Automatic extraction of protein point mutations using a graph bigram association

Protein point mutations are an essential component of the evolutionary and experimental analysis of protein structure and function. While many manually curated databases attempt to index point mutations, most experimentally generated point mutations and the biological impacts of the changes are described in the peer-reviewed published literature. We describe an application, Mutation GraB (Graph Bigram), that identifies, extracts, and verifies point mutations from biomedical literature. The principal problem of point mutation extraction is to link the point mutation with its associated protein and organism of origin. Our algorithm uses a graph-based bigram traversal to identify these relevant associations and exploits the Swiss-Prot protein database to verify this information. The graph bigram method is different from other models for point mutation extraction in that it incorporates frequency and positional data of all terms in an article to drive the point mutation–protein association. Our method was tested on 589 articles describing point mutations from the G protein–coupled receptor (GPCR), tyrosine kinase, and ion channel protein families. We evaluated our graph bigram metric against a word-proximity metric for term association on datasets of full-text literature in these three different protein families. Our testing shows that the graph bigram metric achieves a higher F-measure for the GPCRs (0.79 versus 0.76), protein tyrosine kinases (0.72 versus 0.69), and ion channel transporters (0.76 versus 0.74). Importantly, in situations where more than one protein can be assigned to a point mutation and disambiguation is required, the graph bigram metric achieves a precision of 0.84 compared with the word distance metric precision of 0.73. We believe the graph bigram search metric to be a significant improvement over previous search metrics for point mutation extraction and to be applicable to text-mining application requiring the association of words.     

Classifying G protein-coupled receptors and nuclear receptors on the basis of protein power spectrum from fast Fourier transform

Summary. As the potential drug targets, G-protein coupled receptors (GPCRs) and nuclear receptors (NRs) are the focuses in pharmaceutical research. It is of great practical significance to develop an automated and reliable method to facilitate the identification of novel receptors. In this study, a method of fast Fourier transform-based support vector machine was proposed to classify GPCRs and NRs from the hydrophobicity of proteins. The models for all the GPCR families and NR subfamilies were trained and validated using jackknife test and the results thus obtained are quite promising. Meanwhile, the performance of the method was evaluated on GPCR and NR independent datasets with good performance. The good results indicate the applicability of the method. Two web servers implementing the prediction are available at and .     

Mutagenesis and peptide analysis of the DRY motif in the alpha2A adrenergic receptor: evidence for alternate mechanisms in G protein-coupled receptors

In G protein-coupled receptors (GPCRs), a conserved aspartic acid in the DRY motif at the cytoplasmic end of helix 3 regulates the transition to the active state, while the adjacent arginine is crucial for G protein activation. To examine the functions of these two residues, we made D130I and R131Q mutations in the alpha2A adrenergic receptor (AR). We demonstrate that, unlike other GPCRs, the alpha2A AR is not constitutively activated by the D130I mutation, although the mutation increases agonist affinity. While the R131Q mutation severely disrupts function, it decreases rather than increasing agonist affinity as seen in other GPCRs. We then investigated the molecular effects of the same mutations in a peptide model and showed that Arg131 is not required for peptide-mediated G protein activation. These results indicate that the alpha2A AR does not follow the conventional GPCR mechanistic paradigm with respect to the function of the DRY motif.     

Structural models for dimerization of G-protein coupled receptors: the opioid receptor homodimers

Among the most exciting functional features of G-protein coupled receptors (GPCRs) that are coming into focus lately are those relating to the role and structural characteristics of their oligomerization (mostly homo- and heterodimers). The structural underpinnings of these novel functional insights are still not clear, as current experimental techniques have not yet succeeded in identifying the dimerization interfaces between GPCR monomers. Two computational approaches have recently been designed in our lab to provide reasonable three-dimensional (3D) molecular models of the transmembrane (TM) regions of GPCR dimers based on a combination of the structural information of receptor monomers and analyses of correlated mutations in receptor families. The modeling of GPCR heterodimers has been described recently. We present here a related approach for modeling of GPCR homodimers that identifies the interfaces in the most likely configurations of the complexes. The approach is illustrated for the three cloned opioid receptor subtypes (OPRD, OPRM, and OPRK).     

