First principles predictions of the structure and function of g-protein-coupled receptors: validation for bovine rhodopsin

Accurate Hellmann-Feynman forces from density functional calculations with augmented Gaussian basis sets

The Hellmann-Feynman (HF) theorem provides a way to compute forces directly from the electron density, enabling efficient force calculations for large systems through machine learning (ML) models for the electron density. The main issue holding back the general acceptance of the HF approach for atom-centered basis sets is the well-known Pulay force which, if naively discarded, typically constitutes an error upward of 10 eV/Å in forces. In this work, we demonstrate that if a suitably augmented Gaussian basis set is used for density functional calculations, the Pulay force can be suppressed, and HF forces can be computed as accurately as analytical forces with state-of-the-art basis sets, allowing geometry optimization and molecular dynamics to be reliably performed with HF forces. Our results pave a clear path forward for the accurate and efficient simulation of large systems using ML densities and the HF theorem.

OMPcontact: An Outer Membrane Protein Inter-Barrel Residue Contact Prediction Method

In the two transmembrane protein types, outer membrane proteins (OMPs) perform diverse important biochemical functions, including substrate transport and passive nutrient uptake and intake. Hence their 3D structures are expected to reveal these functions. Because experimental structures are scarce, predicted 3D structures are more adapted to OMP research instead, and the inter-barrel residue contact is becoming one of the most remarkable features, improving prediction accuracy by describing the structural information of OMPs. To predict OMP structures accurately, we explored an OMP inter-barrel residue contact prediction method: OMPcontact. Multiple OMP-specific features were integrated in the method, including residue evolutionary covariation, topology-based transmembrane segment relative residue position, OMP lipid layer accessibility, and residue evolution conservation. These features describe the properties of a residue pair in different respects: sequential, structural, evolutionary, and biochemical. Within a 3-residues slide window, a Support Vector Machine (SVM) could accurately determinate the inter-barrel contact residue pair using above features. A 5-fold cross-valuation process was applied in testing the OMPcontact performance against a non-redundant OMP set with 75 samples inside. The tests compared four evolutionary covariation methods and screen analyzed the adaptive ones for inter-barrel contact prediction. The results showed our method not only efficiently realized the prediction, but also scored the possibility for residue pairs reliably. This is expected to improve OMP tertiary structure prediction. Therefore, OMPcontact will be helpful in compiling a structural census of outer membrane protein.

G-protein coupled receptors: advances in simulation and drug discovery

G-protein coupled receptors (GPCRs), the largest family of human membrane proteins, mediate cellular signaling and represent primary targets of about one third of currently marketed drugs. GPCRs undergo highly dynamic structural transitions during signal transduction, from binding of extracellular ligands to coupling with intracellular effector proteins. Molecular dynamics (MD) simulations have been utilized to investigate GPCR signaling mechanisms (such as pathways of ligand binding and receptor activation/deactivation) and to design novel small-molecule drug candidates. Future research directions point towards modeling cooperative binding of multiple orthosteric and allosteric ligands to GPCRs, GPCR oligomerization and interactions of GPCRs with different intracellular signaling proteins. Through methodological and supercomputing advances, MD simulations will continue to provide important insights into GPCR signaling mechanisms and further facilitate structure-based drug design.

Flat-Bottom Strategy for Improved Accuracy in Protein Side-Chain Placements

We present a new strategy for protein side-chain placement that uses flat-bottom potentials for rotamer scoring. The extent of the flat bottom depends on the coarseness of the rotamer library and is optimized for libraries ranging from diversities of 0.2 Å to 5.0 Å. The parameters reported here were optimized for forcefields using Lennard-Jones 12-6 van der Waals potential with DREIDING parameters but are expected to be similar for AMBER, CHARMM, and other forcefields. This Side-Chain Rotamer Excitation Analysis Method is implemented in the SCREAM software package. Similar scoring function strategies should be useful for ligand docking, virtual ligand screening, and protein folding applications.

What can we learn from molecular dynamics simulations for GPCR drug design?

Recent years have seen a tremendous progress in the elucidation of experimental structural information for G-protein coupled receptors (GPCRs). Although for the vast majority of pharmaceutically relevant GPCRs structural information is still accessible only by homology models the steadily increasing amount of structural information fosters the application of structure-based drug design tools for this important class of drug targets. In this article we focus on the application of molecular dynamics (MD) simulations in GPCR drug discovery programs. Typical application scenarios of MD simulations and their scope and limitations will be described on the basis of two selected case studies, namely the binding of small molecule antagonists to the human CC chemokine receptor 3 (CCR3) and a detailed investigation of the interplay between receptor dynamics and solvation for the binding of small molecules to the human muscarinic acetylcholine receptor 3 (hM3R).

Predicted ligands for the human urotensin-II G protein-coupled receptor with some experimental validation

Human Urotensin-II (U-II) is the most potent mammalian vasoconstrictor known.1 Thus, a U-II antagonist would be of therapeutic value in a number of cardiovascular disorders.2 Here, we describe our work on the prediction of the structure of the human U-II receptor (hUT2 R) using GEnSeMBLE (GPCR Ensemble of Structures in Membrane BiLayer Environment) complete sampling Monte Carlo method. With the validation of our predicted structures, we designed a series of new potential antagonists predicted to bind more strongly than known ligands. Next, we carried out R-group screening to suggest a new ligand predicted to bind with 7 kcal mol(-1) better energy than 1-{2-[4-(2-bromobenzyl)-4-hydroxypiperidin-1-yl]ethyl}-3-(thieno[3,2-b]pyridin-7-yl)urea, the designed antagonist predicted to have the highest affinity for the receptor. Some of these predictions were tested experimentally, validating the computational results. Using the pharmacophore generated from the predicted structure for hUT2 R bound to ACT-058362, we carried out virtual screening based on this binding site. The most potent hit compounds identified contained 2-(phenoxymethyl)-1,3,4-thiadiazole core, with the best derivative exhibiting an IC50 value of 0.581 μM against hUT2 R when tested in vitro. Our efforts identified a new scaffold as a potential new lead structure for the development of novel hUT2 R antagonists, and the computational methods used could find more general applicability to other GPCRs.

