Proteomic applications of automated GPCR classification

Specificity of Monoterpene Interactions with Insect Octopamine and Tyramine Receptors: Insights from in Silico Sequence and Structure Comparison

Octopamine and tyramine receptors (OARs/TARs) are interesting targets for new insecticide development due to their unique roles in insects' physiological and cellular response and their specificity to invertebrates. Monoterpene compounds that bear resemblance to the natural ligands have been shown to bind to the OARs/TARs but elicit varied responses in different insect species. Using in silico methods, we attempt to investigate the molecular basis of monoterpene interactions and their specificity in different OARs and TARs of damaging or beneficial insects. Sequence and structure comparison revealed that the OARs/TARs studied generally have more similarities in terms of structure rather than sequence identity. Together with clustering and network analyses, we also revealed that the role of IL3 might be crucial in the identification of OAR and TAR and their unique function. Among the 35 monoterpenes subjected to ensemble docking, carvacrol had the most negative average binding energies with the target insect OARs and TARs. The differences in the key interacting residues of carvacrol with insect OARs and TARs could be the origin of variation in the responses of insect species to this monoterpene. Results suggest that carvacrol may be a potential natural-product-based insecticide, targeting multiple insect pests while being nonharmful to honeybees and Asian swallowtail butterflies. This work could provide insights into the development of effective species-specific natural-product-based insecticides that are more environmentally friendly than conventional insecticides.

Identification and characterization of expression profiles of neuropeptides and their GPCRs in the swimming crab, Portunus trituberculatus

Neuropeptides and their G protein-coupled receptors (GPCRs) regulate multiple physiological processes. Currently, little is known about the identity of native neuropeptides and their receptors in Portunus trituberculatus. This study employed RNA-sequencing and reverse transcription-polymerase chain reaction (RT-PCR) techniques to identify neuropeptides and their receptors that might be involved in regulation of reproductive processes of P. trituberculatus. In the central nervous system transcriptome data, 47 neuropeptide transcripts were identified. In further analyses, the tissue expression profile of 32 putative neuropeptide-encoding transcripts was estimated. Results showed that the 32 transcripts were expressed in the central nervous system and 23 of them were expressed in the ovary. A total of 47 GPCR-encoding transcripts belonging to two classes were identified, including 39 encoding GPCR-A family and eight encoding GPCR-B family. In addition, we assessed the tissue expression profile of 33 GPCRs (27 GPCR-As and six GPCR-Bs) transcripts. These GPCRs were found to be widely expressed in different tissues. Similar to the expression profiles of neuropeptides, 20 of these putative GPCR-encoding transcripts were also detected in the ovary. This is the first study to establish the identify of neuropeptides and their GPCRs in P. trituberculatus, and provide information for further investigations into the effect of neuropeptides on the physiology and behavior of decapod crustaceans.

Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space

GPCR proteins belong to diverse families of proteins that are defined at multiple hierarchical levels. Inspecting relationships between GPCR proteins on the hierarchical structure is important, since characteristics of the protein can be inferred from proteins in similar hierarchical information. However, modeling of GPCR families has been performed separately for each of the family, subfamily, and sub-subfamily level. Relationships between GPCR proteins are ignored in these approaches as they process the information in the proteins with several disconnected models. In this study, we propose DeepHier, a deep learning model to simultaneously learn representations of GPCR family hierarchy from the protein sequences with a unified single model. Novel loss term based on metric learning is introduced to incorporate hierarchical relations between proteins. We tested our approach using a public GPCR sequence dataset. Metric distances in the deep feature space corresponded to the hierarchical family relation between GPCR proteins. Furthermore, we demonstrated that further downstream tasks, like phylogenetic reconstruction and motif discovery, are feasible in the constructed embedding space. These results show that hierarchical relations between sequences were successfully captured in both of technical and biological aspects.

Improving protein domain classification for third-generation sequencing reads using deep learning

BACKGROUND

With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the underlying data. However, the high error rate in TGS data raises a new challenge to established domain analysis pipelines. The state-of-the-art methods are not optimized for noisy reads and have shown unsatisfactory accuracy of domain classification in TGS data. New computational methods are still needed to improve the performance of domain prediction in long noisy reads.