The G protein-coupled receptors in the pufferfish <Emphasis Type="Italic">Takifugu rubripes</Emphasis>

Background

Guanine protein-coupled receptors (GPCRs) constitute a eukaryotic transmembrane protein family and function as “molecular switches” in the second messenger cascades and are found in all organisms between yeast and humans. They form the single, biggest drug-target family due to their versatility of action and their role in several physiological functions, being active players in detecting the presence of light, a variety of smells and tastes, amino acids, nucleotides, lipids, chemicals etc. in the environment of the cell. Comparative genomic studies on model organisms provide information on target receptors in humans and their function. The Japanese teleost Fugu has been identified as one of the smallest vertebrate genomes and a compact model to study the human genome, owing to the great similarity in its gene repertoire with that of human and other vertebrates. Thus the characterization of the GPCRs of Fugu would provide insights to the evolution of the vertebrate genome.

Results

We classified the GPCRs in the Fugu genome and our analysis of its 316 membrane-bound receptors, available on the public databases as well as from literature, detected 298 GPCRs that were grouped into five main families according to the GRAFS classification system (namely, Glutamate, Rhodopsin, Adhesion, Frizzled and Secretin). We also identified 18 other GPCRs that could not be grouped under the GRAFS family and hence were classified as ‘Other 7TM’ receptors. On comparison of the GPCR information from the Fugu genome with those in the human and chicken genomes, we detected 96.83% (306/316) and 96.51% (305/316) orthology in GPCRs among the Fugu-human genomes and Fugu-chicken genomes, respectively.

Conclusions

This study reveals the position of pisces in vertebrate evolution from the GPCR perspective. Fugu can act as a reference model for the human genome for other protein families as well, going by the high orthology observed for GPCRs between Fugu and human. The evolutionary comparison of GPCR sequences between key vertebrate classes of mammals, birds and fish will help in identifying key functional residues and motifs so as to fill in the blanks in the evolution of GPCRs in vertebrates.

     

The anatomy of mammalian sweet taste receptors

All sweet‐tasting compounds are detected by a single G‐protein coupled receptor (GPCR), the heterodimer T1R2‐T1R3, for which no experimental structure is available. The sweet taste receptor is a class C GPCR, and the recently published crystallographic structures of metabotropic glutamate receptor (mGluR) 1 and 5 provide a significant step forward for understanding structure‐function relationships within this family. In this article, we recapitulate more than 600 single point site‐directed mutations and available structural data to obtain a critical alignment of the sweet taste receptor sequences with respect to other class C GPCRs. Using this alignment, a homology 3D‐model of the human sweet taste receptor is built and analyzed to dissect out the role of key residues involved in ligand binding and those responsible for receptor activation. Proteins 2017; 85:332–341. © 2016 Wiley Periodicals, Inc.     

Classifying G-protein-coupled receptors to the finest subtype level

G-protein-coupled receptors (GPCRs) constitute a remarkable protein family of receptors that are involved in a broad range of biological processes. A large number of clinically used drugs elicit their biological effect via a GPCR. Thus, developing a reliable computational method for predicting the functional roles of GPCRs would be very useful in the pharmaceutical industry. Nowadays, researchers are more interested in functional roles of GPCRs at the finest subtype level. However, with the accumulation of many new protein sequences, none of the existing methods can completely classify these GPCRs to their finest subtype level. In this paper, a pioneer work was performed trying to resolve this problem by using a hierarchical classification method. The first level determines whether a query protein is a GPCR or a non-GPCR. If it is considered as a GPCR, it will be finally classified to its finest subtype level. GPCRs are characterized by 170 sequence-derived features encapsulating both amino acid composition and physicochemical features of proteins, and support vector machines are used as the classification engine. To test the performance of the present method, a non-redundant dataset was built which are organized at seven levels and covers more functional classes of GPCRs than existing datasets. The number of protein sequences in each level is 5956, 2978, 8079, 8680, 6477, 1580 and 214, respectively. By 5-fold cross-validation test, the overall accuracy of 99.56%, 93.96%, 82.81%, 85.93%, 94.1%, 95.38% and 92.06% were observed at each level. When compared with some previous methods, the present method achieved a consistently higher overall accuracy. The results demonstrate the power and effectiveness of the proposed method to accomplish the classification of GPCRs to the finest subtype level.     

GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble

G protein-coupled receptors (GPCRs) are transmembrane proteins, which transduce signals from extracellular ligands to intracellular G protein. Automatic classification of GPCRs can provide important information for the development of novel drugs in pharmaceutical industry. In this paper, we propose an evolutionary approach, GPCR-MPredictor, which combines individual classifiers for predicting GPCRs. GPCR-MPredictor is a web predictor that can efficiently predict GPCRs at five levels. The first level determines whether a protein sequence is a GPCR or a non-GPCR. If the predicted sequence is a GPCR, then it is further classified into family, subfamily, sub-subfamily, and subtype levels. In this work, our aim is to analyze the discriminative power of different feature extraction and classification strategies in case of GPCRs prediction and then to use an evolutionary ensemble approach for enhanced prediction performance. Features are extracted using amino acid composition, pseudo amino acid composition, and dipeptide composition of protein sequences. Different classification approaches, such as k-nearest neighbor (KNN), support vector machine (SVM), probabilistic neural networks (PNN), J48, Adaboost, and Naives Bayes, have been used to classify GPCRs. The proposed hierarchical GA-based ensemble classifier exploits the prediction results of SVM, KNN, PNN, and J48 at each level. The GA-based ensemble yields an accuracy of 99.75, 92.45, 87.80, 83.57, and 96.17% at the five levels, on the first dataset. We further perform predictions on a dataset consisting of 8,000 GPCRs at the family, subfamily, and sub-subfamily level, and on two other datasets of 365 and 167 GPCRs at the second and fourth levels, respectively. In comparison with the existing methods, the results demonstrate the effectiveness of our proposed GPCR-MPredictor in classifying GPCRs families. It is accessible at .     

Prediction of heterodimerization interfaces of G-protein coupled receptors with a new subtractive correlated mutation method

Recent studies employing differential epitope tagging, selective immunoprecipitation of receptor complexes and fluorescence or bioluminescence resonance energy transfer techniques provide direct evidence for heterodimerization between both closely and distantly related members of the G-protein coupled receptor (GPCR) family. Since heterodimerization appears to play a role in modulating agonist affinity, efficacy and/or trafficking properties, the molecular models of GPCRs required to understand receptor function must consider these oligomerization hypotheses. To advance knowledge in this field, we present here a computational approach based on correlated mutation analysis and the structural information contained in three-dimensional molecular models of the transmembrane regions of GPCRs built using the rhodopsin crystal structure as a template. The new subtractive correlated mutation method reveals likely heterodimerization interfaces amongst the different alternatives for the positioning of two tightly packed bundles of seven transmembrane domains next to each other in contact heterodimers of GPCRs. Predictions are applied to GPCRs in the class of opioid receptors. However, in the absence of a known structure of any GPCR dimer, the features of the method and predictions are also illustrated and analyzed for a dimeric complex of known structure.     