Preferential binding of an odor within olfactory receptors: a precursor to receptor activation

Using computational methods, which allow mechanistic insights at a molecular level, we explored the olfactory receptor (OR)-odor interactions for 2 mouse ORs, S79 and S86. Both ORs have been previously experimentally, functionally characterized. The odors used were mostly carboxylic acids, which differed in chain length, substituents on the primary carbon atom-chain and degree of unsaturation. These odors elicited varied activation responses from both ORs. Our studies revealed that both receptors have 2 distinct binding sites. Preferential binding in 1 of the 2 sites is correlated with OR activation. The activating odorants: nonanedioic acid, heptanoic acid, and octanoic acid for OR S79 and nonanoic acid for OR S86 preferentially bind in the region bound by transmembranes (TMs [helical domains]) III, IV, V, and VI. The non excitatory odorants heptanol for S79 and heptanoic acid for S86 showed a greater likelihood of binding in the region bound by TMs I, II, III, and VII. Nanosecond-scale molecular dynamics simulations of the physiologically relevant conditions of docked OR-odorant complexes enabled us to quantitatively assess the roles of individual OR amino acids in odor binding. Amino acid-odorant contact maps and distance determinations over the course of the simulations lend support to our conclusions.

Transmembrane protein alignment and fold recognition based on predicted topology

BACKGROUND

Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from soluble proteins, current protein structure prediction tools for soluble proteins may not work well for TMPs. With the increasing number of experimental TMP structures available, template-based methods have the potential to become broadly applicable for TMP structure prediction. However, the current fold recognition methods for TMPs are not as well developed as they are for soluble proteins.

METHODOLOGY

We developed a novel TMP Fold Recognition method, TMFR, to recognize TMP folds based on sequence-to-structure pairwise alignment. The method utilizes topology-based features in alignment together with sequence profile and solvent accessibility. It also incorporates a gap penalty that depends on predicted topology structure segments. Given the difference between α-helical transmembrane protein (αTMP) and β-strands transmembrane protein (βTMP), parameters of scoring functions are trained respectively for these two protein categories using 58 αTMPs and 17 βTMPs in a non-redundant training dataset.

RESULTS

We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs. Our method achieved 10% and 9% better accuracies than HHalign in αTMPs and βTMPs, respectively. The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition. The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.

Critical analysis of the successes and failures of homology models of G protein-coupled receptors

We present a critical assessment of the performance of our homology model refinement method for G protein-coupled receptors (GPCRs), called LITICon that led to top ranking structures in a recent structure prediction assessment GPCRDOCK2010. GPCRs form the largest class of drug targets for which only a few crystal structures are currently available. Therefore, accurate homology models are essential for drug design in these receptors. We submitted five models each for human chemokine CXCR4 (bound to small molecule IT1t and peptide CVX15) and dopamine D3DR (bound to small molecule eticlopride) before the crystal structures were published. Our models in both CXCR4/IT1t and D3/eticlopride assessments were ranked first and second, respectively, by ligand RMSD to the crystal structures. For both receptors, we developed two types of protein models: homology models based on known GPCR crystal structures, and ab initio models based on the prediction method MembStruk. The homology-based models compared better to the crystal structures than the ab initio models. However, a robust refinement procedure for obtaining high accuracy structures is needed. We demonstrate that optimization of the helical tilt, rotation, and translation is vital for GPCR homology model refinement. As a proof of concept, our in-house refinement program LITiCon captured the distinct orientation of TM2 in CXCR4, which differs from that of adrenoreceptors. These findings would be critical for refining GPCR homology models in future.

Sanjeevini: a freely accessible web-server for target directed lead molecule discovery

Background

Computational methods utilizing the structural and functional information help to understand specific molecular recognition events between the target biomolecule and candidate hits and make it possible to design improved lead molecules for the target.

Results

Sanjeevini represents a massive on-going scientific endeavor to provide to the user, a freely accessible state of the art software suite for protein and DNA targeted lead molecule discovery. It builds in several features, including automated detection of active sites, scanning against a million compound library for identifying hit molecules, all atom based docking and scoring and various other utilities to design molecules with desired affinity and specificity against biomolecular targets. Each of the modules is thoroughly validated on a large dataset of protein/DNA drug targets.

Conclusions

The article presents Sanjeevini, a freely accessible user friendly web-server, to aid in drug discovery. It is implemented on a tera flop cluster and made accessible via a web-interface at http://www.scfbio-iitd.res.in/sanjeevini/sanjeevini.jsp. A brief description of various modules, their scientific basis, validation, and how to use the server to develop in silico suggestions of lead molecules is provided.

Replica exchange Monte-Carlo simulations of helix bundle membrane proteins: rotational parameters of helices

We propose a united-residue model of membrane proteins to investigate the structures of helix bundle membrane proteins (HBMPs) using coarse-grained (CG) replica exchange Monte-Carlo (REMC) simulations. To demonstrate the method, it is used to identify the ground state of HBMPs in a CG model, including bacteriorhodopsin (BR), halorhodopsin (HR), and their subdomains. The rotational parameters of transmembrane helices (TMHs) are extracted directly from the simulations, which can be compared with their experimental measurements from site-directed dichroism. In particular, the effects of amphiphilic interaction among the surfaces of TMHs on the rotational angles of helices are discussed. The proposed CG model gives a reasonably good structure prediction of HBMPs, as well as a clear physical picture for the packing, tilting, orientation, and rotation of TMHs. The root mean square deviation (RMSD) in coordinates of C(α) atoms of the ground state CG structure from the X-ray structure is 5.03 Å for BR and 6.70 Å for HR. The final structure of HBMPs is obtained from the all-atom molecular dynamics simulations by refining the predicted CG structure, whose RMSD is 4.38 Å for BR and 5.70 Å for HR.

