Sequence analysis reveals how G protein-coupled receptors transduce the signal to the G protein

In Silico Structure and Sequence Analysis of Bacterial Porins and Specific Diffusion Channels for Hydrophilic Molecules: Conservation, Multimericity and Multifunctionality

Diffusion channels are involved in the selective uptake of nutrients and form the largest outer membrane protein (OMP) family in Gram-negative bacteria. Differences in pore size and amino acid composition contribute to the specificity. Structure-based multiple sequence alignments shed light on the structure-function relations for all eight subclasses. Entropy-variability analysis results are correlated to known structural and functional aspects, such as structural integrity, multimericity, specificity and biological niche adaptation. The high mutation rate in their surface-exposed loops is likely an important mechanism for host immune system evasion. Multiple sequence alignments for each subclass revealed conserved residue positions that are involved in substrate recognition and specificity. An analysis of monomeric protein channels revealed particular sequence patterns of amino acids that were observed in other classes at multimeric interfaces. This adds to the emerging evidence that all members of the family exist in a multimeric state. Our findings are important for understanding the role of members of this family in a wide range of bacterial processes, including bacterial food uptake, survival and adaptation mechanisms.

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ∼75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.

ss-TEA: Entropy based identification of receptor specific ligand binding residues from a multiple sequence alignment of class A GPCRs

BACKGROUND

G-protein coupled receptors (GPCRs) are involved in many different physiological processes and their function can be modulated by small molecules which bind in the transmembrane (TM) domain. Because of their structural and sequence conservation, the TM domains are often used in bioinformatics approaches to first create a multiple sequence alignment (MSA) and subsequently identify ligand binding positions. So far methods have been developed to predict the common ligand binding residue positions for class A GPCRs.

RESULTS

Here we present 1) ss-TEA, a method to identify specific ligand binding residue positions for any receptor, predicated on high quality sequence information. 2) The largest MSA of class A non olfactory GPCRs in the public domain consisting of 13324 sequences covering most of the species homologues of the human set of GPCRs. A set of ligand binding residue positions extracted from literature of 10 different receptors shows that our method has the best ligand binding residue prediction for 9 of these 10 receptors compared to another state-of-the-art method.

CONCLUSIONS

The combination of the large multi species alignment and the newly introduced residue selection method ss-TEA can be used to rapidly identify subfamily specific ligand binding residues. This approach can aid the design of site directed mutagenesis experiments, explain receptor function and improve modelling. The method is also available online via GPCRDB at http://www.gpcr.org/7tm/.

GPCRDB: information system for G protein-coupled receptors

The GPCRDB is a Molecular Class-Specific Information System (MCSIS) that collects, combines, validates and disseminates large amounts of heterogeneous data on G protein-coupled receptors (GPCRs). The GPCRDB contains experimental data on sequences, ligand-binding constants, mutations and oligomers, as well as many different types of computationally derived data such as multiple sequence alignments and homology models. The GPCRDB provides access to the data via a number of different access methods. It offers visualization and analysis tools, and a number of query systems. The data is updated automatically on a monthly basis. The GPCRDB can be found online at http://www.gpcr.org/7tm/.

Distinct binding mode of 125I-AngII to AT1 receptor without the Cys18-Cys274 disulfide bridge

Previous studies on angiotensin II (AngII) AT(1) receptor function have revealed that the N-terminal residues of AngII may modulate receptor activation by binding at the receptor extracellular site. A remarkable feature of this site is an insertion of 8 amino acids in the middle of the EC-3 loop including the Cys(274) residue that supposedly makes a disulfide bond with N-terminal Cys(18). As demonstrated by assays with Del(267-275)AT(1), the role of the Cys(18)-Cys(274) disulfide bridge is to keep a conformation of the inserted residues that allows a normal binding of the AngII N-terminal residues. C18S AT(1) receptor mutant, supposedly having a dissociated disulfide bridge, but an intact residue insertion, is constitutively activated and can less efficiently bind AngII. Similar results were observed when the S-S disulfide bond was disrupted in (C18S,C274S) AT(1) receptor. The importance of the free N-terminal amino group of Asp(1) and of the Arg(2) guanidino group for the binding of AngII to C18S mutant with EC-3 loop insertion was investigated by means of assays using AngII peptide analogues bearing a single mutation of Asp(1) for Sar(1) or Arg(2) for Lys(2), as ligands. This study showed that like AngII, [Sar(1)]-AngII can bind the C18S mutant receptor with low affinity whereas [Lys(2)]-AngII binding is still more reduced. Interestingly, when (125)I-AngII instead of (3)H-AngII was used, no significant binding of this mutant was observed although wild type AT(1) receptor was shown to bind all AngII analogues.