Conformational changes of G protein-coupled receptors during their activation by agonist binding

The superfamily of G protein-coupled receptors (GPCRs) is the largest and most diverse group of transmembrane proteins involved in signal transduction. Many of the over 1000 human GPCRs represent important pharmaceutical targets. However, despite high interest in this receptor family, no high-resolution structure of a human GPCR has been resolved yet. This is mainly due to difficulties in obtaining large quantities of pure and active protein. Until now, only a high-resolution x-ray structure of an inactive state of bovine rhodopsin is available. Since no structure of an active state has been solved, information of the GPCR activation process can be gained only by biophysical techniques. In this review, we first describe what is known about the ground state of GPCRs to then address questions about the nature of the conformational changes taking place during receptor activation and the mechanism controlling the transition from the resting to the active state. Finally, we will also address the question to what extent information about the three-dimensional GPCR structure can be included into pharmaceutical drug design programs.     

Classifying G-protein coupled receptors with support vector machines

MOTIVATION: The enormous amount of protein sequence data uncovered by genome research has increased the demand for computer software that can automate the recognition of new proteins. We discuss the relative merits of various automated methods for recognizing G-Protein Coupled Receptors (GPCRs), a superfamily of cell membrane proteins. GPCRs are found in a wide range of organisms and are central to a cellular signalling network that regulates many basic physiological processes. They are the focus of a significant amount of current pharmaceutical research because they play a key role in many diseases. However, their tertiary structures remain largely unsolved. The methods described in this paper use only primary sequence information to make their predictions. We compare a simple nearest neighbor approach (BLAST), methods based on multiple alignments generated by a statistical profile Hidden Markov Model (HMM), and methods, including Support Vector Machines (SVMs), that transform protein sequences into fixed-length feature vectors. RESULTS: The last is the most computationally expensive method, but our experiments show that, for those interested in annotation-quality classification, the results are worth the effort. In two-fold cross-validation experiments testing recognition of GPCR subfamilies that bind a specific ligand (such as a histamine molecule), the errors per sequence at the Minimum Error Point (MEP) were 13.7% for multi-class SVMs, 17.1% for our SVMtree method of hierarchical multi-class SVM classification, 25.5% for BLAST, 30% for profile HMMs, and 49% for classification based on nearest neighbor feature vector Kernel Nearest Neighbor (kernNN). The percentage of true positives recognized before the first false positive was 65% for both SVM methods, 13% for BLAST, 5% for profile HMMs and 4% for kernNN.     

Desensitization of herpesvirus-encoded G protein-coupled receptors

Members of the herpesvirus family, including human cytomegalovirus (HCMV) and Kaposi's sarcoma-associated herpesvirus (KSHV/HHV-8), encode G protein-coupled receptor (GPCR) homologs, which strongly activate classical G protein signal transduction networks within the cell. In animal models of herpesvirus infection, the viral GPCRs appear to play physiologically important roles by enabling viral replication within tropic tissues and by promoting reactivation from latency. While a number of studies have defined intracellular signaling pathways activated by herpesviral GPCRs, it remains unclear if their physiological function is subjected to the process of desensitization as observed for cellular GPCRs. G protein-coupled receptor kinases (GRK) and arrestin proteins have been recently implicated in regulating viral GPCR signaling; however, the role that these desensitization proteins play in viral GPCR function in vivo remains unknown. Here, we review what is currently known regarding viral GPCR desensitization and discuss potential biological ramifications of viral GPCR regulation by the host cell desensitization machinery.     

Comprehensive repertoire and phylogenetic analysis of the G protein-coupled receptors in human and mouse

