G protein-coupled receptors (GPCRs) mediate our sense of vision, smell, taste, and pain. They are also involved in cell recognition and communication processes, and hence have emerged as a prominent superfamily for drug targets. Unfortunately, the atomic-level structure is available for only one GPCR (bovine rhodopsin), making it difficult to use structure-based methods to design drugs and mutation experiments. We have recently developed first principles methods (MembStruk and HierDock) for predicting structure of GPCRs, and for predicting the ligand binding sites and relative binding affinities. Comparing to the one case with structural data, bovine rhodopsin, we find good accuracy in both the structure of the protein and of the bound ligand. We report here the application of MembStruk and HierDock to ␤1-adrenergic receptor, endothelial differential gene 6, mouse and rat I7 olfactory receptors, and human sweet receptor. We find that the predicted structure of ␤1-adrenergic receptor leads to a binding site for epinephrine that agrees well with the mutation experiments. Similarly the predicted binding sites and affinities for endothelial differential gene 6, mouse and rat I7 olfactory receptors, and human sweet receptor are consistent with the available experimental data. These predicted structures and binding sites allow the design of mutation experiments to validate and improve the structure and function prediction methods. As these structures are validated they can be used as targets for the design of new receptor-selective antagonists or agonists for GPCRs.GPCR ͉ olfactory receptor ͉ ␤-adrenergic receptor ͉ endothelial differentiation gene ͉ taste receptor G protein-coupled receptors (GPCRs) mediate senses such as odor, taste, vision, and pain (1) in mammals. In addition, important cell recognition and communication processes often involve GPCRs. Indeed, many diseases involve malfunction of these receptors (2), making them important targets for drug development. Unfortunately, despite their importance there is insufficient structural information on GPCRs for structure-based drug design. This is because these membrane-bound proteins are difficult to crystallize, and the atomic-level structure has been solved only for bovine rhodopsin (3, 4). Consequently, it is important to develop theoretical methods to predict the structure and function of GPCRs (5, 6).Experimental data relevant to the function of GPCRs is available for ligand activation of GPCRs (7-15) and site-directed mutagenesis (16)(17)(18). This data has led to information about structural features in the ligand-binding regions of GPCRs (refs. 5 and 19, and references therein). Protein sequence analyses on GPCRs reveals a common protein topology consisting of a membrane-spanning seven-helix bundle, which likely accommodates the binding site for low-molecular-weight ligands. Structurally, GPCRs can be classified as (i) GPCRs with short N terminus (5-80 residues) and (ii) GPCRs with a long N-terminal ectodomain (Ϸ80-600 residues). The long N terminus of ...

Dopamine neurotransmitter and its receptors play a critical role in the cell signaling process responsible for information transfer in neurons functioning in the nervous system. Development of improved therapeutics for such disorders as Parkinson's disease and schizophrenia would be significantly enhanced with the availability of the 3D structure for the dopamine receptors and of the binding site for dopamine and other agonists and antagonists. We report here the 3D structure of the long isoform of the human D2 dopamine receptor, predicted from primary sequence using firstprinciples theoretical and computational techniques (i.e., we did not use bioinformatic or experimental 3D structural information in predicting structures). The predicted 3D structure is validated by comparison of the predicted binding site and the relative binding affinities of dopamine, three known dopamine agonists (antiparkinsonian), and seven known antagonists (antipsychotic) in the D2 receptor to experimentally determined values. These structures correctly predict the critical residues for binding dopamine and several antagonists, identified by mutation studies, and give relative binding affinities that correlate well with experiments. The predicted binding site for dopamine and agonists is located between transmembrane (TM) helices 3, 4, 5, and 6, whereas the best antagonists bind to a site involving TM helices 2, 3, 4, 6, and 7 with minimal contacts to TM helix 5. We identify characteristic differences between the binding sites of agonists and antagonists.W ith the implication of G protein-coupled receptor (GPCR) in many diseases (1, 2), the need to solve the highresolution 3D structure of this class of integral membrane proteins to enable structure-based drug design is an important problem in structural biology. Despite the importance of solving the structure of the GPCRs, the only experimental 3D structure available for a GPCR is bovine rhodopsin. This lack of structures is because the GPCRs are bound to the membrane, making it difficult to express in sufficient quantities for crystallization.To provide structural and ligand binding information on GPCRs, we have been developing first-principles computational techniques for predicting the 3D structure of GPCRs using only the amino acid sequence (MembStruk) and for predicting binding site and binding energy of various ligands to GPCRs (HierDock). Using these techniques, we have reported the structure of olfactory receptors (3, 4), bovine rhodopsin (4, 5), and other GPCRs (4). Dopamine neurotransmitter plays a critical role in cellular signaling processes responsible for information transfer in neurons functioning in the nervous system (6, 7). Dopamine receptors (DR) belong to the superfamily of GPCRs, and to date there are five reported sequences for the human DR with multiple isoforms for each. The DRs may be subdivided based on their pharmacological behavior into the D1-like and the D2-like subfamilies, and these are ideal targets for treating schizophrenia and Parkinson's disease; th...