Computing highly correlated positions using mutual information and graph theory for G protein-coupled receptors

G protein-coupled receptors (GPCRs) are a superfamily of seven transmembrane-spanning proteins involved in a wide array of physiological functions and are the most common targets of pharmaceuticals. This study aims to identify a cohort or clique of positions that share high mutual information. Using a multiple sequence alignment of the transmembrane (TM) domains, we calculated the mutual information between all inter-TM pairs of aligned positions and ranked the pairs by mutual information. A mutual information graph was constructed with vertices that corresponded to TM positions and edges between vertices were drawn if the mutual information exceeded a threshold of statistical significance. Positions with high degree (i.e. had significant mutual information with a large number of other positions) were found to line a well defined inter-TM ligand binding cavity for class A as well as class C GPCRs. Although the natural ligands of class C receptors bind to their extracellular N-terminal domains, the possibility of modulating their activity through ligands that bind to their helical bundle has been reported. Such positions were not found for class B GPCRs, in agreement with the observation that there are not known ligands that bind within their TM helical bundle. All identified key positions formed a clique within the MI graph of interest. For a subset of class A receptors we also considered the alignment of a portion of the second extracellular loop, and found that the two positions adjacent to the conserved Cys that bridges the loop with the TM3 qualified as key positions. Our algorithm may be useful for localizing topologically conserved regions in other protein families.

Privileged Structures in GPCRs

Certain kinds of ligand substructures recur frequently in pharmacologically successful synthetic compounds. For this reason they are called privileged structures. In seeking an explanation for this phenomenon, it is observed that the privileged structure represents a generic substructure that matches commonly recurring conserved structural motifs in the target proteins, which may otherwise be quite diverse in sequence and function. Using sequence-handling tools, it is possible to identify which other receptors may respond to the ligand, as dictated on the one hand by the nature of the privileged substructure itself or by the rest of the ligand in which a more specific message resides. It is suggested that privileged structures interact with the partially exposed receptor machinery responsible for the switch between the active and inactive states. Depending on how they have been designed to interact, one can predispose these substructures to favour either one state or the other; thus privileged structures can be used to create either agonists or antagonists. In terms of the mechanism of recognition, the region that the privileged structures bind to are rich in aromatic residues, which explains the prevalence of aromatic groups and atoms such as sulphur or halogens in many of the ligands. Finally, the approach described here can be used to design drugs for orphan receptors whose function has not yet been established experimentally.

The Angiotensin II AT1 Receptor Structure-Activity Correlations in the Light of Rhodopsin Structure

The most prevalent physiological effects of ANG II, the main product of the renin-angiotensin system, are mediated by the AT1 receptor, a rhodopsin-like AGPCR. Numerous studies of the cardiovascular effects of synthetic peptide analogs allowed a detailed mapping of ANG II's structural requirements for receptor binding and activation, which were complemented by site-directed mutagenesis studies on the AT1 receptor to investigate the role of its structure in ligand binding, signal transduction, phosphorylation, binding to arrestins, internalization, desensitization, tachyphylaxis, and other properties. The knowledge of the high-resolution structure of rhodopsin allowed homology modeling of the AT1 receptor. The models thus built and mutagenesis data indicate that physiological (agonist binding) or constitutive (mutated receptor) activation may involve different degrees of expansion of the receptor's central cavity. Residues in ANG II structure seem to control these conformational changes and to dictate the type of cytosolic event elicited during the activation. 1) Agonist aromatic residues (Phe8 and Tyr4) favor the coupling to G protein, and 2) absence of these residues can favor a mechanism leading directly to receptor internalization via phosphorylation by specific kinases of the receptor's COOH-terminal Ser and Thr residues, arrestin binding, and clathrin-dependent coated-pit vesicles. On the other hand, the NH2-terminal residues of the agonists ANG II and [Sar1]-ANG II were found to bind by two distinct modes to the AT1 receptor extracellular site flanked by the COOH-terminal segments of the EC-3 loop and the NH2-terminal domain. Since the [Sar1]-ligand is the most potent molecule to trigger tachyphylaxis in AT1 receptors, it was suggested that its corresponding binding mode might be associated with this special condition of receptors.