Quantifying the differential effects of DHA and DPA on the early events in visual signal transduction

A range of evidence from animal, clinical and epidemiological studies indicates that highly polyunsaturated acyl chains play important roles in development, cognition, vision and other aspects of neurological function. In a number of these studies n3 polyunsaturated fatty acids (PUFAs) appear to be more efficacious than n6 PUFAs. In a previous study of retinal rod outer segments obtained from rats raised on either an n3 adequate or deficient diet, we demonstrated that the replacement of 22:6n3 by 22:5n6 in the n3 deficient rats led to functional deficits in each step in the visual signaling process (Niu et al., 2004). In this study, we examined rhodopsin and phosphodiesterase function and acyl chain packing properties in membranes consisting of phosphatidylcholines with sn-1=18:0, and sn-2=22:6n3, 22:5n6, or 22:5n3 in order to determine if differences in function are due to the loss of one double bond or due to differences in double bond location. At 37 °C the n6 lipid shifted the equilibrium between the active metarhodopsin II (MII) state and inactive metarhodopsin I (MI) state towards MI. In addition, 22:5n6 reduced the rates of MII formation and MII-transducin complex formation by 2- and 6-fold, respectively. At a physiologically relevant level of rhodopsin light stimulation, the activity of phosphodiesterase was reduced by 50% in the 22:5n6 membrane, relative to either of the n3 membranes. Activity levels in the two n3 membranes were essentially identical. Ensemble acyl chain order was assessed with time-resolved fluorescence measurements of the membrane probe diphenylhexatriene (DPH). Analysis in terms of the orientational distribution of DPH showed that acyl chain packing in the two n3 membranes is quite similar, while in the 22:5n6 membrane there was considerably less packing disorder in the bilayer midplane. These results demonstrate that the n3 bond configuration uniquely optimizes the early steps in signaling via a mechanism which may involve acyl chain packing deep in the bilayer.

Identification of anti-malarial compounds as novel antagonists to chemokine receptor CXCR4 in pancreatic cancer cells

Despite recent advances in targeted therapies, patients with pancreatic adenocarcinoma continue to have poor survival highlighting the urgency to identify novel therapeutic targets. Our previous investigations have implicated chemokine receptor CXCR4 and its selective ligand CXCL12 in the pathogenesis and progression of pancreatic intraepithelial neoplasia and invasive pancreatic cancer; hence, CXCR4 is a promising target for suppression of pancreatic cancer growth. Here, we combined in silico structural modeling of CXCR4 to screen for candidate anti-CXCR4 compounds with in vitro cell line assays and identified NSC56612 from the National Cancer Institute's (NCI) Open Chemical Repository Collection as an inhibitor of activated CXCR4. Next, we identified that NSC56612 is structurally similar to the established anti-malarial drugs chloroquine and hydroxychloroquine. We evaluated these compounds in pancreatic cancer cells in vitro and observed specific antagonism of CXCR4-mediated signaling and cell proliferation. Recent in vivo therapeutic applications of chloroquine in pancreatic cancer mouse models have demonstrated decreased tumor growth and improved survival. Our results thus provide a molecular target and basis for further evaluation of chloroquine and hydroxychloroquine in pancreatic cancer. Historically safe in humans, chloroquine and hydroxychloroquine appear to be promising agents to safely and effectively target CXCR4 in patients with pancreatic cancer.

Bihelix: Towards de novo structure prediction of an ensemble of G-protein coupled receptor conformations

G-Protein Coupled Receptors (GPCRs) play a critical role in cellular signal transduction pathways and are prominent therapeutic targets. Recently there has been major progress in obtaining experimental structures for a few GPCRs. Each GPCR, however, exhibits multiple conformations that play a role in their function and we have been developing methods aimed at predicting structures for all these conformations. Analysis of available structures shows that these conformations differ in relative helix tilts and rotations. The essential issue is, determining how to orient each of the seven helices about its axis since this determines how it interacts with the other six helices. Considering all possible helix rotations to ensure that no important packings are overlooked, and using rotation angle increments of 30° about the helical axis would still lead to 12(7) or 35 million possible conformations each with optimal residue positions. We show in this paper how to accomplish this. The fundamental idea is to optimize the interactions between each pair of contacting helices while ignoring the other 5 and then to estimate the energies of all 35 million combinations using these pair-wise interactions. This BiHelix approach dramatically reduces the effort to examine the complete set of conformations and correctly identifies the crystal packing for the experimental structures plus other near-native packings we believe may play an important role in activation. This approach also enables a detailed structural analysis of functionally distinct conformations using helix-helix interaction energy landscapes and should be useful for other helical transmembrane proteins as well.

AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors

We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp .

The use of G-protein coupled receptor models in lead optimization

With the emerging new crystal structures of G-protein coupled receptors (GPCRs), the number of reported in silico receptor models vastly increases every year. The use of these models in lead optimization (LO) is investigated here. Although there are many studies where GPCR models are used to identify new chemotypes by virtual screening, the classical application in LO is rarely reported. The reason for this may be that the quality of a model, which is appropriate for atomistic modeling, must be very high, and the biology of GPCR ligand-dependent signaling is still not fully understood. However, the few reported studies show that GPCR models can be used efficiently in LO for various problems, such as affinity optimization or tuning of physicochemical parameters.

Predicted structures and dynamics for agonists and antagonists bound to serotonin 5-HT2B and 5-HT2C receptors

Subtype 2 serotonin (5-hydroxytryptamine, 5-HT) receptors are major drug targets for schizophrenia, feeding disorders, perception, depression, migraines, hypertension, anxiety, hallucinogens, and gastrointestinal dysfunctions. (1) We report here the predicted structure of 5-HT2B and 5-HT2C receptors bound to highly potent and selective 5-HT2B antagonist PRX-08066 3, (pKi: 30 nM), including the key binding residues [V103 (2.53), L132 (3.29), V190 (4.60), and L347 (6.58)] determining the selectivity of binding to 5-HT2B over 5-HT2A. We also report structures of the endogenous agonist (5-HT) and a HT2B selective antagonist 2 (1-methyl-1-1,6,7,8-tetrahydro-pyrrolo[2,3-g]quinoline-5-carboxylic acid pyridine-3-ylamide). We examine the dynamics for the agonist- and antagonist-bound HT2B receptors in explicit membrane and water finding dramatically different patterns of water migration into the NPxxY motif and the binding site that correlates with the stability of ionic locks in the D(E)RY region.

Ligand supported homology modeling and docking evaluation of CCR2: docked pose selection by consensus scoring

Chemokine receptor 2 (CCR2) is a G-protein coupled receptor (GPCR) and a crucial target for various inflammatory and autoimmune diseases. The structure based antagonists design for many GPCRs, including CCR2, is restricted by the lack of an experimental three dimensional structure. Homology modeling is widely used for the study of GPCR-ligand binding. Since there is substantial diversity for the ligand binding pocket and binding modes among GPCRs, the receptor-ligand binding mode predictions should be derived from homology modeling with supported ligand information. Thus, we modeled the binding of our proprietary CCR2 antagonist using ligand supported homology modeling followed by consensus scoring the docking evaluation based on all modeled binding sites. The protein-ligand model was then validated by visual inspection of receptor-ligand interaction for consistency of published site-directed mutagenesis data and virtual screening a decoy compound database. This model was able to successfully identify active compounds within the decoy database. Finally, additional hit compounds were identified through a docking-based virtual screening of a commercial database, followed by a biological assay to validate CCR2 inhibitory activity. Thus, this procedure can be employed to screen a large database of compounds to identify new CCR2 antagonists.

