GPCRTree: online hierarchical classification of GPCR function

The path to the G protein-coupled receptor structural landscape: Major milestones and future directions

G protein-coupled receptors (GPCRs) play a crucial role in cell function by transducing signals from the extracellular environment to the inside of the cell. They mediate the effects of various stimuli, including hormones, neurotransmitters, ions, photons, food tastants and odorants, and are renowned drug targets. Advancements in structural biology techniques, including X-ray crystallography and cryo-electron microscopy (cryo-EM), have driven the elucidation of an increasing number of GPCR structures. These structures reveal novel features that shed light on receptor activation, dimerization and oligomerization, dichotomy between orthosteric and allosteric modulation, and the intricate interactions underlying signal transduction, providing insights into diverse ligand-binding modes and signalling pathways. However, a substantial portion of the GPCR repertoire and their activation states remain structurally unexplored. Future efforts should prioritize capturing the full structural diversity of GPCRs across multiple dimensions. To do so, the integration of structural biology with biophysical and computational techniques will be essential. We describe in this review the progress of nuclear magnetic resonance (NMR) to examine GPCR plasticity and conformational dynamics, of atomic force microscopy (AFM) to explore the spatial-temporal dynamics and kinetic aspects of GPCRs, and the recent breakthroughs in artificial intelligence for protein structure prediction to characterize the structures of the entire GPCRome. In summary, the journey through GPCR structural biology provided in this review illustrates how far we have come in decoding these essential proteins architecture and function. Looking ahead, integrating cutting-edge biophysics and computational tools offers a path to navigating the GPCR structural landscape, ultimately advancing GPCR-based applications.

Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs

Class A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs. They are essential targets of drug discovery and thus various computational approaches have been applied to predict their ligands. However, there are a large number of orphan receptors in class A GPCRs and it is difficult to use a general protein-specific supervised prediction scheme. Therefore, the compound-protein interaction (CPI) prediction approach has been considered one of the most suitable for class A GPCRs. However, the accuracy of CPI prediction is still insufficient. The current CPI prediction model generally employs the whole protein sequence as the input because it is difficult to identify the important regions in general proteins. In contrast, it is well-known that only a few transmembrane helices of class A GPCRs play a critical role in ligand binding. Therefore, using such domain knowledge, the CPI prediction performance could be improved by developing an encoding method that is specifically designed for this family. In this study, we developed a protein sequence encoder called the Helix encoder, which takes only a protein sequence of transmembrane regions of class A GPCRs as input. The performance evaluation showed that the proposed model achieved a higher prediction accuracy compared to a prediction model using the entire protein sequence. Additionally, our analysis indicated that several extracellular loops are also important for the prediction as mentioned in several biological researches.

Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1

With approximately 400 encoding genes in humans, odorant receptors (ORs) are the largest subfamily of class A G protein-coupled receptors (GPCRs). Despite its high relevance and representation, the odorant-GPCRome is structurally poorly characterized: no experimental structures are available, and the low sequence identity of ORs to experimentally solved GPCRs is a significant challenge for their modeling. Moreover, the receptive range of most ORs is unknown. The odorant receptor OR5K1 was recently and comprehensively characterized in terms of cognate agonists. Here, we report two additional agonists and functional data of the most potent compound on two mutants, L104^3.32 and L255^6.51. Experimental data was used to guide the investigation of the binding modes of OR5K1 ligands into the orthosteric binding site using structural information from AI-driven modeling, as recently released in the AlphaFold Protein Structure Database, and from homology modeling. Induced-fit docking simulations were used to sample the binding site conformational space for ensemble docking. Mutagenesis data guided side chain residue sampling and model selection. We obtained models that could better rationalize the different activity of active (agonist) versus inactive molecules with respect to starting models and also capture differences in activity related to minor structural differences. Therefore, we provide a model refinement protocol that can be applied to model the orthosteric binding site of ORs as well as that of GPCRs with low sequence identity to available templates.