RESULTS

In this work, we introduce ProDOMA, a deep learning model that conducts domain classification for TGS reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In addition, we formulate our problem as an open-set problem and thus our model can reject reads not containing the targeted domains. In the experiments on simulated long reads of protein coding sequences and real TGS reads from the human genome, our model outperforms HMMER and DeepFam on protein domain classification.

CONCLUSIONS

In summary, ProDOMA is a useful end-to-end protein domain analysis tool for long noisy reads without relying on error correction.

The comprehensive expression analysis of the G protein-coupled receptor from Penaeus monodon indicating it participates in innate immunity and anti-ammonia nitrogen stress

The G protein-coupled receptors (GPCRs) composed a superfamily that played an important role in physiological processes of crustaceans, with multiple functions such as growth and development, acting as a defense against stimulations from external factors. In this paper, one kind of GPCRs were identified from Penaeus monodon, called PmGPCR, included an open reading frame (ORF) of 1113 bp. Bioinformatic analysis showed that PmGPCR protein had the typical structure of seven transmembrane domains (7TM), especially the special Asp-Arg-Try motif (DRY motif) between the third transmembrane structures (TM3) and the second intracellular loops (IL-2) which can prove that PmGPCR belongs to the rhodopsin-like family. The analyses of phylogenetic tree indicated that the amino acid sequence of PmGPCR should be merged into Procambarus clarkiic with high identity (98%). Quantitative real-time PCR (q RT-PCR) revealed that PmGPCR mRNA was highly expressed in hepatopancreas, abdominal ganglia and lymph, in which it was significantly higher than that of other tissues (P < 0.05). In addition, the expression of PmGPCR was analyzed during three days post-stimulation with the gram-positive/negative bacteria, the mRNA expression level increased after challenged with gram - positive bacteria in hepatopancreas, lymph and intestines. During the development stages, PmGPCR showed significantly higher expression in nauplius, zoea III, mysis III and post larvae stages than that in other development stages. Meanwhile, the highest transcripts expression of PmGPCR in abdominal ganglia, hepatopancreas, lymph and intestines respectively appeared at D0, D1, D2 and D3/D4 stages of molting. High or low concentration of ammonia nitrogen up-regulated the expression level of PmGPCR at the initial stage in hepatopancreas and gill, and then down-regulated at 48 h. These results indicated PmGPCR may mediate the pathways that involved in growth and development process, survival in the adversity, in addition, provided the useful data to research GPCR-mediated physiological and biological process and explain the mechanisms to defense pathogens and anti-stress in shrimp.

Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest

G protein-coupled receptors (GPCRs) are the largest receptor superfamily. In this paper, we try to employ physical-chemical properties, which come from SVM-Prot, to represent GPCR. Random Forest was utilized as classifier for distinguishing them from other protein sequences. MEME suite was used to detect the most significant 10 conserved motifs of human GPCRs. In the testing datasets, the average accuracy was 91.61%, and the average AUC was 0.9282. MEME discovery analysis showed that many motifs aggregated in the seven hydrophobic helices transmembrane regions adapt to the characteristic of GPCRs. All of the above indicate that our machine-learning method can successfully distinguish GPCRs from non-GPCRs.

In silico prediction of the G-protein coupled receptors expressed during the metamorphic molt of Sagmariasus verreauxi (Crustacea: Decapoda) by mining transcriptomic data: RNA-seq to repertoire

Against a backdrop of food insecurity, the farming of decapod crustaceans is a rapidly expanding and globally significant source of food protein. Sagmariasus verreauxi spiny lobster, the subject of this study, are decapods of underdeveloped aquaculture potential. Crustacean neuropeptide G-protein coupled receptors (GPCRs) mediate endocrine pathways that are integral to animal fecundity, growth and survival. The potential use of novel biotechnologies to enhance GPCR-mediated physiology may assist in improving the health and productivity of farmed decapod populations. This study catalogues the GPCRs expressed in the early developmental stages, as well as adult tissues, with a view to illuminating key neuropeptide receptors. De novo assembled contiguous sequences generated from transcriptomic reads of metamorphic and post metamorphic S. verreauxi were filtered for seven transmembrane domains, and used as a reference for iterative re-mapping. Subsequent putative GPCR open reading frames (ORFs) were BLAST annotated, categorised, and compared to published orthologues based on phylogenetic analysis. A total of 85 GPCRs were digitally predicted, that represented each of the four arthropod subfamilies. They generally displayed low-level and non-differential metamorphic expression with few exceptions that we examined using RT-PCR and qPCR. Two putative CHH-like neuropeptide receptors were annotated. Three dimensional structural modelling suggests that these receptors exhibit a conserved extracellular ligand binding pocket, providing support to the notion that these receptors co-evolved with their ligands across Decapoda. This perhaps narrows the search for means to increase productivity of farmed decapod populations.