Importance of receptor flexibility in binding of cyclam compounds to the chemokine receptor CXCR4

We have elucidated the binding sites of four moncyclam and one bicyclam antagonist AMD3100, in the human chemokine receptor CXCR4. Using the predicted structural models of CXCR4, we have further predicted the binding sites of these cyclam compounds. We used the computational method LITiCon to map the differences in receptor structure stabilized by the mono and bicyclam compounds. Accounting for the receptor flexibility lead to a single binding mode for the cyclam compounds, that has not been possible previously using a single receptor structural model and fixed receptor docking algorithms. There are several notable differences in the receptor conformations stabilized by monocyclam antagonist compared to a bicylam antagonist. The loading of the Cu(2+) ions in the cyclam compounds, shrinks the size of the cyclam rings and the residue D262(6.58) plays an important role in bonding to the copper ion in the monocylam compounds while residue E288(7.39) is important for the bicyclam compound.

Hydrophobicity profiles in G protein-coupled receptor transmembrane helical domains

The lack of a crystallographically derived structure for all but three G (TP [guanosine triphosphate]-binding) protein-coupled receptor (GPCRs) proteins necessitates the use of computationally derived methods to determine their structures. Computational methodologies allow a mechanistic glimpse into GPCR-ligand interactions at a molecular level to better understand the initial steps leading to a protein's biologic functions, ie, protecting the ligands that activate, deactivate, or inhibit the protein, stabilizing protein structure in the membrane's lipid bilayer, and ensuring that the hydrophilic environment within the GPCR-binding pocket is maintained. Described here is a formalism that quantifies the amphiphilic nature of a helix, by determining the effective hydrophobicity (or hydrophilicity) at specific positions around it. This formalism will enable computational protein modelers to position helices so that the functional aspects of GPCRs are adequately represented in the model. Hydro-Eff®, an online tool, allows users to calculate effective helical hydrophobicities.

GPCR somatostatin receptor extracellular loop 2 is a key ectodomain for making subtype-selective antibodies with agonist-like activities in the pancreatic neuroendocrine tumor BON cell line

OBJECTIVES

The extracellular loop 2 (ECL2) ectodomain of the G protein-coupled receptor class A is thought to function like an inactivation "lid." We created polyclonal somatostatin receptor ECL2 (anti-SSTR ECL2) antibodies to target this lid and to examine if these antibodies can selectively activate the SSTR.

METHODS

Western blots and live-cell immunofluorescence microscopy determined anti-SSTR ECL2 antibody receptor binding selectivity. 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium assay (MTS assay) and cell cycle assay (fluorescence-activated cell sorting) checked for antibody effect on antiproliferation. Nexin assay examined the antibody's ability to induce apoptosis. LANCE cAMP kit (Perkin Elmer) detected antibody-dependent cAMP decrease. Enzyme-linked immunosorbent assay measured antibody effect on suppressing serotonin secretion. Ligand-receptor binding interference assay with the fluorescent somatostatin (FAM-SST) was used to examine antibody interference to SST-SSTR binding.

RESULTS

Anti-SSTR ECL2 antibodies are SSTR subtype selective and agonist-like, and they suppress cell proliferation via cell cycle arrest and apoptosis. In addition, these antibodies decrease cAMP production and inhibit serotonin secretion. Interestingly, these antibodies do not interfere with SST-SSTR binding.

CONCLUSIONS

The ECL2 is an important ectodomain for G protein-coupled receptor activation and required for ligand binding selectivity. The anti-SSTR2, anti-SSTR3, and anti-SSTR5 ECL2 antibodies independently inhibited BON proliferation and decreased hormone secretion. Unlike octreotide, our antibodies do not interfere with SST-SSTR binding.

Integrated prediction of one-dimensional structural features and their relationships with conformational flexibility in helical membrane proteins

Background

Many structural properties such as solvent accessibility, dihedral angles and helix-helix contacts can be assigned to each residue in a membrane protein. Independent studies exist on the analysis and sequence-based prediction of some of these so-called one-dimensional features. However, there is little explanation of why certain residues are predicted in a wrong structural class or with large errors in the absolute values of these features. On the other hand, membrane proteins undergo conformational changes to allow transport as well as ligand binding. These conformational changes often occur via residues that are inherently flexible and hence, predicting fluctuations in residue positions is of great significance.

Results

We performed a statistical analysis of common patterns among selected one-dimensional equilibrium structural features (ESFs) and developed a method for simultaneously predicting all of these features using an integrated system. Our results show that the prediction performance can be improved if multiple structural features are trained in an integrated model, compared to the current practice of developing individual models. In particular, the performance of the solvent accessibility and bend-angle prediction improved in this way. The well-performing bend-angle prediction can be used to predict helical positions with severe kinks at a modest success rate. Further, we showed that single-chain conformational dynamics, measured by B-factors derived from normal mode analysis, could be predicted from observed and predicted ESFs with good accuracy. A web server was developed (http://tardis.nibio.go.jp/netasa/htmone/) for predicting the one-dimensional ESFs from sequence information and analyzing the differences between the predicted and observed values of the ESFs.

Conclusions

The prediction performance of the integrated model is significantly better than that of the models performing the task separately for each feature for the solvent accessibility and bend-angle predictions. The predictability of the features also plays a role in determining flexible positions. Although the dynamics studied here concerns local atomic fluctuations, a similar analysis in terms of global structural features will be helpful in predicting large-scale conformational changes, for which work is in progress.