Role of lysine187 within the second extracellular loop of the type A cholecystokinin receptor in agonist-induced activation. Use of complementary charge-reversal mutagenesis to define a functionally important interdomain interaction

Activation of guanine nucleotide-binding protein (G protein)-coupled receptors is believed to involve conformational change that exposes a domain for G protein coupling at the cytosolic surface of the helical confluence, although the mechanisms for achieving this are not well understood. This conformational change can be achieved by docking a diverse variety of agonist ligands, known to occur by interacting with different regions of these receptors. In this study, we focus on the importance of a specific basic residue (Lys187) within the second extracellular loop of the receptor for the peptide hormone, cholecystokinin. Alanine-replacement and charge-reversal mutagenesis of this residue showed that it had no effect on the binding of natural peptide and nonpeptidyl ligands of this receptor but markedly interfered with agonist-stimulated signaling. It was demonstrated that this negative effect on biological activity could be eliminated with the truncation of the first 30 residues of the amino-terminal tail of this receptor. Complementary charge-reversal mutagenesis of each of the five conserved acidic residues within this region of the receptor in the presence of the charge-reversed Lys187 revealed that only the Asp5 mutant fully reversed the negative functional impact of the Lys187 charge reversal. Thus, we have demonstrated that a basic residue within the second extracellular loop of the cholecystokinin receptor interacts with a specific acidic residue within the amino terminus of this receptor. This residue-residue interaction is nicely accommodated within a new molecular model of the agonist-occupied cholecystokinin receptor.

Modeling GPCRs

Many GPCR models have been built over the years for many different purposes, of which drug-design undoubtedly has been the most frequent one. The release of the structure of bovine rhodopsin in August 2000 enabled us to analyze models built before that period to learn things for the models we build today. We conclude that the GPCR modeling field is riddled with "common knowledge". Several characteristics of the bovine rhodopsin structure came as a big surprise, and had obviously not been predicted, which led to large errors in the models. Some of these surprises, however, could have been predicted if the modelers had more rigidly stuck to the rule that holds for all models, namely that a model should explain all experimental facts, and not just those facts that agree with the modeler's preconceptions.

TreeDet: a web server to explore sequence space

The TreeDet (Tree Determinant) Server is the first release of a system designed to integrate results from methods that predict functional sites in protein families. These methods take into account the relation between sequence conservation and evolutionary importance. TreeDet fully analyses the space of protein sequences in either user-uploaded or automatically generated multiple sequence alignments. The methods implemented in the server represent three main classes of methods for the detection of family-dependent conserved positions, a tree-based method, a correlation based method and a method that employs a principal component analyses coupled to a cluster algorithm. An additional method is provided to highlight the reliability of the position in the alignments. The server is available at .

A two-entropies analysis to identify functional positions in the transmembrane region of class A G protein-coupled receptors

Residues in the transmembrane region of G protein-coupled receptors (GPCRs) are important for ligand binding and activation, but the function of individual positions is poorly understood. Using a sequence alignment of class A GPCRs (grouped in subfamilies), we propose a so-called "two-entropies analysis" to determine the potential role of individual positions in the transmembrane region of class A GPCRs. In our approach, such positions appear scattered, while largely clustered according to their biological function. Our method appears superior when compared to other bioinformatics approaches, such as the evolutionary trace method, entropy-variability plot, and correlated mutation analysis, both qualitatively and quantitatively.

Role of the Cys18–Cys274 disulfide bond and of the third extracellular loop in the constitutive activation and internalization of angiotensin II type 1 receptor

An insertion of residues in the third extracellular loop and a disulfide bond linking this loop to the N-terminal domain were identified in a structural model of a G-protein coupled receptor specific to angiotensin II (AT1 receptor), built in homology to the seven-transmembrane-helix bundle of rhodopsin. Both the insertion and the disulfide bond were located close to an extracellular locus, flanked by the second extracellular loop (EC-2), the third extracellular loop (EC-3) and the N-terminal domain of the receptor; they contained residues identified by mutagenesis studies to bind the angiotensin II N-terminal segment (residues D1 and R2). It was postulated that the insertion and the disulfide bond, also found in other receptors such as those for bradykinin, endothelin, purine and other ligands, might play a role in regulating the function of the AT1 receptor. This possibility was investigated by assaying AT1 forms devoid of the insertion and with mutations to Ser on both positions of Cys residues forming the disulfide bond. Binding and activation experiments showed that abolition of this bond led to constitutive activation, decay of agonist binding and receptor activation levels. Furthermore, the receptors thus mutated were translocated to cytosolic environments including those in the nucleus. The receptor form with full deletion of the EC-3 loop residue insertion, displayed a wild type receptor behavior.