A statistical physics perspective on alignment-independent protein sequence comparison

Motivation: Within bioinformatics, the textual alignment of amino acid sequences has long dominated the determination of similarity between proteins, with all that implies for shared structure, function and evolutionary descent. Despite the relative success of modern-day sequence alignment algorithms, so-called alignment-free approaches offer a complementary means of determining and expressing similarity, with potential benefits in certain key applications, such as regression analysis of protein structure-function studies, where alignment-base similarity has performed poorly.

Results: Here, we offer a fresh, statistical physics-based perspective focusing on the question of alignment-free comparison, in the process adapting results from ‘first passage probability distribution’ to summarize statistics of ensemble averaged amino acid propensity values. In this article, we introduce and elaborate this approach.

Contact: d.r.flower@aston.ac.uk

G Protein-Coupled Receptor (GPCR) Expression in Native Cells: "Novel" endoGPCRs as Physiologic Regulators and Therapeutic Targets

G protein-coupled receptors (GPCRs), the largest family of signaling receptors in the human genome, are also the largest class of targets of approved drugs. Are the optimal GPCRs (in terms of efficacy and safety) currently targeted therapeutically? Especially given the large number (∼ 120) of orphan GPCRs (which lack known physiologic agonists), it is likely that previously unrecognized GPCRs, especially orphan receptors, regulate cell function and can be therapeutic targets. Knowledge is limited regarding the diversity and identity of GPCRs that are activated by endogenous ligands and that native cells express. Here, we review approaches to define GPCR expression in tissues and cells and results from studies using these approaches. We identify problems with the available data and suggest future ways to identify and validate the physiologic and therapeutic roles of previously unrecognized GPCRs. We propose that a particularly useful approach to identify functionally important GPCRs with therapeutic potential will be to focus on receptors that show selective increases in expression in diseased cells from patients and experimental animals.

Structure prediction of the second extracellular loop in G-protein-coupled receptors

G-protein-coupled receptors (GPCRs) play key roles in living organisms. Therefore, it is important to determine their functional structures. The second extracellular loop (ECL2) is a functionally important region of GPCRs, which poses significant challenge for computational structure prediction methods. In this work, we evaluated CABS, a well-established protein modeling tool for predicting ECL2 structure in 13 GPCRs. The ECL2s (with between 13 and 34 residues) are predicted in an environment of other extracellular loops being fully flexible and the transmembrane domain fixed in its x-ray conformation. The modeling procedure used theoretical predictions of ECL2 secondary structure and experimental constraints on disulfide bridges. Our approach yielded ensembles of low-energy conformers and the most populated conformers that contained models close to the available x-ray structures. The level of similarity between the predicted models and x-ray structures is comparable to that of other state-of-the-art computational methods. Our results extend other studies by including newly crystallized GPCRs.

Latrophilins updated

Latrophilins (LPHN) are part of a yet unexplored family of receptors comprising three isoforms, LPHN1-3, and belonging to a unique branch of G protein-coupled receptors (GPCR) named adhesion GPCR (aGPCR). LPHN are considered to be prototypical models for the study of aGPCR as they are one of the most evolutionary conserved members. Previously described as the target for a potent neurotoxin from the black widow spider venom, LPHN are now being studied under a whole new perspective. Indeed, recent advances have provided a better understanding of different aspects of this prototypical family of receptors: 1) elucidation of LPHN ectodomain organization by crystallography has unveiled a new functional domain with great repercussion on all the other members of the aGPCR family, 2) proteomic approaches have opened the gate to unsuspected functional characteristics of LPHN cellular role, and 3) genetic approaches have provided hints into the physiological functions of LPHN in specific systems and organisms. Moreover, genomic linkage studies screening human patients from diverse genetic backgrounds have involved LPHN gene defects in human disorders such as attention-deficit hyperactivity disorder and cancer. In this review, we will provide a historical perspective addressing experimental research on these receptors while highlighting the new advances and discoveries concerning LPHN functions. As GPCR still represent the most studied targets for the development of pharmacological approaches aiming at alleviating human disorders, the relevance of studying LPHN retains a high pertinence to better understand these receptors for the treatment of human diseases.