Prediction of the three-dimensional structure for the rat urotensin II receptor, and comparison of the antagonist binding sites and binding selectivity between human and rat receptors from atomistic simulations

Urotensin-II (U-II) has been shown to be the most potent mammalian vasoconstrictor known. Thus, a U-II antagonist might be of therapeutic value in a number of cardiovascular disorders. However, interspecies variability of several nonpeptidic ligands complicates the interpretation of in vivo studies of such antagonists in preclinical animal disease models. ACT058362 is a selective antagonist for the human U-II receptor (hUT2R) with a reported K(d) value of approximately 4 nM in a molecular binding assay, but it is reported to bind weakly to rat UT2R (rUT2R), with a K(d) value of approximately 1 500 nM. In contrast, the arylsulphonamide SB706375 is a selective antagonist against both hUT2R (K(d)= approximately 9 nM) and rUT2R (K(d)= approximately 21 nM). To understand the species selectivity of the UT2R, we investigated the binding site of ACT058362 and SB706375 in both hUT2R and rUT2R to explain the dramatically lower (approximately 400-fold) affinity of ACT058362 for rUT2R and the similar affinity (approximately 10 nM) of SB706375 for both UT2Rs. These studies used MembStruk and MSCDock to predict the UT2R structure and the binding site of ACT058362 and SB706375. Based on binding energies, we found two binding modes each with D130(3.32) as the crucial anchoring point (Ballesteros-Weinstein numbering given in superscript). We predict that ACT058362 (an aryl-amine-aryl or ANA ligand) binds in the transmembrane (TM) 3456 region, while SB706375 (an aryl-aryl-amine or AAN ligand) binds in the TM 1237 region. These predicted sites explain the known differences in binding of the ANA ligand to rat and human receptors, while explaining the similar binding of the AAN compound to rat and human receptors. Moreover the predictions explain currently available structure-activity relationship (SAR) data. To further validate the predicted binding sites of these ligands in hUT2R and rUT2R, we propose several mutations that would help define the structural origins of differential responses between UT2R of different species, potentially indicating novel UT2R antagonists with cross-species high affinity.

Allosteric antagonist binding sites in class B GPCRs: corticotropin receptor 1

The 41 amino acid neuropeptide, corticotropin-releasing factor (CRF) and its associated receptors CRF(1)-R and CRF(2)-R have been targeted for treating stress related disorders. Both CRF(1)-R and CRF(2)-R belong to the class B G-protein coupled receptors for which little information is known regarding the small molecule antagonist binding characteristics. However, it has been shown recently that different non-peptide allosteric ligands stabilize different receptor conformations for CRF(1)-R and hence an understanding of the ligand induced receptor conformational changes is important in the pharmacology of ligand binding. In this study, we modeled the receptor and identified the binding sites of representative small molecule allosteric antagonists for CRF(1)-R. The predicted binding sites of the investigated compounds are located within the transmembrane (TM) domain encompassing TM helices 3, 5 and 6. The docked compounds show strong interactions with H228 on TM3 and M305 on TM5 that have also been implicated in the binding by site directed mutation studies. H228 forms a hydrogen bond of varied strengths with all the antagonists in this study and this is in agreement with the decreased binding affinity of several compounds with H228F mutation. Also mutating M305 to Ile showed a sharp decrease in the calculated binding energy whereas the binding energy loss on M305 to Leu was less significant. These results are in qualitative agreement with the decrease in binding affinities observed experimentally. We further predicted the conformational changes in CRF(1)-R induced by the allosteric antagonist NBI-27914. Movement of TM helices 3 and 5 are dominant and generates three degenerate conformational states two of which are separated by an energy barrier from the third, when bound to NBI-27914. Binding of NBI-27914 was predicted to improve the interaction of the ligand with M305 and also enhanced the aromatic stacking between the ligand and F232 on TM3. A virtual ligand screening of ~13,000 compounds seeded with ~350 CRF(1)-R specific active antagonists performed on the NBI-27914 stabilized conformation of CRF(1)-R yielded a 44% increase in enrichment compared to the initially modeled receptor conformation at a 10% cutoff. The NBI-27914 stabilized conformation also shows a high enrichment for high affinity antagonists compared to the weaker ones. Thus, the conformational changes induced by NBI-27914 improved the ligand screening efficiency of the CRF(1)-R model and demonstrate a generalized application of the method in drug discovery.

Predicted 3D structures for adenosine receptors bound to ligands: comparison to the crystal structure

G protein-coupled receptors (GPCRs) are therapeutic targets for many diseases, but progress in developing active and selective therapeutics has been severely hampered by the difficulty in obtaining accurate structures. We have been developing methods for predicting the structures for GPCR ligand complexes, but validation has been hampered by a lack of experimental structures with which to compare our predictions. We report here the predicted structures of the human adenosine GPCR subtypes (A(1), A(2A), A(2B), and A(3)) and the binding sites for adenosine agonist and eight antagonists to this predicted structure, making no use of structural data, and compare with recent experimental crystal structure for ZM241385 bound human A(2A) receptor. The predicted structure correctly identifies 9 of the 12 crystal binding site residues. Moreover, the predicted binding energies of eight antagonists to the predicted structure of A(2A) correlate quite well with experiment. These excellent predictions resulted when we used Monte Carlo techniques to optimize the loop structures, particularly the cysteine linkages. Ignoring these linkages led to a much worse predicted binding site (identifying only 3 of the 12 important residues). These results indicate that computational methods can predict the three-dimensional structure of GPCR membrane proteins sufficiently accurately for use in designing subtype selective ligands for important GPCR therapeutics targets.

Critical review of general guidelines for membrane proteins model building and analysis

Membrane proteins play major roles in many biological processes such as signalling, transport, etc. They have been shown to be involved in the development of many diseases and have become important drug targets per se. The understanding of their functional properties may be facilitated if a 3D structure is available. However, in the case of membrane proteins, only a few 3D structures have been solved to date. Bioinformatics and molecular modelling approaches are thus powerful alternatives to fill the gap between the sequence and the structure. Here, a review of the most recent approaches is proposed together with guidelines on how to use them. In addition, insofar as important biological processes require conformational changes, we discuss some interesting methods aimed at exploring the dynamic behaviour of proteins in their membrane environment. The paper ends with a brief description of useful approaches for determining oligomerisation or ligand binding sites.

A new challenge-development of test systems for the infochemical effect

BACKGROUND, AIM, AND SCOPE

Many-if not all-organisms depend on so-called infochemicals, chemical substances in their surroundings which inform the receivers about their biotic and abiotic environment and which allow them to react adequately to these signals. Anthropogenic substances can interfere with this complex chemical communication system. This finding is called infochemical effect. So far, it is not known to what extent anthropogenic discharges act as infochemicals and influence life and reproduction of organisms in the environment because adequate testing methods to identify chemicals which show the infochemical effect and to quantify their effects have not been developed yet. The purpose of this article is to help and find suitable test designs.