Molecular Characterization and Expression Analysis of Putative Class C (Glutamate Family) G Protein-Coupled Receptors in Ascidian Styela clava

In this study, we performed the genome-wide domain analysis and sequence alignment on the genome of Styela clava, and obtained a repertoire of 204 putative GPCRs, which exhibited a highly reduced gene number compared to vertebrates and cephalochordates. In this repertoire, six Class C GPCRs, including four metabotropic glutamate receptors (Sc-GRMs), one calcium-sensing receptor (Sc-CaSR), and one gamma-aminobutyric acid (GABA) type B receptor 2-like (Sc-GABABR2-like) were identified, with the absence of type 1 taste and vomeronasal receptors. All the Sc-GRMs and Sc-CaSR contained the typical "Venus flytrap" and cysteine-rich domains required for ligand binding and subsequent propagation of conformational changes. In swimming larvae, Sc-grm3 and Sc-casr were mainly expressed at the junction of the sensory vesicle and tail nerve cord while the transcripts of Sc-grm4, Sc-grm7a, and Sc-grm7b appeared at the anterior trunk, which suggested their important functions in neurotransmission. The high expression of these Class C receptors at tail-regression and metamorphic juvenile stages hinted at their potential involvement in regulating metamorphosis. In adults, the transcripts were highly expressed in several peripheral tissues, raising the possibility that S. clava Class C GPCRs might function as neurotransmission modulators peripherally after metamorphosis. Our study systematically characterized the ancestral chordate Class C GPCRs to provide insights into the origin and evolution of these receptors in chordates and their roles in regulating physiological and morphogenetic changes relevant to the development and environmental adaption.

Classification Model for the Second Extracellular Loop of Class A GPCRs

The extracellular loop 2 (ECL2) is the longest and the most diverse loop among class A G protein-coupled receptors (GPCRs). It connects the transmembrane (TM) helices 4 and 5 and contains a highly conserved cysteine through which it is bridged with TM3. In this paper, experimental ECL2 structures were analyzed based on their sequences, shapes, and intramolecular contacts. To take into account the flexibility, we incorporated into our analyses information from the molecular dynamics trajectories available on the GPCRmd website. Despite the high sequence variability, shapes of the analyzed structures, defined by the backbone volume overlaps, can be clustered into seven main groups. Conformational differences within the clusters can be then identified by intramolecular interactions with other GPCR structural domains. Overall, our work provides a reorganization of the structural information of the ECL2 of class A GPCR subfamilies, highlighting differences and similarities on sequence and conformation levels.

Revisiting the classification of adhesion GPCRs

G protein–coupled receptors (GPCRs) are encoded by over 800 genes in the human genome. Motivated by different scientific rationales, the two classification systems that are mainly in use, the ABC and GRAFS systems, organize GPCRs according to their pharmacological features and phylogenetic relations, respectively. Within those systems, adhesion GPCRs (aGPCRs) constitute a group of over 30 mammalian homologs, most of which are still orphans with undefined activating signals and signal transduction properties. Previous efforts have further subdivided mammalian aGPCRs into nine subfamilies to indicate phylogenetic relationships. However, this subclassification scheme has shortcomings and inconsistencies that require attention. Here, we have reassessed the phylogenetic relationships of aGPCRs from vertebrate and invertebrate species. Our findings confirm that secretin receptor–like GPCRs most probably emerged from ancestral aGPCRs. We show that reassignment of several aGPCRs to families essentially requires input from functional data. Our analyses establish the need for introducing novel aGPCR subfamilies due to aGPCR sequences from invertebrate species that are not readily assignable to any existing subfamily. We conclude that the current classification systems ought to be updated to consider an unambiguous taxonomy of a hierarchically organized classification and pharmacological properties, and to accommodate phylogenetic affiliations between aGPCR genes within mammals and across the animal kingdom.