GPCRsort-responding to the next generation sequencing data challenge: prediction of G protein-coupled receptor classes using only structural region lengths

Next generation sequencing (NGS) and the attendant data deluge are increasingly impacting molecular life sciences research. Chief among the challenges and opportunities is to enhance our ability to classify molecular target data into meaningful and cohesive systematic nomenclature. In this vein, the G protein-coupled receptors (GPCRs) are the largest and most divergent receptor family that plays a crucial role in a host of pathophysiological pathways. For the pharmaceutical industry, GPCRs are a major drug target and it is estimated that 60%-70% of all medicines in development today target GPCRs. Hence, they require an efficient and rapid classification to group the members according to their functions. In addition to NGS and the Big Data challenge we currently face, an emerging number of orphan GPCRs further demand for novel, rapid, and accurate classification of the receptors since the current classification tools are inadequate and slow. This study presents the development of a new classification tool for GPCRs using the structural features derived from their primary sequences: GPCRsort. Comparison experiments with the current known GPCR classification techniques showed that GPCRsort is able to rapidly (in the order of minutes) classify uncharacterized GPCRs with 97.3% accuracy, whereas the best available technique's accuracy is 90.7%. GPCRsort is available in the public domain for postgenomics life scientists engaged in GPCR research with NGS: http://bioserver.ceng.metu.edu.tr/GPCRSort .

HMM-ModE: implementation, benchmarking and validation with HMMER3

BACKGROUND

HMM-ModE is a computational method that generates family specific profile HMMs using negative training sequences. The method optimizes the discrimination threshold using 10 fold cross validation and modifies the emission probabilities of profiles to reduce common fold based signals shared with other sub-families. The protocol depends on the program HMMER for HMM profile building and sequence database searching. The recent release of HMMER3 has improved database search speed by several orders of magnitude, allowing for the large scale deployment of the method in sequence annotation projects. We have rewritten our existing scripts both at the level of parsing the HMM profiles and modifying emission probabilities to upgrade HMM-ModE using HMMER3 that takes advantage of its probabilistic inference with high computational speed. The method is benchmarked and tested on GPCR dataset as an accurate and fast method for functional annotation.

RESULTS

The implementation of this method, which now works with HMMER3, is benchmarked with the earlier version of HMMER, to show that the effect of local-local alignments is marked only in the case of profiles containing a large number of discontinuous match states. The method is tested on a gold standard set of families and we have reported a significant reduction in the number of false positive hits over the default HMM profiles. When implemented on GPCR sequences, the results showed an improvement in the accuracy of classification compared with other methods used to classify the familyat different levels of their classification hierarchy.

CONCLUSIONS

The present findings show that the new version of HMM-ModE is a highly specific method used to differentiate between fold (superfamily) and function (family) specific signals, which helps in the functional annotation of protein sequences. The use of modified profile HMMs of GPCR sequences provides a simple yet highly specific method for classification of the family, being able to predict the sub-family specific sequences with high accuracy even though sequences share common physicochemical characteristics between sub-families.

Identification of G protein-coupled receptors in Schistosoma haematobium and S. mansoni by comparative genomics

BACKGROUND

Schistosomiasis is a parasitic disease affecting ~200 million people worldwide. Schistosoma haematobium and S. mansoni are two relatively closely related schistosomes (blood flukes), and the causative agents of urogenital and hepatointestinal schistosomiasis, respectively. The availability of genomic, transcriptomic and proteomic data sets for these two schistosomes now provides unprecedented opportunities to explore their biology, host interactions and schistosomiasis at the molecular level. A particularly important group of molecules involved in a range of biological and developmental processes in schistosomes and other parasites are the G protein-coupled receptors (GPCRs). Although GPCRs have been studied in schistosomes, there has been no detailed comparison of these receptors between closely related species. Here, using a genomic-bioinformatic approach, we identified and characterised key GPCRs in S. haematobium and S. mansoni (two closely related species of schistosome).