MAIN FEATURES

Test systems used in basic research to elucidate the olfactory cascade and the communication of environmental organisms by infochemicals are plentiful. Some of them might be the basis for a quantified ecotoxicological analysis of the infochemical effect. In principle, test systems for the infochemical effect could be developed at each step of the chemosensory signal transduction and processing cascade.

RESULTS

Experimental set-ups were compiled systematically under the aspect whether they might be usable for testing the infochemical effect of single chemicals in standardized quantifying laboratory experiments. For an appropriate ecotoxicological assessment of the infochemical effect, experimental studies of many disciplines, such as molecular biology, neurobiology, physiology, chemical ecology, and population dynamics, should be evaluated in detail before a decision can be made which test system, respectively which test battery, might be suited best. The test systems presented here are based on the knowledge of the genetic sequences for olfactory receptors, binding studies of odorants, signal transmission, and reactions of the receivers on the level of the organisms or the populations. The following basic approaches are conceivable to identify the role of an infochemical: binding studies to the odorant-binding protein or to the odorant receptor binding protein (e.g., by in situ hybridization and immunohistochemical studies), measurement of electrical signals of the receptor cells in the tissue (e.g., electroolfactograms, electroantennograms), registration of phenotypic changes (e.g., observation under the microscope), behavioral tests (e.g., in situ online biomonitoring, use of T-shaped olfactometers, tests of avoidance responses), measurement of population changes (e.g., cell density or turbidity measurements), and multispecies tests with observation of community structure and community function. The main focus of this study is on aquatic organisms.

DISCUSSION

It is evident that the infochemical effect is a very complex sublethal endpoint, and it needs further studies with standardized quantitative methods to elucidate whether and to what extent the ecosystem is affected. The collection of approaches presented here is far from being complete but should serve as a point of depart for further experimental research.

CONCLUSIONS

This article is the first to compare various approaches for testing the infochemical effect. The development of a suitable test system will not be easy as there are a multitude of relevant chemicals, a multitude of relevant receptors, and a multitude of relevant reactions, and it must be expected that the effective concentrations are very low. The chemical communication is of utmost importance for the ecosystem and justifies great endeavors to find solutions to these technical problems.

RECOMMENDATIONS AND PERSPECTIVES

The infochemical effect is a new chapter in ecotoxicology. Will a new endpoint, the so-called infochemical effect, be required in addition to the actual standard test battery of Annex 5 to Commission Directive 92/69/EEC (EC 1992)? Finding the answer to this question is a big challenge that could be met by a comprehensive research project.

Elucidation of binding sites of dual antagonists in the human chemokine receptors CCR2 and CCR5

Design of dual antagonists for the chemokine receptors CCR2 and CCR5 will be greatly facilitated by knowledge of the structural differences of their binding sites. Thus, we computationally predicted the binding site of the dual CCR2/CCR5 antagonist N-dimethyl-N-[4-[[[2-(4-methylphenyl)-6,7-dihydro-5H-benzohepten-8-yl] carbonyl]amino]benzyl]tetrahydro-2H-pyran-4-aminium (TAK-779), and a CCR2-specific antagonist N-(carbamoylmethyl)-3-trifluoromethyl benzamido-parachlorobenzyl 3-aminopyrrolidine (Teijin compound 1) in an ensemble of predicted structures of human CCR2 and CCR5. Based on our predictions of the protein-ligand interactions, we examined the activity of the antagonists for cells expressing thirteen mutants of CCR2 and five mutants of CCR5. The results show that residues Trp98(2.60) and Thr292(7.40) contribute significantly to the efficacy of both TAK-779 and Teijin compound 1, whereas His121(3.33) and Ile263(6.55) contribute significantly only to the antagonistic effect of Teijin compound 1 at CCR2. Mutation of residues Trp86(2.60) and Tyr108(3.32) adversely affected the efficacy of TAK-779 in antagonizing CCR5-mediated chemotaxis. Y49A(1.39) and E291A(7.39) mutants of CCR2 showed a complete loss of CCL2 binding and chemotaxis, despite robust cell surface expression, suggesting that these residues are critical in maintaining the correct receptor architecture. Modeling studies support the hypothesis that the residues Tyr49(1.39), Trp98(2.60), Tyr120(3.32), and Glu291(7.39) of CCR2 form a tight network of aromatic cluster and polar contacts between transmembrane helices 1, 2, 3, and 7.

Modeling small molecule-compound binding to G-protein-coupled receptors

G-protein-coupled receptors (GPCRs) form a superfamily of membrane proteins that play a crucial role in mediating physiological processes as well as pathogenesis of many critical diseases. They are one of the most successful drug targets, accounting for more than 30% of prescription drugs on the market today. Three-dimensional structural information on GPCRs will greatly aid the drug design process, and great strides are being made in obtaining crystallographic information on GPCRs. Since this process is both tedious and risky, a combination of computational methods and biophysical experiments is a useful approach to rapidly obtain information on a wide variety of GPCRs. In this review, we describe the methods/protocols involved in these computational techniques, as well as methods for site-directed mutagenesis and ligand-binding assays that are currently being used for validating structural-model and small-molecule-ligand binding to GPCRs. We discuss the merits and pitfalls of the various methods used in obtaining structural and dynamic information for ligand binding to GPCRs. Another important factor to consider in drug design is the conformational flexibility of GPCRs since it has been shown that small-molecule ligands of varied efficacy stabilize different receptor conformations leading to functional selectivity of ligands. We discuss the computational methods used to study this specific ligand-induced state.

Computational Biology of Olfactory Receptors

Olfactory receptors, in addition to being involved in first step of the physiological processes that leads to olfaction, occupy an important place in mammalian genomes. ORs constitute super families in these genomes. Elucidating ol-factory receptor function at a molecular level can be aided by a computationally derived structure and an understanding of its interactions with odor molecules. Experimental functional analyses of olfactory receptors in conjunction with computational studies serve to validate findings and generate hypotheses. We present here a review of the research efforts in: creating computational models of olfactory receptors, identifying binding strategies for these receptors with odorant molecules, performing medium to long range simulation studies of odor ligands in the receptor binding region, and identifying amino acid positions within the receptor that are responsible for ligand-binding and olfactory receptor activation. Written as a primer and a teaching tool, this review will help researchers extend the methodologies described herein to other GPCRs.