Beyond the Flavour: The Potential Druggability of Chemosensory G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) belong to the largest class of drug targets. Approximately half of the members of the human GPCR superfamily are chemosensory receptors, including odorant receptors (ORs), trace amine-associated receptors (TAARs), bitter taste receptors (TAS2Rs), sweet and umami taste receptors (TAS1Rs). Interestingly, these chemosensory GPCRs (csGPCRs) are expressed in several tissues of the body where they are supposed to play a role in biological functions other than chemosensation. Despite their abundance and physiological/pathological relevance, the druggability of csGPCRs has been suggested but not fully characterized. Here, we aim to explore the potential of targeting csGPCRs to treat diseases by reviewing the current knowledge of csGPCRs expressed throughout the body and by analysing the chemical space and the drug-likeness of flavour molecules.

Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments

Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

Visualizing the GPCR Network: Classification and Evolution

In this study, we delineate an unsupervised clustering algorithm, minimum span clustering (MSC), and apply it to detect G-protein coupled receptor (GPCR) sequences and to study the GPCR network using a base dataset of 2770 GPCR and 652 non-GPCR sequences. High detection accuracy can be achieved with a proper dataset. The clustering results of GPCRs derived from MSC show a strong correlation between their sequences and functions. By comparing our level 1 MSC results with the GPCRdb classification, the consistency is 87.9% for the fourth level of GPCRdb, 89.2% for the third level, 98.4% for the second level, and 100% for the top level (the lowest resolution level of GPCRdb). The MSC results of GPCRs can be well explained by estimating the selective pressure of GPCRs, as exemplified by investigating the largest two subfamilies, peptide receptors (PRs) and olfactory receptors (ORs), in class A GPCRs. PRs are decomposed into three groups due to a positive selective pressure, whilst ORs remain as a single group due to a negative selective pressure. Finally, we construct and compare phylogenetic trees using distance-based and character-based methods, a combination of which could convey more comprehensive information about the evolution of GPCRs.

G protein-coupled receptors: signalling and regulation by lipid agonists for improved glucose homoeostasis

G protein-coupled receptors (GPCRs) play a pivotal role in cell signalling, controlling many processes such as immunity, growth, cellular differentiation, neurological pathways and hormone secretions. Fatty acid agonists are increasingly recognised as having a key role in the regulation of glucose homoeostasis via stimulation of islet and gastrointestinal GPCRs. Downstream cell signalling results in modulation of the biosynthesis, secretion, proliferation and anti-apoptotic pathways of islet and enteroendocrine cells. GPR40 and GPR120 are activated by long-chain fatty acids (>C12) with both receptors coupling to the Gαq subunit that activates the Ca(2+)-dependent pathway. GPR41 and GPR43 are stimulated by short-chain fatty acids (C2-C5), and activation results in binding to Gαi that inhibits the adenylyl cyclase pathway attenuating cAMP production. In addition, GPR43 also couples to the Gαq subunit augmenting intracellular Ca(2+) and activating phospholipase C. GPR55 is specific for cannabinoid endogenous agonists (endocannabinoids) and non-cannabinoid fatty acids, which couples to Gα12/13 and Gαq proteins, leading to enhancing intracellular Ca(2+), extracellular signal-regulated kinase 1/2 (ERK) phosphorylation and Rho kinase. GPR119 is activated by fatty acid ethanolamides and binds to Gαs utilising the adenylate cyclase pathway, which is dependent upon protein kinase A. Current research indicates that GPCR therapies may be approved for clinical use in the near future. This review focuses on the recent advances in preclinical diabetes research in the signalling and regulation of GPCRs on islet and enteroendocrine cells involved in glucose homoeostasis.