METHODS

Using a Hidden Markov Model (HMM) and Support Vector Machine (SVM)-based pipeline, we classified and sub-classified GPCRs of S. haematobium and S. mansoni, combined with phylogenetic and transcription analyses.

RESULTS

We identified and classified classes A, B, C and F as well as an unclassified group of GPCRs encoded in the genomes of S. haematobium and S. mansoni. In addition, we characterised ligand-specific subclasses (i.e. amine, peptide, opsin and orphan) within class A (rhodopsin-like).

CONCLUSIONS

Most GPCRs shared a high degree of similarity and conservation, except for members of a particular clade (designated SmGPR), which appear to have diverged between S. haematobium and S. mansoni and might explain, to some extent, some of the underlying biological differences between these two schistosomes. The present set of annotated GPCRs provides a basis for future functional genomic studies of cellular GPCR-mediated signal transduction and a resource for future drug discovery efforts in schistosomes.

Identification of novel arthropod vector G protein-coupled receptors

BACKGROUND

The control of vector-borne diseases, such as malaria, dengue fever, and typhus fever is often achieved with the use of insecticides. Unfortunately, insecticide resistance is becoming common among different vector species. There are currently no chemical alternatives to these insecticides because new human-safe classes of molecules have yet to be brought to the vector-control market. The identification of novel targets offer opportunities for rational design of new chemistries to control vector populations. One target family, G protein-coupled receptors (GPCRs), has remained relatively under explored in terms of insecticide development.

METHODS

A novel classifier, Ensemble*, for vector GPCRs was developed. Ensemble* was validated and compared to existing classifiers using a set of all known GPCRs from Aedes aegypti, Anopheles gambiae, Apis Mellifera, Drosophila melanogaster, Homo sapiens, and Pediculus humanus. Predictions for unidentified sequences from Ae. aegypti, An. gambiae, and Pe. humanus were validated. Quantitative RT-PCR expression analysis was performed on previously-known and newly discovered Ae. aegypti GPCR genes.

RESULTS

We present a new analysis of GPCRs in the genomes of Ae, aegypti, a vector of dengue fever, An. gambiae, a primary vector of Plasmodium falciparum that causes malaria, and Pe. humanus, a vector of epidemic typhus fever, using a novel GPCR classifier, Ensemble*, designed for insect vector species. We identified 30 additional putative GPCRs, 19 of which we validated. Expression of the newly discovered Ae. aegypti GPCR genes was confirmed via quantitative RT-PCR.

CONCLUSION

A novel GPCR classifier for insect vectors, Ensemble*, was developed and GPCR predictions were validated. Ensemble* and the validation pipeline were applied to the genomes of three insect vectors (Ae. aegypti, An. gambiae, and Pe. humanus), resulting in the identification of 52 GPCRs not previously identified, of which 11 are predicted GPCRs, and 19 are predicted and confirmed GPCRs.

Identification of Candidate Vaccine Antigens In Silico

The identification of immunogenic whole-protein antigens is fundamental to the successful discovery of candidate subunit vaccines and their rapid, effective, and efficient transformation into clinically useful, commercially successful vaccine formulations. In the wider context of the experimental discovery of vaccine antigens, with particular reference to reverse vaccinology, this chapter adumbrates the principal computational approaches currently deployed in the hunt for novel antigens: genome-level prediction of antigens, antigen identification through the use of protein sequence alignment-based approaches, antigen detection through the use of subcellular location prediction, and the use of alignment-independent approaches to antigen discovery. Reference is also made to the recent emergence of various expert systems for protein antigen identification.

The repertoire of G protein-coupled receptors in the human parasite Schistosoma mansoni and the model organism Schmidtea mediterranea

Background

G protein-coupled receptors (GPCRs) constitute one of the largest groupings of eukaryotic proteins, and represent a particularly lucrative set of pharmaceutical targets. They play an important role in eukaryotic signal transduction and physiology, mediating cellular responses to a diverse range of extracellular stimuli. The phylum Platyhelminthes is of considerable medical and biological importance, housing major pathogens as well as established model organisms. The recent availability of genomic data for the human blood fluke Schistosoma mansoni and the model planarian Schmidtea mediterranea paves the way for the first comprehensive effort to identify and analyze GPCRs in this important phylum.