Understanding differences in the repertoire of orthologous gene pairs is vital for interpretation of pharmacological and physiological experiments if conclusions are conveyed between species. Here we present a comprehensive dataset for G protein-coupled receptors (GPCRs) in both human and mouse with a phylogenetic road map. We performed systematic searches applying several search tools such as BLAST, BLAT, and Hidden Markov models and searches in literature data. We aimed to gather a full-length version of each human or mouse GPCR in only one copy referring to a single chromosomal position. Moreover, we performed detailed phylogenetic analysis of the transmembrane regions of the receptors to establish accurate orthologous pairs. The results show the identity of 495 mouse and 400 human functional nonolfactory GPCRs. Overall, 329 of the receptors are found in one-to-one orthologous pairs, while 119 mouse and 31 human receptors originate from species-specific expansions or deletions. The average percentage similarity of the orthologue pairs is 85%, while it varies between the main GRAFS families from an average of 59 to 94%. The orthologous pairs for the lipid-binding GPCRs had the lowest levels of conservation, while the biogenic amines had highest levels of conservation. Moreover, we searched for expressed sequence tags (ESTs) and identified more than 17,000 ESTs matching GPCRs in mouse and human, providing information about their expression patterns. On the whole, this is the most comprehensive study of the gene repertoire that codes for human and mouse GPCRs. The datasets are available for downloading.     

Evolutionary trace of G protein-coupled receptors reveals clusters of residues that determine global and class-specific functions

G protein-coupled receptor (GPCR) activation mediated by ligand-induced structural reorganization of its helices is poorly understood. To determine the universal elements of this conformational switch, we used evolutionary tracing (ET) to identify residue positions commonly important in diverse GPCRs. When mapped onto the rhodopsin structure, these trace residues cluster into a network of contacts from the retinal binding site to the G protein-coupling loops. Their roles in a generic transduction mechanism were verified by 211 of 239 published mutations that caused functional defects. When grouped according to the nature of the defects, these residues sub-divided into three striking sub-clusters: a trigger region, where mutations mostly affect ligand binding, a coupling region near the cytoplasmic interface to the G protein, where mutations affect G protein activation, and a linking core in between where mutations cause constitutive activity and other defects. Differential ET analysis of the opsin family revealed an additional set of opsin-specific residues, several of which form part of the retinal binding pocket, and are known to cause functional defects upon mutation. To test the predictive power of ET, we introduced novel mutations in bovine rhodopsin at a globally important position, Leu-79, and at an opsin-specific position, Trp-175. Both were functionally critical, causing constitutive G protein activation of the mutants and rapid loss of regeneration after photobleaching. These results define in GPCRs a canonical signal transduction mechanism where ligand binding induces conformational changes propagated through adjacent trigger, linking core, and coupling regions.     

Structure modeling of all identified G protein-coupled receptors in the human genome

G protein-coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global C(alpha) root-mean-squared deviation from native of 4.6 angstroms, with a root-mean-squared deviation in the transmembrane helix region of 2.1 angstroms. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. All predicted GPCR models are freely available for noncommercial users on our Web site (http://www.bioinformatics.buffalo.edu/GPCR).     

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.     

G protein-coupled time travel: evolutionary aspects of GPCR research

The common seven-transmembrane-domain (TMD) architecture of G protein-coupled receptors (GPCRs) has been preserved over a vast period of time, and highly conserved amino acid motifs and residues have evolved to establish ligand and signal transduction specificities. The mining of evolutionary data from sequenced genomes and targeted retrieved orthologs has proven helpful for understanding the physiological relevance of individual GPCRs and for interpreting the clinical significance of GPCR mutations in structural terms. Sequence analysis of GPCR pseudogenes, which are considered as genomic traces of past functions, as well as recent success in sequence analysis of GPCR genes from extinct species, provide further information. This review discusses recent advances and approaches aimed at developing a better understanding of GPCR biology based on evolutionary data.     

NMR structure of the second intracellular loop of the alpha 2A adrenergic receptor: evidence for a novel cytoplasmic helix