Preferential binding of allosteric modulators to active and inactive conformational states of metabotropic glutamate receptors

Metabotropic glutamate receptors (mGluRs) are G protein coupled receptors that play important roles in synaptic plasticity and other neuro-physiological and pathological processes. Allosteric mGluR ligands are particularly promising drug targets because of their modulatory effects--enhancing or suppressing the response of mGluRs to glutamate. The mechanism by which this modulation occurs is not known. Here, we propose the hypothesis that positive and negative modulators will differentially stabilize the active and inactive conformations of the receptors, respectively. To test this hypothesis, we have generated computational models of the transmembrane regions of different mGluR subtypes in two different conformations. The inactive conformation was modeled using the crystal structure of the inactive, dark state of rhodopsin as template and the active conformation was created based on a recent model of the light-activated state of rhodopsin. Ligands for which the nature of their allosteric effects on mGluRs is experimentally known were docked to the modeled mGluR structures using ArgusLab and Autodock softwares. We find that the allosteric ligand binding pockets of mGluRs are overlapping with the retinal binding pocket of rhodopsin, and that ligands have strong preferences for the active and inactive states depending on their modulatory nature. In 8 out of 14 cases (57%), the negative modulators bound the inactive conformations with significant preference using both docking programs, and 6 out of 9 cases (67%), the positive modulators bound the active conformations. Considering results by the individual programs only, even higher correlations were observed: 12/14 (86%) and 8/9 (89%) for ArgusLab and 10/14 (71%) and 7/9 (78%) for AutoDock. These findings strongly support the hypothesis that mGluR allosteric modulation occurs via stabilization of different conformations analogous to those identified in rhodopsin where they are induced by photochemical isomerization of the retinal ligand--despite the extensive differences in sequences between mGluRs and rhodopsin.

The structure of human serotonin 2c G-protein-coupled receptor bound to agonists and antagonists

We used the MembStruk computational procedure to predict the three-dimensional (3D) structure for the serotonin 5-HT(2C) G-protein-coupled receptor (GPCR). Using this structure, we used the MSCDock computational procedure to predict the 3D structures for bound ligand-protein complexes for agonists such as serotonin and antagonists such as ritanserin, metergoline, and methiothepin. In addition, we predicted the SAR data for a series of psilocybin analogs, both agonists and antagonists. We performed molecular dynamics (MD) on serotonin bound to 5-HT(2C) and we find the protein and binding site to be stable after 5ns. We find good agreement with the currently known experimental data and we predict a number of new mutations which could be used to validate further our predicted structures. This agreement between theory and experiment suggests that our 3D structure is sufficiently accurate for use in drug design. We also compare a preliminary prediction for 5-HT(2B) with our prediction for 5-HT(2C) and find a difference in TM5 that contributes to different serotonin binding modes in 5-HT(2B) and 5-HT(2C).

Advances in the Development and Application of Computational Methodologies for Structural Modeling of G-Protein Coupled Receptors

BACKGROUND: Despite the large amount of experimental data accumulated in the past decade on G-protein coupled receptor (GPCR) structure and function, understanding of the molecular mechanisms underlying GPCR signaling is still far from being complete, thus impairing the design of effective and selective pharmaceuticals. OBJECTIVE: Understanding of GPCR function has been challenged even further by more recent experimental evidence that several of these receptors are organized in the cell membrane as homo- or hetero-oligomers, and that they may exhibit unique pharmacological properties. Given the complexity of these new signaling systems, researcher's efforts are turning increasingly to molecular modeling, bioinformatics and computational simulations for mechanistic insights of GPCR functional plasticity. METHODS: We review here current advances in the development and application of computational approaches to improve prediction of GPCR structure and dynamics, thus enhancing current understanding of GPCR signaling. RESULTS/CONCLUSIONS: Models resulting from use of these computational approaches further supported by experiments are expected to help elucidate the complex allosterism that propagates through GPCR complexes, ultimately aiming at successful structure-based rational drug design.

Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a number of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method.

Packing of transmembrane helices in bacteriorhodopsin folding: structure and thermodynamics

We propose a coarse-grained (CG) model to study the native structure and physical properties of helical membrane proteins (HMPs) using off-lattice computer simulations. Instead of considering sequence heterogeneity explicitly, we model its effect on the packing of helices by employing a mean packing parameter r(0), which is calculated from an all-atom (AA) model. Specifically, this CG model is applied to investigate the packing of helices in bacteriorhodopsin (BR), and predicts the seven helix bundle structure of BR with a root mean square deviation (RMSD) in coordinates of helix backbone atoms (N, C, C(alpha)) of 3.99 A from its crystal structure. This predicted structure is further refined in an AA model by Amber and the refined structure has a RMSD (in coordinates of helix backbone atoms) of 2.64 A. The predicted packing position, tilting angle, and orientation angle of each helix in the refined structure are consistent with experimental data and their physical origins can be well understood in our model. Our results show that a reasonably good structure of BR can be predicted by using such a dual-scale approach, provided that its secondary structure is known. Starting from a random initial configuration, the folded structure can be obtained in days using a regular desktop computer. Various thermodynamic properties of helix packing of BR are also investigated in this CG model.

Toward high-resolution prediction and design of transmembrane helical protein structures

The prediction and design at the atomic level of membrane protein structures and interactions is a critical but unsolved challenge. To address this problem, we have developed an all-atom physical model that describes intraprotein and protein-solvent interactions in the membrane environment. We evaluated the ability of the model to recapitulate the energetics and structural specificities of polytopic membrane proteins by using a battery of in silico prediction and design tests. First, in side-chain packing and design tests, the model successfully predicts the side-chain conformations at 73% of nonexposed positions and the native amino acid identities at 34% of positions in naturally occurring membrane proteins. Second, the model predicts significant energy gaps between native and nonnative structures of transmembrane helical interfaces and polytopic membrane proteins. Third, distortions in transmembrane helices are successfully recapitulated in docking experiments by using fragments of ideal helices judiciously defined around helical kinks. Finally, de novo structure prediction reaches near-atomic accuracy (<2.5 A) for several small membrane protein domains (<150 residues). The success of the model highlights the critical role of van der Waals and hydrogen-bonding interactions in the stability and structural specificity of membrane protein structures and sets the stage for the high-resolution prediction and design of complex membrane protein architectures.

