Systematic analysis of primary sequence domain segments for the discrimination between class C GPCR subtypes

G-protein-coupled receptors (GPCRs) are a large and diverse super-family of eukaryotic cell membrane proteins that play an important physiological role as transmitters of extracellular signal. In this paper, we investigate Class C, a member of this super-family that has attracted much attention in pharmacology. The limited knowledge about the complete 3D crystal structure of Class C receptors makes necessary the use of their primary amino acid sequences for analytical purposes. Here, we provide a systematic analysis of distinct receptor sequence segments with regard to their ability to differentiate between seven class C GPCR subtypes according to their topological location in the extracellular, transmembrane, or intracellular domains. We build on the results from the previous research that provided preliminary evidence of the potential use of separated domains of complete class C GPCR sequences as the basis for subtype classification. The use of the extracellular N-terminus domain alone was shown to result in a minor decrease in subtype discrimination in comparison with the complete sequence, despite discarding much of the sequence information. In this paper, we describe the use of Support Vector Machine-based classification models to evaluate the subtype-discriminating capacity of the specific topological sequence segments.Peer ReviewedPostprint (author's final draft

Structure and functional motifs of GCR1, the only plant protein with a GPCR fold?

Whether GPCRs exist in plants is a fundamental biological question. Interest in deorphanizing new G protein coupled receptors (GPCRs), arises because of their importance in signaling. Within plants, this is controversial as genome analysis has identified 56 putative GPCRs, including GCR1 which is reportedly a remote homologue to class A, B and E GPCRs. Of these, GCR2, is not a GPCR; more recently it has been proposed that none are, not even GCR1. We have addressed this disparity between genome analysis and biological evidence through a structural bioinformatics study, involving fold recognition methods, from which only GCR1 emerges as a strong candidate. To further probe GCR1, we have developed a novel helix alignment method, which has been benchmarked against the the class A – class B - class F GPCR alignments. In addition, we have presented a mutually consistent set of alignments of GCR1 homologues to class A, class B and class F GPCRs, and shown that GCR1 is closer to class A and /or class B GPCRs than class A, class B or class F GPCRs are to each other. To further probe GCR1, we have aligned transmembrane helix 3 of GCR1 to each of the 6 GPCR classes. Variability comparisons provide additional evidence that GCR1 homologues have the GPCR fold. From the alignments and a GCR1 comparative model we have identified motifs that are common to GCR1, class A, B and E GPCRs. We discuss the possibilities that emerge from this controversial evidence that GCR1 has a GPCR fol

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

Prediction and classification for GPCR sequences based on ligand specific features

Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research. Although thousands of GPCR sequences are known, many of them are orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 1 subfamilies of GPCRs, a novel method for obtaining class specific features, based on the existence of activating ligand specific patterns, has been developed and utilized for a majority voting classification. Exploiting the fact that there is a non-promiscuous relationship between the specific binding of GPCRs into their ligands and their functional classification, our method classifies Level 1 subfamilies of GPCRs with a high predictive accuracy between 99% and 87% in a three-fold cross validation test. The method also tells us which motifs are significant for class determination which has important design implications. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization

Global Analysis of Predicted G Protein-Coupled Receptor Genes in the Filamentous Fungus, Neurospora crassa.

G protein-coupled receptors (GPCRs) regulate facets of growth, development, and environmental sensing in eukaryotes, including filamentous fungi. The largest predicted GPCR class in these organisms is the Pth11-related, with members similar to a protein required for disease in the plant pathogen Magnaporthe oryzae. However, the Pth11-related class has not been functionally studied in any filamentous fungal species. Here, we analyze phenotypes in available mutants for 36 GPCR genes, including 20 Pth11-related, in the model filamentous fungus Neurospora crassa. We also investigate patterns of gene expression for all 43 predicted GPCR genes in available datasets. A total of 17 mutants (47%) possessed at least one growth or developmental phenotype. We identified 18 mutants (56%) with chemical sensitivity or nutritional phenotypes (11 uniquely), bringing the total number of mutants with at least one defect to 28 (78%), including 15 mutants (75%) in the Pth11-related class. Gene expression trends for GPCR genes correlated with the phenotypes observed for many mutants and also suggested overlapping functions for several groups of co-transcribed genes. Several members of the Pth11-related class have phenotypes and/or are differentially expressed on cellulose, suggesting a possible role for this gene family in plant cell wall sensing or utilization