A major, unresolved question in signal transduction by G protein coupled receptors (GPCRs) is to understand how, at atomic resolution, a GPCR activates a G protein. A step toward answering this question was made with the determination of the high-resolution structure of rhodopsin; we now know the intramolecular interactions that characterize the resting conformation of a GPCR. To what degree does this structure represent a structural paradigm for other GPCRs, especially at the cytoplasmic surface where GPCR-G protein interaction occurs and where the sequence homology is low among GPCRs? To address this question, we performed NMR studies on approximately 35-residue-long peptides including the critical second intracellular loop (i2) of the alpha 2A adrenergic receptor (AR) and of rhodopsin. To stabilize the secondary structure of the peptide termini, 4-12 residues from the adjacent transmembrane helices were included and structures determined in dodecylphosphocholine micelles. We also characterized the effects on an alpha 2A AR peptide of a D130I mutation in the conserved DRY motif. Our results show that in contrast to the L-shaped loop in the i2 of rhodopsin, the i2 of the alpha 2A AR is predominantly helical, supporting the hypothesis that there is structural diversity within GPCR intracellular loops. The D130I mutation subtly modulates the helical structure. The spacing of nonpolar residues in i2 with helical periodicity is a predictor of helical versus loop structure. These data should lead to more accurate models of the intracellular surface of GPCRs and of receptor-mediated G protein activation.     

NR4A orphan nuclear receptors influence retinoic acid and docosahexaenoic acid signaling via up-regulation of fatty acid binding protein 5

The orphan nuclear receptor (NR) Nurr1 is expressed in the developing and adult nervous system and is also induced as an immediate early gene in a variety of cell types. In silico analysis of human promoters identified fatty acid binding protein 5 (FABP5), a protein shown to enhance retinoic acid-mediated PPARβ/δ signaling, as a potential Nurr1 target gene. Nurr1 has previously been implicated in retinoid signaling via its heterodimerization partner RXR. Since NRs are commonly involved in cross-regulatory control we decided to further investigate the regulatory relationship between Nurr1 and FABP5. FABP5 expression was up-regulated by Nurr1 and other NR4A NRs in HEK293 cells, and Nurr1 was shown to activate and bind to the FABP5 promoter, supporting that FABP5 is a direct downstream target of NR4A NRs. We also show that the RXR ligand docosahexaenoic acid (DHA) can induce nuclear translocation of FABP5. Moreover, via up-regulation of FABP5 Nurr1 can enhance retinoic acid-induced signaling of PPARβ/δ and DHA-induced activation of RXR. We also found that other members of the NR4A orphan NRs can up-regulate FABP5. Thus, our findings suggest that NR4A orphan NRs can influence signaling events of other NRs via control of FABP5 expression levels.     

Disease-causing mutation in GPR54 reveals the importance of the second intracellular loop for class A G-protein-coupled receptor function

The G-protein-coupled receptor (GPCR) GPR54 is essential for the development and maintenance of reproductive function in mammals. A point mutation (L148S) in the second intracellular loop (IL2) of GPR54 causes idiopathic hypogonadotropic hypogonadism, a disorder characterized by delayed puberty and infertility. Here, we characterize the molecular mechanism by which the L148S mutation causes disease and address the role of IL2 in Class A GPCR function. Biochemical, immunocytochemical, and pharmacological analysis demonstrates that the mutation does not affect the expression, ligand binding properties, or protein interaction network of GPR54. In contrast, diverse GPR54 functional responses are markedly inhibited by the L148S mutation. Importantly, the leucine residue at this position is highly conserved among class A GPCRs. Indeed, mutating the corresponding leucine of the alpha(1A)-AR recapitulates the effects observed with L148S GPR54, suggesting the critical importance of this hydrophobic IL2 residue for Class A GPCR functional coupling. Interestingly, co-immunoprecipitation studies indicate that L148S does not hinder the association of Galpha subunits with GPR54. However, fluorescence resonance energy transfer analysis strongly suggests that L148S impairs the ligand-induced catalytic activation of Galpha. Combining our data with a predictive Class A GPCR/Galpha model suggests that IL2 domains contain a conserved hydrophobic motif that, upon agonist stimulation, might stabilize the switch II region of Galpha. Such an interaction could promote opening of switch II of Galpha to facilitate GDP-GTP exchange and coupling to downstream signaling responses. Importantly, mutations that disrupt this key hydrophobic interface can manifest as human disease.