GRIS: glycoprotein-hormone receptor information system

The glycoprotein-hormone receptor information system (GRIS) presents a comprehensive view on all available molecular data for the lutropin/choriogonadotropin receptor, follitropin receptor, and thyrotropin receptor G protein-coupled receptors. It features a mutation database presently containing 696 point mutations, combined with all sequences and the associated homology models. The mutation information was automatically extracted from the literature and manually augmented with respect to constitutivity, surface expression, sensitivity to hormones, and binding affinity. All information in this integrated system is presented in a G protein-coupled receptor specialist-friendly way. A series of interactive tools such as rotamer analysis, mutation prediction, or cavity visualization aids with the design and interpretation of experiments. A universal residue numbering system has been introduced to ease database searches as well as the use of the information in conjunction with literature data from diverse origins. Users can upload new mutations. GRIS is freely accessible at http://gris.ulb.ac.be/.

The study of G-protein coupled receptor oligomerization with computational modeling and bioinformatics

To achieve a structural context for the analysis of G-protein coupled receptor (GPCR) oligomers, molecular modeling must be used to predict the corresponding interaction interfaces. The task is complicated by the paucity of detailed structural data at atomic resolution, and the large number of possible modes in which the bundles of seven transmembrane (TM) segments of the interacting GPCR monomers can be packed together into dimers and/or higher-order oligomers. Approaches and tools offered by bioinformatics can be used to reduce the complexity of this task and, combined with computational modeling, can serve to yield testable predictions for the structural properties of oligomers. Most of the bioinformatics methods take advantage of the evolutionary relation that exists among GPCRs, as expressed in their sequences and measurable in the common elements of their structural and functional features. These common elements are responsible for the presence of detectable patterns of motifs and correlated mutations evident from the alignment of the sequences of these complex biological systems. The decoding of these patterns in terms of structural and functional determinants can provide indications about the most likely interfaces of dimerization/oligomerization of GPCRs. We review here the main approaches from bioinformatics, enhanced by computational molecular modeling, that have been used to predict likely interfaces of dimerization/oligomerization of GPCRs, and compare results from their application to rhodopsin-like GPCRs. A compilation of the most frequently predicted GPCR oligomerization interfaces points to specific regions of TMs 4-6.

Predicting functional sites with an automated algorithm suitable for heterogeneous datasets

Background

In a previous report (La et al., Proteins, 2005), we have demonstrated that the identification of phylogenetic motifs, protein sequence fragments conserving the overall familial phylogeny, represent a promising approach for sequence/function annotation. Across a structurally and functionally heterogeneous dataset, phylogenetic motifs have been demonstrated to correspond to a wide variety of functional site archetypes, including those defined by surface loops, active site clefts, and less exposed regions. However, in our original demonstration of the technique, phylogenetic motif identification is dependent upon a manually determined similarity threshold, prohibiting large-scale application of the technique.

Results

In this report, we present an algorithmic approach that determines thresholds without human subjectivity. The approach relies on significant raw data preprocessing to improve signal detection. Subsequently, Partition Around Medoids Clustering (PAMC) of the similarity scores assesses sequence fragments where functional annotation remains in question. The accuracy of the approach is confirmed through comparisons to our previous (manual) results and structural analyses. Triosephosphate isomerase and arginyl-tRNA synthetase are discussed as exemplar cases. A quantitative functional site prediction assessment algorithm indicates that the phylogenetic motif predictions, which require sequence information only, are nearly as good as those from evolutionary trace methods that do incorporate structure.

Conclusion

The automated threshold detection algorithm has been incorporated into MINER, our web-based phylogenetic motif identification server. MINER is freely available on the web at . Pre-calculated functional site predictions of the COG database and an implementation of the threshold detection algorithm, in the R statistical language, can also be accessed at the website.

