUMMALS, ProbCons, MAFFT and SPEM. The PROMALS web server takes FASTA format protein sequences as input. The output includes a colored alignment augmented with information about sequence grouping, predicted secondary structures and positional conservation. The PROMALS web server is available at: http://prodata.swmed.edu/promals

PROMALS3D: a tool for multiple protein sequence and structure alignments

Although multiple sequence alignments (MSAs) are essential for a wide range of applications from structure modeling to prediction of functional sites, construction of accurate MSAs for distantly related proteins remains a largely unsolved problem. The rapidly increasing database of spatial structures is a valuable source to improve alignment quality. We explore the use of 3D structural information to guide sequence alignments constructed by our MSA program PROMALS. The resulting tool, PROMALS3D, automatically identifies homologs with known 3D structures for the input sequences, derives structural constraints through structure-based alignments and combines them with sequence constraints to construct consistency-based multiple sequence alignments. The output is a consensus alignment that brings together sequence and structural information about input proteins and their homologs. PROMALS3D can also align sequences of multiple input structures, with the output representing a multiple structure-based alignment refined in combination with sequence constraints. The advantage of PROMALS3D is that it gives researchers an easy way to produce high-quality alignments consistent with both sequences and structures of proteins. PROMALS3D outperforms a number of existing methods for constructing multiple sequence or structural alignments using both reference-dependent and reference-independent evaluation methods

A pathogenic UFSP2 variant in an autosomal recessive form of pediatric neurodevelopmental anomalies and epilepsy

Purpose: Neurodevelopmental disabilities are common and genetically heterogeneous. We identified a homozygous variant in the gene encoding UFM1-specific peptidase 2 (UFSP2), which participates in the UFMylation pathway of protein modification. UFSP2 variants are implicated in autosomal dominant skeletal dysplasias, but not neurodevelopmental disorders. Homozygosity for the variant occurred in eight children from four South Asian families with neurodevelopmental delay and epilepsy. We describe the clinical consequences of this variant and its effect on UFMylation.Methods: Exome sequencing was used to detect potentially pathogenic variants and identify shared regions of homozygosity. Immunoblotting assessed protein expression and post-translational modifications in patient-derived fibroblasts.Results: The variant (c.344T\u3eA; p.V115E) is rare and alters a conserved residue in UFSP2. Immunoblotting in patient-derived fibroblasts revealed reduced UFSP2 abundance and increased abundance of UFMylated targets, indicating the variant may impair de-UFMylation rather than UFMylation. Reconstituting patient-derived fibroblasts with wild-type UFSP2 reduced UFMylation marks. Analysis of UFSP2\u27s structure indicated that variants observed in skeletal disorders localize to the catalytic domain, whereas V115 resides in an N-terminal domain possibly involved in substrate binding.Conclusion: Different UFSP2 variants cause markedly different diseases, with homozygosity for V115E causing a severe syndrome of neurodevelopmental disability and epilepsy

Flatworm-specific transcriptional regulators promote the specification of tegumental progenitors in Schistosoma mansoni

Schistosomes infect more than 200 million people. These parasitic flatworms rely on a syncytial outer coat called the tegument to survive within the vasculature of their host. Although the tegument is pivotal for their survival, little is known about maintenance of this tissue during the decades schistosomes survive in the bloodstream. Here, we demonstrate that the tegument relies on stem cells (neoblasts) to specify fusogenic progenitors that replace tegumental cells lost to turnover. Molecular characterization of neoblasts and tegumental progenitors led to the discovery of two flatworm-specific zinc finger proteins that are essential for tegumental cell specification. These proteins are homologous to a protein essential for neoblast-driven epidermal maintenance in free-living flatworms. Therefore, we speculate that related parasites (i.e., tapeworms and flukes) employ similar strategies to control tegumental maintenance. Since parasitic flatworms infect every vertebrate species, understanding neoblast-driven tegumental maintenance could identify broad-spectrum therapeutics to fight diseases caused by these parasites

Modelling the Effects of Climatic Factors on the Biomass and Rodent Distribution in a Tibetan Grassland Region in China

To identify the main climatic factors from 2007 to 2009 that influence biomass and rodent distribution, 576 fixed sample plots within 81 million km2 of different climatic grassland in Tibet were monitored. The aboveground biomass, the total burrows, the active burrows, the burrow index, and the rodent density in the plots were measured yearly in October. The monthly precipitation and the average temperatures from April to November were obtained for four successive years (2006-2009). Correlative and modelling analyses between the aboveground biomass, the rodent density, and the climatic factors were performed. The results showed that biomass and rodent density were significantly correlated with the climatic factors. Using ridge regression analyses, models of the biomass and rodent density with respect to the monthly precipitations and average temperatures of the previous year were developed. The raw testing data demonstrated that the models can be used approximately to predict biomass and rodent density

MUMMALS: multiple sequence alignment improved by using hidden Markov models with local structural information

We have developed MUMMALS, a program to construct multiple protein sequence alignment using probabilistic consistency. MUMMALS improves alignment quality by using pairwise alignment hidden Markov models (HMMs) with multiple match states that describe local structural information without exploiting explicit structure predictions. Parameters for such models have been estimated from a large library of structure-based alignments. We show that (i) on remote homologs, MUMMALS achieves statistically best accuracy among several leading aligners, such as ProbCons, MAFFT and MUSCLE, albeit the average improvement is small, in the order of several percent; (ii) a large collection (>10 000) of automatically computed pairwise structure alignments of divergent protein domains is superior to smaller but carefully curated datasets for estimation of alignment parameters and performance tests; (iii) reference-independent evaluation of alignment quality using sequence alignment-dependent structure superpositions correlates well with reference-dependent evaluation that compares sequence-based alignments to structure-based reference alignments