Results

Application of a novel transmembrane-oriented approach to receptor mining led to the discovery of 117 S. mansoni GPCRs, representing all of the major families; 105 Rhodopsin, 2 Glutamate, 3 Adhesion, 2 Secretin and 5 Frizzled. Similarly, 418 Rhodopsin, 9 Glutamate, 21 Adhesion, 1 Secretin and 11 Frizzled S. mediterranea receptors were identified. Among these, we report the identification of novel receptor groupings, including a large and highly-diverged Platyhelminth-specific Rhodopsin subfamily, a planarian-specific Adhesion-like family, and atypical Glutamate-like receptors. Phylogenetic analysis was carried out following extensive gene curation. Support vector machines (SVMs) were trained and used for ligand-based classification of full-length Rhodopsin GPCRs, complementing phylogenetic and homology-based classification.

Conclusions

Genome-wide investigation of GPCRs in two platyhelminth genomes reveals an extensive and complex receptor signaling repertoire with many unique features. This work provides important sequence and functional leads for understanding basic flatworm receptor biology, and sheds light on a lucrative set of anthelmintic drug targets.

Mechanisms of Hedgehog signalling in cancer

The Hedgehog (Hh) pathway is a conserved signalling system essential for embryonic development and for the maintenance of self-renewal pathways in progenitor cells. Mutations that deregulate Hh signalling are directly implicated in basal cell carcinoma and medulloblastoma. The mechanisms of Hh pathway activation in cancers in which no pathway mutations have been identified are less clear, but of great translational significance. Small molecule inhibitors of the pathway, many of which are in early phase clinical trials, may shed further light on this question. Canonical Hh signalling promotes the expression of target genes through the Glioma-associated oncogene (GLI) transcription factors. There is now increasing evidence suggesting that 'non-canonical' Hh signalling mechanisms, some of which are independent of GLI-mediated transcription, may be important in cancer and development. The focus of this review is to summarise some of the known mechanisms of Hh signalling as well as its emerging role in cancer.

Expression of G protein-coupled receptors and related proteins in HEK293, AtT20, BV2, and N18 cell lines as revealed by microarray analysis

BACKGROUND

G protein coupled receptors (GPCRs) are one of the most widely studied gene superfamilies. Thousands of GPCR research studies have utilized heterologous expression systems such as human embryonic kidney cells (HEK293). Though often treated as 'blank slates', these cell lines nevertheless endogenously express GPCRs and related signaling proteins. The outcome of a given GPCR study can be profoundly influenced by this largely unknown complement of receptors and/or signaling proteins. Little easily accessible information exists that describes the expression profiles of the GPCRs in cell lines. What is accessible is often limited in scope - of the hundreds of GPCRs and related proteins, one is unlikely to find information on expression of more than a dozen proteins in a given cell line. Microarray technology has allowed rapid analysis of mRNA levels of thousands of candidate genes, but though often publicly available, the results can be difficult to efficiently access or even to interpret.

RESULTS

To bridge this gap, we have used microarrays to measure the mRNA levels of a comprehensive profile of non-chemosensory GPCRs and over a hundred GPCR signaling related gene products in four cell lines frequently used for GPCR research: HEK293, AtT20, BV2, and N18.

CONCLUSIONS

This study provides researchers an easily accessible mRNA profile of the endogenous signaling repertoire that these four cell lines possess. This will assist in choosing the most appropriate cell line for studying GPCRs and related signaling proteins. It also provides a better understanding of the potential interactions between GPCRs and those signaling proteins.

Trichomonas transmembrane cyclases result from massive gene duplication and concomitant development of pseudogenes

Background

Trichomonas vaginalis has an unusually large genome (∼160 Mb) encoding ∼60,000 proteins. With the goal of beginning to understand why some Trichomonas genes are present in so many copies, we characterized here a family of ∼123 Trichomonas genes that encode transmembrane adenylyl cyclases (TMACs).