A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models

BACKGROUND

G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligands interact and activate GPCRs, leading to signal transduction within cells. Most of these responses are mediated through the interaction of GPCRs with heterotrimeric GTP-binding proteins (G-proteins). Due to the information explosion in biological sequence databases, the development of software algorithms that could predict properties of GPCRs is important. Experimental data reported in the literature suggest that heterotrimeric G-proteins interact with parts of the activated receptor at the transmembrane helix-intracellular loop interface. Utilizing this information and membrane topology information, we have developed an intensive exploratory approach to generate a refined library of statistical models (Hidden Markov Models) that predict the coupling preference of GPCRs to heterotrimeric G-proteins. The method predicts the coupling preferences of GPCRs to Gs, Gi/o and Gq/11, but not G12/13 subfamilies.

RESULTS

Using a dataset of 282 GPCR sequences of known coupling preference to G-proteins and adopting a five-fold cross-validation procedure, the method yielded an 89.7% correct classification rate. In a validation set comprised of all receptor sequences that are species homologues to GPCRs with known coupling preferences, excluding the sequences used to train the models, our method yields a correct classification rate of 91.0%. Furthermore, promiscuous coupling properties were correctly predicted for 6 of the 24 GPCRs that are known to interact with more than one subfamily of G-proteins.

CONCLUSION

Our method demonstrates high correct classification rate. Unlike previously published methods performing the same task, it does not require any transmembrane topology prediction in a preceding step. A web-server for the prediction of GPCRs coupling specificity to G-proteins available for non-commercial users is located at http://bioinformatics.biol.uoa.gr/PRED-COUPLE.

A family-based approach reveals the function of residues in the nuclear receptor ligand-binding domain

Literature studies, 3D structure data, and a series of sequence analysis techniques were combined to reveal important residues in the structure and function of the ligand-binding domain of nuclear hormone receptors. A structure-based multiple sequence alignment allowed for the seamless combination of data from many different studies on different receptors into one single functional model. It was recently shown that a combined analysis of sequence entropy and variability can divide residues in five classes; (1) the main function or active site, (2) support for the main function, (3) signal transduction, (4) modulator or ligand binding and (5) the rest. Mutation data extracted from the literature and intermolecular contacts observed in nuclear receptor structures were analyzed in view of this classification and showed that the main function or active site residues of the nuclear receptor ligand-binding domain are involved in cofactor recruitment. Furthermore, the sequence entropy-variability analysis identified the presence of signal transduction residues that are located between the ligand, cofactor and dimer sites, suggesting communication between these regulatory binding sites. Experimental and computational results agreed well for most residues for which mutation data and intermolecular contact data were available. This allows us to predict the role of the residues for which no functional data is available yet. This study illustrates the power of family-based approaches towards the analysis of protein function, and it points out the problems and possibilities presented by the massive amounts of data that are becoming available in the "omics era". The results shed light on the nuclear receptor family that is involved in processes ranging from cancer to infertility, and that is one of the more important targets in the pharmaceutical industry.

Inferring Property Selection Pressure from Positional Residue Conservation

In this study, we attempt to understand and explain positional selection pressure in terms of underlying physical and chemical properties. We propose a set of constraining assumptions about how these pressures behave, then describe a procedure for analysing and explaining the distribution of residues at a particular position in a multiple sequence alignment. In contrast to previous approaches, our model takes into account both amino acid frequencies and a large number of physical-chemical properties. By analysing each property separately, it is possible to identify positions where distinct conservation patterns are present. In addition, the model can easily incorporate sequence weights that adjust for bias in the sample sequences. Finally, a test of statistical significance is provided for our conservation measure. The applicability of this method is demonstrated on two HIV-1 proteins: Nef and Env. The tools, data and results presented in this article are available at http://flan.blm.cs.cmu.edu.

Evolutionary analysis of rhodopsin and cone pigments: connecting the three-dimensional structure with spectral tuning and signal transfer

Extensive sequence data and structural sampling of expressed proteins from different species lead to the idea that entire molecules or specific domain folds belong to large superfamilies of proteins. A subset of G protein-coupled receptors, one of the largest families involved in cellular signaling, rod and cone opsins are involved in phototransduction in photoreceptor cells. Here, the evolutionary analysis of opsin sequences and structures predicts key residues involved in the transmission of the signal from the binding site of the chromophore to the cytoplasmic surface and residues that are involved in the spectral tuning of opsins to short wavelengths of light.