Comparing Class A GPCRs to bitter taste receptors: Structural motifs, ligand interactions and agonist-to-antagonist ratios

G protein-coupled receptors (GPCRs) are seven transmembrane (TM) proteins that play a key role in human physiology. The GPCR superfamily comprises about 800 members, classified into several classes, with rhodopsin-like Class A being the largest and most studied thus far. A huge component of the human repertoire consists of the chemosensory GPCRs, including ∼400 odorant receptors, 25 bitter taste receptors (TAS2Rs), which are thought to guard the organism from consuming poisons, and sweet and umami TAS1R heteromers, which indicate the nutritive value of food. The location of the binding site of TAS2Rs is similar to that of Class A GPCRs. However, most of the known bitter ligands are agonists, with only a few antagonists documented thus far. The agonist-to-antagonist ratios of Class A GPCRs vary, but in general are much lower than for TAS2Rs. For a set of well-studied GPCRs, a gradual change in agonists-to-antagonists ratios is observed when comparing low (10 μM)- and high (10 nM)-affinity ligand sets from ChEMBL and the DrugBank set of drugs. This shift reflects pharmaceutical bias toward the therapeutically desirable pharmacology for each of these GPCRs, while the 10 μM sets possibly represent the native tendency of the receptors toward either agonists or antagonists. Analyzing ligand-GPCR interactions in 56 X-ray structures representative of currently available structural data, we find that the N-terminus, TM1 and TM2 are more involved in binding of antagonists than of agonists. On the other hand, ECL2 tends to be more involved in binding of agonists. This is of interest, since TAS2Rs harbor variations on the typical Class A sequence motifs, including the absence of the ECL2-TM3 disulfide bridge. This suggests an alternative mode of regulation of conformational states for TAS2Rs, with potentially less stabilized inactive state. The comparison of TAS2Rs and Class A GPCRs structural features and the pharmacology of the their ligands highlights the intricacies of GPCR architecture and provides a framework for rational design of new ligands.

Applying random forest and subtractive fuzzy c-means clustering techniques for the development of a novel G protein-coupled receptor discrimination method using pseudo amino acid compositions

G protein-coupled receptors (GPCRs) constitute the largest superfamily of integral membrane proteins (IMPs) and they tremendously contribute in the flow of information into cells. In this study, the random forest (RF) and the subtractive fuzzy c-means clustering (SBC) methods have been used to determine the importance of input variables and discriminate GPCRs from non-GPCRs using twenty amino acid and fifty pseudo amino acid compositions derived from GPCR sequences. The studied dataset was retrieved from the UniProt/SWISSPROT database and consists of 1000 GPCR and 1000 non-GPCR reviewed sequences. The top ranked RF-SBC-based model discriminates GPCRs and non-GPCRs successfully with the accuracy, sensitivity, specificity and Matthew's coefficient correlation (MCC) rates of 99.15%, 99.60%, 98.70% and 0.983%, respectively. These rates were obtained from averaged values of 5-fold cross validation using only twenty four out of fifty pseudo amino acid composition features. The results show that the proposed RF-SBC-based model outperforms other existing algorithms in terms of the evaluated performance criteria. The webserver for the proposed algorithm is available at http://brcinfo.shinyapps.io/GPCRIden.

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 .

Bioinformatics tools for predicting GPCR gene functions

The automatic classification of GPCRs by bioinformatics methodology can provide functional information for new GPCRs in the whole 'GPCR proteome' and this information is important for the development of novel drugs. Since GPCR proteome is classified hierarchically, general ways for GPCR function prediction are based on hierarchical classification. Various computational tools have been developed to predict GPCR functions; those tools use not simple sequence searches but more powerful methods, such as alignment-free methods, statistical model methods, and machine learning methods used in protein sequence analysis, based on learning datasets. The first stage of hierarchical function prediction involves the discrimination of GPCRs from non-GPCRs and the second stage involves the classification of the predicted GPCR candidates into family, subfamily, and sub-subfamily levels. Then, further classification is performed according to their protein-protein interaction type: binding G-protein type, oligomerized partner type, etc. Those methods have achieved predictive accuracies of around 90 %. Finally, I described the future subject of research of the bioinformatics technique about functional prediction of GPCR.

Modelling and enhanced molecular dynamics to steer structure-based drug discovery

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes.