Computational prediction of atomic structures of helical membrane proteins aided by EM maps

Integral membrane proteins pose a major challenge for protein-structure prediction because only approximately 100 high-resolution structures are available currently, thereby impeding the development of rules or empirical potentials to predict the packing of transmembrane alpha-helices. However, when an intermediate-resolution electron microscopy (EM) map is available, it can be used to provide restraints which, in combination with a suitable computational protocol, make structure prediction feasible. In this work we present such a protocol, which proceeds in three stages: 1), generation of an ensemble of alpha-helices by flexible fitting into each of the density rods in the low-resolution EM map, spanning a range of rotational angles around the main helical axes and translational shifts along the density rods; 2), fast optimization of side chains and scoring of the resulting conformations; and 3), refinement of the lowest-scoring conformations with internal coordinate mechanics, by optimizing the van der Waals, electrostatics, hydrogen bonding, torsional, and solvation energy contributions. In addition, our method implements a penalty term through a so-called tethering map, derived from the EM map, which restrains the positions of the alpha-helices. The protocol was validated on three test cases: GpA, KcsA, and MscL.

Prediction of the 3D structure and dynamics of human DP G-protein coupled receptor bound to an agonist and an antagonist

Prostanoids play important physiological roles in the cardiovascular and immune systems and in pain sensation in peripheral systems through their interactions with eight G-protein coupled receptors. These receptors are important drug targets, but development of subtype specific agonists and antagonists has been hampered by the lack of 3D structures for these receptors. We report here the 3D structure for the human DP G-protein coupled receptor (GPCR) predicted by the MembStruk computational method. To validate this structure, we use the HierDock computational method to predict the binding mode for the endogenous agonist (PGD2) to DP. Based on our structure, we predicted the binding of different antagonists and optimized them. We find that PGD2 binds vertically to DP in the TM1237 region with the alpha chain toward the extracellular (EC) region and the omega chain toward the middle of the membrane. This structure explains the selectivity of the DP receptor and the residues involved in the predicted binding site correlate very well with available mutation experiments on DP, IP, TP, FP, and EP subtypes. We report molecular dynamics of DP in explicit lipid and water and find that the binding of the PGD2 agonist leads to correlated rotations of helices of TM3 and TM7, whereas binding of antagonist leads to no such rotations. Thus, these motions may be related to the mechanism of activation.

Prediction of the 3D Structure of FMRF-amide Neuropeptides Bound to the Mouse MrgC11 GPCR and Experimental Validation

We report the 3D structure predicted for the mouse MrgC11 (mMrgC11) receptor by using the MembStruk computational protocol, and the predicted binding site for the F-M-R-F-NH(2) neuropeptide together with four singly chirally modified ligands. We predicted that the R-F-NH(2) part of the tetrapeptide sticks down into the protein between the transmembrane (TM) domains 3, 4, 5, and 6. The Phe (F-NH(2)) interacted favorably with Tyr110 (TM3), while the Arg makes salt bridges to Asp161 (TM4) and Asp179 (TM5). We predicted that the Met extends from the binding site, but the terminal Phe residue sticks back into an aromatic/hydrophobic site flanked by Tyr237, Leu238, Leu240, and Tyr256 (TM6), and Trp162 (TM4). We carried out subsequent mutagenesis experiments followed by intracellular calcium-release assays that demonstrated the dramatic decrease in activity for the Tyr110Ala, Asp161Ala, and Asp179Ala substitutions, which was predicted by our model. These experiments provide strong evidence that our predicted G protein-coupled receptor (GPCR) structure is sufficiently accurate to identify binding sites for selective ligands. Similar studies were made with the mMrgA1 receptor, which did not bind the R-F-NH(2) dipeptide; we explain this to be due to the increased hydrophobic character of the binding pocket in mMrgA1.

Prediction of the 3-D structure of rat MrgA G protein-coupled receptor and identification of its binding site

Mrg receptors are orphan G protein-coupled receptors (GPCRs) located mainly at the specific set of sensory neurons in the dorsal root ganglia, suggesting a role in nociception. We report here the 3-D structure of rat MrgA (rMrgA) receptor [obtained from homology modeling to the recently validated predicted structures of mouse MrgA1 and MrgC11] and the structure of adenine (a known agonist, K(i)=18nM) bound to rMrgA. This predicted binding site is located within transmembrane helical domains (TMs) 3, 4, 5 and 6, with Asn residues in TM3 and TM4 identified as the key residues for adenine binding. Here the side chain of Asn88 (TM3) forms two pairs of hydrogen bonds with N3 and N9 of adenine while Asn146 (TM4) makes two pairs of hydrogen bonds with N1 and N6 of adenine. These interactions lock adenine tightly in the binding pocket. We also predict the binding site of guanine (not an agonist) and seven other derivatives. Guanine cannot make the hydrogen bond to Asn146 (TM4), leading to binding too weak to be observed experimentally. The predicted binding affinity for other adenine derivatives correlates with the availability of the hydrogen bonds to these two Asn residues. These results validate the predicted structure for rat MrgA and suggest mutation experiments that could further validate the structure. Moreover, the predicted structure and binding site should be useful for seeking other small molecule agonists and antagonists.

3-Dimensional structures of G protein-coupled receptors and binding sites of agonists and antagonists

We summarize here recent progress in predicting the 3-dimensional (3D) structure of G protein-coupled receptors (GPCR) and in predicting the binding sites for various agonists and antagonists. These receptors play a critical role in cell communications (dopamine, histamine, epinephrine, and serotonin) and in sensing the outside world (vision, smell, taste, and pain). There are no experimental 3D structures available for human GPCR despite their vital function and importance as therapeutic targets. Indeed, considering every form of life, there is an experimental structure for only 1 GPCR: bovine rhodopsin. Consequently, we developed the MembStruk method to predict the 3D structure without using homology. We then validated our predicted structures by using them to predict their binding sites and binding energies for strongly binding agonists and antagonists. The results were in excellent agreement with available binding and mutation experiments. We will summarize the results for adrenergic receptors, dopamine receptors, chemokine receptors, muscarinic acetylcholine receptors, and a tetrapeptide receptor (mas-related gene C11).