Functional classification of G-Protein coupled receptors, based on their specific ligand coupling patterns

Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research. Although thousands of GPCR sequences are known, many of them re- main as orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 2 subfamilies of Amine GPCRs, a novel method for obtaining fixed-length feature vectors, based on the existence of activating ligand specific patterns, has been developed and utilized for a Support Vector Machine (SVM)-based classification. Exploiting the fact that there is a non-promiscuous relationship between the specific binding of GPCRs into their ligands and their functional classification, our method classifies Level 2 subfamilies of Amine GPCRs with a high predictive accuracy of 97.02% in a ten-fold cross validation test. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization

The G protein-coupled receptor heterodimer network (GPCR-HetNet) and its hub components

G protein-coupled receptors (GPCRs) oligomerization has emerged as a vital characteristic of receptor structure. Substantial experimental evidence supports the existence of GPCR-GPCR interactions in a coordinated and cooperative manner. However, despite the current development of experimental techniques for large-scale detection of GPCR heteromers, in order to understand their connectivity it is necessary to develop novel tools to study the global heteroreceptor networks. To provide insight into the overall topology of the GPCR heteromers and identify key players, a collective interaction network was constructed. Experimental interaction data for each of the individual human GPCR protomers was obtained manually from the STRING and SCOPUS databases. The interaction data were used to build and analyze the network using Cytoscape software. The network was treated as undirected throughout the study. It is comprised of 156 nodes, 260 edges and has a scale-free topology. Connectivity analysis reveals a significant dominance of intrafamily versus interfamily connections. Most of the receptors within the network are linked to each other by a small number of edges. DRD2, OPRM, ADRB2, AA2AR, AA1R, OPRK, OPRD and GHSR are identified as hubs. In a network representation 10 modules/clusters also appear as a highly interconnected group of nodes. Information on this GPCR network can improve our understanding of molecular integration. GPCR-HetNet has been implemented in Java and is freely available at http://www.iiia.csic.es/similar to ismel/GPCR-Nets/index.html

Identification of novel post-transcriptional features in olfactory receptor family mRNAs.

Olfactory receptor (Olfr) genes comprise the largest gene family in mice. Despite their importance in olfaction, how most Olfr mRNAs are regulated remains unexplored. Using RNA-seq analysis coupled with analysis of pre-existing databases, we found that Olfr mRNAs have several atypical features suggesting that post-transcriptional regulation impacts their expression. First, Olfr mRNAs, as a group, have dramatically higher average AU-content and lower predicted secondary structure than do control mRNAs. Second, Olfr mRNAs have a higher density of AU-rich elements (AREs) in their 3'UTR and upstream open reading frames (uORFs) in their 5 UTR than do control mRNAs. Third, Olfr mRNAs have shorter 3' UTR regions and with fewer predicted miRNA-binding sites. All of these novel properties correlated with higher Olfr expression. We also identified striking differences in the post-transcriptional features of the mRNAs from the two major classes of Olfr genes, a finding consistent with their independent evolutionary origin. Together, our results suggest that the Olfr gene family has encountered unusual selective forces in neural cells that have driven them to acquire unique post-transcriptional regulatory features. In support of this possibility, we found that while Olfr mRNAs are degraded by a deadenylation-dependent mechanism, they are largely protected from this decay in neural lineage cells

Finding class C GPCR subtype-discriminating n-grams through feature selection

G protein-coupled receptors (GPCRs) are a large and heterogeneous superfamily of receptors that are key cell players for their role as extracellular signal transmitters. Class C GPCRs, in particular, are of great interest in pharmacology. The lack of knowledge about their full 3-D structure prompts the use of their primary amino acid sequences for the construction of robust classifiers, capable of discriminating their different subtypes. In this paper, we describe the use of feature selection techniques to build Support Vector Machine (SVM)-based classification models from selected receptor subsequences described as n-grams. We show that this approach to classification is useful for finding class C GPCR subtype-specific motifs.Peer ReviewedPostprint (author’s final draft