A major challenge in the application of structure-based drug design methods to proteins belonging to the superfamily of G protein-coupled receptors (GPCRs) is the paucity of structural information (1). The 19 chemokine receptors, belonging to the Class A family of GPCRs, are important drug targets not only for autoimmune diseases like multiple sclerosis but also for the blockade of human immunodeficiency virus type 1 entry (2). Using the MembStruk computational method (3), we predicted the three-dimensional structure of the human CCR1 receptor. In addition, we predicted the binding site of the small molecule CCR1 antagonist BX 471, which is currently in Phase II clinical trials (4). Based on the predicted antagonist binding site we designed 17 point mutants of CCR1 to validate the predictions. Subsequent competitive ligand binding and chemotaxis experiments with these mutants gave an excellent correlation to these predictions. In particular, we find that Tyr-113 and Tyr-114 on transmembrane domain 3 and Ile-259 on transmembrane 6 contribute significantly to the binding of BX 471. Finally, we used the predicted and validated structure of CCR1 in a virtual screening validation of the Maybridge data base, seeded with selective CCR1 antagonists. The screen identified 63% of CCR1 antagonists in the top 5% of the hits. Our results indicate that rational drug design for GPCR targets is a feasible approach.Chemokines belong to a large family of small, chemotactic cytokines that regulate the trafficking of immune cells (5) by binding to cell surface receptors belonging to the GPCR 3 superfamily (5). CCR1, the first CC chemokine receptor to be identified, responds to a number of ligands, including MIP-1␣ (CCL3) and RANTES (regulated on activation normal T cell expressed and secreted) (CCL5) (6, 7). The strong association with a wide variety of autoimmune and pro-inflammatory diseases has made the CCR1 protein an attractive therapeutic target, and Berlex has developed a potent, specific, orally available antagonist, BX 471, currently in a Phase II clinical trial (8).The CCR1 antagonist program that yielded the clinical compound BX 471 followed a traditional drug discovery approach starting with high throughput screening of large compound libraries (9). Although high throughput screening is a main pillar of drug-finding programs in the pharmaceutical industry, it has recently been supplemented by in silico methods to maximize the probability of finding attractive novel leads. Structurebased in silico approaches have been challenging for GPCRs, because only one experimental GPCR structure, that of bovine rhodopsin, with only ϳ20% sequence identity to CCR1 (10), has been reported. Recent developments in GPCR structure prediction methods show great potential for structure-based drug design and identifying novel hits from virtual screens (11)(12)(13)(14)(15).In this communication we report a significant test of the computational method MembStruk by predicting the structure of CCR1. Further, we scanned the entire predicted struc...

The prevailing paradigm for G protein-coupled receptors is that each receptor is narrowly tuned to its ligand and closely related agonists. An outstanding problem is whether this paradigm applies to olfactory receptor (ORs), which is the largest gene family in the genome, in which each of 1,000 different G protein-coupled receptors is believed to interact with a range of different odor molecules from the many thousands that comprise ''odor space. ' O lfactory (odor) receptors (ORs) in the mammalian olfaction system exhibit a combinatorial response to odorant molecules (1). A single odor elicits response from multiple receptors and a single receptor also responds to multiple odorants, so every odorant has been thought to have a unique combination of responses from several receptors. This endows a discriminatory power to the mammalian olfactory system that could discriminate thousands of odors. The mechanisms by which the olfactory system accomplishes its multitude tasks are not clear. However, it is known that each olfactory neuron expresses only one receptor. Odor detection is mediated by Ϸ1,000 ORs that are G protein-coupled membrane-bound proteins. Malnic et al. (1) recently reported the differential responses of individual mouse OR neurons to 24 organic odor compounds (linear alcohols, acids, diacids, and bromoacids with four to nine carbons) by using Ca 2ϩ -imaging techniques, followed by single-cell reverse transcription-PCR to determine the sequence of the responsive OR. These clean single-cell experimental results (1) lead to the compelling question ''what is the molecular basis of odor recognition?'' Such questions can be answered only with the atomic level model of these ORs. No structural information is available for ORs. Also for any member of the membrane protein family, the insolubility of membrane proteins and the difficulty in crystallizing membrane proteins makes it harder to obtain structural information. In this work, we have derived an atomic level structural model for the mammalian OR S25 sequenced by Malnic et al. (1) and also identified the potential binding site for simple aliphatic alcohol and acid odorants to this receptor. The order of binding energies correlate well with the experimental recognition profiles and the binding site predictions also correlate well with the speculations. Modeling TechniquesPrediction of the Structure of ORs. ORs are seven helical transmembrane G protein-coupled receptors. We have derived the atomic model for OR S25 by using a combination of hydrophobicity profile prediction methods (2) and large-scale coarse grain molecular dynamics (MD) methods (3-8) with proper description of differential solvent environment. Prediction of helical regions by using hydrophobicity profiles andoptimization. The transmembrane helices were identified on the basis of hydrophobicity by the multisequence profile method of Donnelly (2), implemented in PERSCAN. A window size of 21 residues was used. For validation, the analysis was done on 21 rat ORs reported by Singer et al. (9)...