Receptor-mediated signaling in Aspergillus fumigatus

Aspergillus fumigatus is the most pathogenic species among the Aspergilli, and the major fungal agent of human pulmonary infection. To prosper in diverse ecological niches, Aspergilli have evolved numerous mechanisms for adaptive gene regulation, some of which are also crucial for mammalian infection. Among the molecules which govern such responses, integral membrane receptors are thought to be the most amenable to therapeutic modulation. This is due to the localization of these molecular sensors at the periphery of the fungal cell, and to the prevalence of small molecules and licensed drugs which target receptor-mediated signaling in higher eukaryotic cells. In this review we highlight the progress made in characterizing receptor-mediated environmental adaptation in A. fumigatus and its relevance for pathogenicity in mammals. By presenting a first genomic survey of integral membrane proteins in this organism, we highlight an abundance of putative seven transmembrane domain (7TMD) receptors, the majority of which remain uncharacterized. Given the dependency of A. fumigatus upon stress adaptation for colonization and infection of mammalian hosts, and the merits of targeting receptor-mediated signaling as an antifungal strategy, a closer scrutiny of sensory perception and signal transduction in this organism is warranted.

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.

Classification of GPCRs using family specific motifs

The classification of G-Protein Coupled Receptor (GPCR) sequences is an important problem that arises from the need to close the gap between the large number of orphan receptors and the relatively small number of annotated receptors. Equally important is the characterization of GPCR Class A subfamilies and gaining insight into the ligand interaction since GPCR Class A encompasses a very large number of drug-targeted receptors. In this work, we propose a method for Class A subfamily classification using sequence-derived motifs which characterizes the subfamilies by discovering receptor-ligand interaction sites. The motifs that best characterize a subfamily are selected by the Distinguishing Power Evaluation (DPE) technique we propose. The experiments performed on GPCR sequence databases show that our method outperforms state-of-the-art classification techniques for GPCR Class A subfamily prediction. An important contribution of our work is to discover key receptor-ligand interaction sites which is very important for drug design.

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.

7TMRmine: a Web server for hierarchical mining of 7TMR proteins

BACKGROUND

Seven-transmembrane region-containing receptors (7TMRs) play central roles in eukaryotic signal transduction. Due to their biomedical importance, thorough mining of 7TMRs from diverse genomes has been an active target of bioinformatics and pharmacogenomics research. The need for new and accurate 7TMR/GPCR prediction tools is paramount with the accelerated rate of acquisition of diverse sequence information. Currently available and often used protein classification methods (e.g., profile hidden Markov Models) are highly accurate for identifying their membership information among already known 7TMR subfamilies. However, these alignment-based methods are less effective for identifying remote similarities, e.g., identifying proteins from highly divergent or possibly new 7TMR families. In this regard, more sensitive (e.g., alignment-free) methods are needed to complement the existing protein classification methods. A better strategy would be to combine different classifiers, from more specific to more sensitive methods, to identify a broader spectrum of 7TMR protein candidates.

DESCRIPTION

We developed a Web server, 7TMRmine, by integrating alignment-free and alignment-based classifiers specifically trained to identify candidate 7TMR proteins as well as transmembrane (TM) prediction methods. This new tool enables researchers to easily assess the distribution of GPCR functionality in diverse genomes or individual newly-discovered proteins. 7TMRmine is easily customized and facilitates exploratory analysis of diverse genomes. Users can integrate various alignment-based, alignment-free, and TM-prediction methods in any combination and in any hierarchical order. Sixteen classifiers (including two TM-prediction methods) are available on the 7TMRmine Web server. Not only can the 7TMRmine tool be used for 7TMR mining, but also for general TM-protein analysis. Users can submit protein sequences for analysis, or explore pre-analyzed results for multiple genomes. The server currently includes prediction results and the summary statistics for 68 genomes.

CONCLUSION

7TMRmine facilitates the discovery of 7TMR proteins. By combining prediction results from different classifiers in a multi-level filtering process, prioritized sets of 7TMR candidates can be obtained for further investigation. 7TMRmine can be also used as a general TM-protein classifier. Comparisons of TM and 7TMR protein distributions among 68 genomes revealed interesting differences in evolution of these protein families among major eukaryotic phyla.