Methodology/Principal Findings

The large family of TMACs genes is the result of recent duplications of a small set of ancestral genes that appear to be unique to trichomonads. Duplicated TMAC genes are not closely associated with repetitive elements, and duplications of flanking sequences are rare. However, there is evidence for TMAC gene replacements by homologous recombination. A high percentage of TMAC genes (∼46%) are pseudogenes, as they contain stop codons and/or frame shifts, or the genes are truncated. Numerous stop codons present in the genome project G3 strain are not present in orthologous genes of two other Trichomonas strains (S1 and B7RC2). Each TMAC is composed of a series of N-terminal transmembrane helices and a single C-terminal cyclase domain that has adenylyl cyclase activity. Multiple TMAC genes are transcribed by Trichomonas cloned by limiting dilution.

Conclusions/Significance

We conclude that one reason for the unusually large genome of Trichomonas is the presence of unstable families of genes such as those encoding TMACs that are undergoing massive gene duplication and concomitant development of pseudogenes.

Trichomonas vaginalis is the only medically important protist (single-cell eukaryote) that is sexually transmitted. The ∼160-Mb Trichomonas genome contains more predicted protein-encoding genes (∼60,000) than the human genome. To begin to understand why there are so many copies of some genes, we chose here to study a large family of genes encoding unique transmembrane cyclases. Our most important results include the following. More than 100 transmembrane cyclase genes do not result from chromosomal duplications, because for the most part only the coding regions of the genes, rather than flanking sequences, are duplicated. Almost half of the transmembrane cyclase genes are pseudogenes, and these pseudogenes are polymorphic among laboratory strains of Trichomonas. Messenger RNAs for numerous transmembrane cyclases are expressed simultaneously, and representative cyclase domains have adenylyl cyclase activity. In summary, the large family of Trichomonas genes encoding transmembrane adenylyl cyclases results from massive gene duplication and concomitant development of pseudogenes.

On the importance of comprehensible classification models for protein function prediction

The literature on protein function prediction is currently dominated by works aimed at maximizing predictive accuracy, ignoring the important issues of validation and interpretation of discovered knowledge, which can lead to new insights and hypotheses that are biologically meaningful and advance the understanding of protein functions by biologists. The overall goal of this paper is to critically evaluate this approach, offering a refreshing new perspective on this issue, focusing not only on predictive accuracy but also on the comprehensibility of the induced protein function prediction models. More specifically, this paper aims to offer two main contributions to the area of protein function prediction. First, it presents the case for discovering comprehensible protein function prediction models from data, discussing in detail the advantages of such models, namely, increasing the confidence of the biologist in the system's predictions, leading to new insights about the data and the formulation of new biological hypotheses, and detecting errors in the data. Second, it presents a critical review of the pros and cons of several different knowledge representations that can be used in order to support the discovery of comprehensible protein function prediction models.

GPCRTree: online hierarchical classification of GPCR function

Background

G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence.

Findings

Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level.

Conclusion

A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: .

Calorimetric studies of bovine rod outer segment disk membranes support a monomeric unit for both rhodopsin and opsin

The photoreceptor rhodopsin is a G-protein coupled receptor that has recently been proposed to exist as a dimer or higher order oligomer, in contrast to the previously described monomer, in retinal rod outer segment disk membranes. Rhodopsin exhibits considerably greater thermal stability than opsin (the bleached form of the receptor), which is reflected in an approximately 15 degrees C difference in the thermal denaturation temperatures (T(m)) of rhodopsin and opsin as measured by differential scanning calorimetry. Here we use differential scanning calorimetry to investigate the effect of partial bleaching of disk membranes on the T(m) of rhodopsin and of opsin in native disk membranes, as well as in cross-linked disk membranes in which rhodopsin dimers are known to be present. The T(m)s of rhodopsin and opsin are expected to be perturbed if mixed oligomers are present. The T(m) remained constant for rhodopsin and opsin in native disks regardless of the level of bleaching. In contrast, the T(m) of cross-linked rhodopsin in disk membranes was dependent on the extent of bleaching. The energy of activation for denaturation of rhodopsin and cross-linked rhodopsin was calculated. Cross-linking rhodopsin significantly decreased the energy of activation. We conclude that in native disk membranes, rhodopsin behaves predominantly as a monomer.

On the hierarchical classification of G protein-coupled receptors

MOTIVATION

G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs.

RESULTS

An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.