The arthropod cuticle is a composite, bipartite system, made of chitin filaments embedded in a proteinaceous matrix. The physical properties of cuticle are determined by the structure and the interactions of its two major components, cuticular proteins (CPs) and chitin. The proteinaceous matrix consists mainly of structural cuticular proteins. The majority of the structural proteins that have been described to date belong to the CPR family, and they are identified by the conserved R&R region (Rebers and Riddiford Consensus). Two major subfamilies of the CPR family RR-1 and RR-2, have also been identified from conservation at sequence level and some correlation with the cuticle type. Recently, several novel families, also containing characteristic conserved regions, have been described. The package HMMER v3.0 [http://hmmer.janelia.org/] was used to build characteristic profile Hidden Markov Models based on the characteristic regions for 8 of these families, (CPF, CPAP3, CPAP1, CPCFC, CPLCA, CPLCG, CPLCW, Tweedle). In brief, these families can be described as having: CPF (a conserved region with 44 amino acids); CPAP1 and CPAP-3 (analogous to peritrophins, with 1 and 3 chitin-binding domains, respectively); CPCFC (2 or 3 C-x(5)-C repeats); and four of five low complexity (LC) families, each with characteristic domains. Using these models, as well as the models previously created for the two major subfamilies of the CPR family, RR-1 and RR-2 (Karouzou et al., 2007), we developed CutProtFam-Pred, an on-line tool (http://bioinformatics.biol.uoa.gr/CutProtFam-Pred) that allows one to query sequences from proteomes or translated transcriptomes, for the accurate detection and classification of putative structural cuticular proteins. The tool has been applied successfully to diverse arthropod proteomes including a crustacean (Daphnia pulex) and a chelicerate (Tetranychus urticae), but at this taxonomic distance only CPRs and CPAPs were recovered.

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G-protein coupled receptors (GPCRs) are a major family of membrane receptors in eukaryotic cells. They play a crucial role in the communication of a cell with the environment. Ligands bind to GPCRs on the outside of the cell, activating them by causing a conformational change, and allowing them to bind to G-proteins. Through their interaction with G-proteins, several effector molecules are activated leading to many kinds of cellular and physiological responses. The great importance of GPCRs and their corresponding signal transduction pathways is indicated by the fact that they take part in many diverse disease processes and that a large part of efforts towards drug development today is focused on them. We present Human-gpDB, a database which currently holds information about 713 human GPCRs, 36 human G-proteins and 99 human effectors. The collection of information about the interactions between these molecules was done manually and the current version of Human-gpDB holds information for about 1663 connections between GPCRs and G-proteins and 1618 connections between G-proteins and effectors. Major advantages of Human-gpDB are the integration of several external data sources and the support of advanced visualization techniques. Human-gpDB is a simple, yet a powerful tool for researchers in the life sciences field as it integrates an up-to-date, carefully curated collection of human GPCRs, G-proteins, effectors and their interactions. The database may be a reference guide for medical and pharmaceutical research, especially in the areas of understanding human diseases and chemical and drug discovery.Database URLs: http://schneider.embl.de/human_gpdb; http://bioinformatics.biol.uoa.gr/human_gpdb/

Supplementary data are available at Bioinformatics online.

A significant amount of experimental evidence suggests that G-protein coupled receptors (GPCRs) do not act exclusively as monomers but also form biologically relevant dimers and oligomers. However, the structural determinants, stoichiometry and functional importance of GPCR oligomerization remain topics of intense speculation. In this study we attempted to evaluate the nature and dynamics of GPCR oligomeric interactions. A representative set of GPCR homodimers were studied through Coarse-Grained Molecular Dynamics simulations, combined with interface analysis and concepts from network theory for the construction and analysis of dynamic structural networks. Our results highlight important structural determinants that seem to govern receptor dimer interactions. A conserved dynamic behavior was observed among different GPCRs, including receptors belonging in different GPCR classes. Specific GPCR regions were highlighted as the core of the interfaces. Finally, correlations of motion were observed between parts of the dimer interface and GPCR segments participating in ligand binding and receptor activation, suggesting the existence of mechanisms through which dimer formation may affect GPCR function. The results of this study can be used to drive experiments aimed at exploring GPCR oligomerization, as well as in the study of transmembrane protein-protein interactions in general.

Motivation Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications. Results We propose here, a method for semi-supervised learning of HMMs that can incorporate labeled, unlabeled and partially labeled data in a straightforward manner. The algorithm is based on a variant of the Expectation-Maximization (EM) algorithm, where the missing labels of the unlabeled or partially labeled data are considered as the missing data. We apply the algorithm to several biological problems, namely, for the prediction of transmembrane protein topology for alpha-helical and beta-barrel membrane proteins and for the prediction of archaeal signal peptides. The results are very promising, since the algorithms presented here can significantly improve the prediction performance of even the top-scoring classifiers. Supplementary information Supplementary data are available at Bioinformatics online.

Next-generation sequencing (NGS) can provide researchers with high impact information regarding alternative splice variants or transcript identifications. However, the enormous amount of data acquired from NGS platforms make the analysis of alternative splicing events hard to accomplish. For this reason, we designed the "Alternative Splicing Detection Tool" (ASDT), an algorithm that is capable of identifying alternative splicing events, including novel ones from high-throughput NGS data. ASDT is available as a PERL script at http://aias.biol.uoa.gr/~mtheo and can be executed on any system with PERL installed. In addition to the detection of annotated and novel alternative splicing events from high-throughput NGS data, ASDT can also analyze the intronic regions of genes, thus enabling the detection of novel cryptic exons residing in annotated introns, extensions of previously annotated exons, or even intron retentions. Consequently, ASDT demonstrates many innovative and unique features that can efficiently contribute to alternative splicing analysis of NGS data.

Even though in the last few years several families of eukaryotic β-barrel outer membrane proteins (OMPs) have been discovered, their computational characterization and their annotation in public databases is far from complete. The PFAM database includes only very few characteristic profiles for these families and, in most cases, the profile Hidden Markov Models (pHMMs) have been trained using prokaryotic and eukaryotic proteins together. Here, we present for the first time a comprehensive computational analysis of eukaryotic transmembrane β-barrels. 12 characteristic pHMMs were built, based on an extensive literature search, that can discriminate eukaryotic β-barrels from other classes of proteins (globular and bacterial β-barrel ones), as well as between mitochondrial and chloroplastic ones. We built eight novel profiles for the chloroplastic β-barrel families that are not present in the PFAM database and, also, updated the profile for the MDM10 family 2 (PF12519) in PFAM and split the porin family (PF01459) into two separate families, namely VDAC and TOM40.