We report the 3D structure of human ␤2 adrenergic receptor (AR) predicted by using the MembStruk first principles method. To validate this structure, we use the HierDock first principles method to predict the ligand-binding sites for epinephrine and norepinephrine and for eight other ligands, including agonists and antagonists to ␤2 AR and ligands not observed to bind to ␤2 AR. The binding sites agree well with available mutagenesis data, and the calculated relative binding energies correlate reasonably with measured binding affinities. In addition, we find characteristic differences in the predicted binding sites of known agonists and antagonists that allow us to infer the likely activity of other ligands. The predicted ligand-binding properties validate the methods used to predict the 3D structure and function. This validation is a successful step toward applying these procedures to predict the 3D structures and function of the other eight subtypes of ARs, which should enable the development of subtype-specific antagonists and agonists with reduced side effects.T he adrenergic receptors (ARs) are the class of G proteincoupled receptors (GPCR) responsible for mediating the effects of the catecholamines epinephrine and norepinephrine. There are currently nine known human ARs, partitioned into three subclasses: ␣1 (three subtypes located in vascular smooth muscle, the digestive tract, liver, and postsynaptically in the CNS), ␣2 (three subtypes located pre-and postsynaptically in the CNS, and in a wide variety of peripheral sites), and ␤ (three subtypes located primarily in cardiac, vascular, and adipose tissues, respectively).The members of this receptor class mediate a wide variety of physiological responses, including vasodilation and vasoconstriction, heart rate modulation, regulation of lipolysis, and blood clotting. These diverse and important functions make the adrenergic receptors a tempting pharmaceutical target, but attempts to create effective and specific drugs acting on these receptors have been slowed down by the lack of a 3D structure for any GPCR other than the bovine photoreceptor rhodopsin. The focus of this paper is the ␤2AR, which is targeted by agonist therapy in the treatment of asthma. Unfortunately, ␤2 agonists also exhibit crossreactivity with the other ␤ARs, causing side effects such as increased heart rate and blood pressure (1). Three-dimensional models of the ARs would be extremely useful in the design of subtype-specific pharmaceutical compounds. In addition, the ARs have been thoroughly studied experimentally so that there are ample data for validating the structural predictions, which may in turn provide improved understanding for the superfamily of GPCRs.We report here the predicted 3D structure of ␤2AR, which we use to predict detailed binding sites of agonists and antagonists to ␤2AR. This is an excellent case for validation because there is a wealth of experimental data on ligand-binding sites and mutational analysis with which to compare our results (2, 3).We use the MembStruk ...

G-protein-coupled receptors (GPCRs) are involved in cell communication processes and with mediating such senses as vision, smell, taste, and pain. They constitute a prominent superfamily of drug targets, but an atomic-level structure is available for only one GPCR, bovine rhodopsin, making it difficult to use structure-based methods to design receptor-specific drugs. We have developed the MembStruk first principles computational method for predicting the three-dimensional structure of GPCRs. In this article we validate the MembStruk procedure by comparing its predictions with the high-resolution crystal structure of bovine rhodopsin. The crystal structure of bovine rhodopsin has the second extracellular (EC-II) loop closed over the transmembrane regions by making a disulfide linkage between Cys-110 and Cys-187, but we speculate that opening this loop may play a role in the activation process of the receptor through the cysteine linkage with helix 3. Consequently we predicted two structures for bovine rhodopsin from the primary sequence (with no input from the crystal structure)-one with the EC-II loop closed as in the crystal structure, and the other with the EC-II loop open. The MembStruk-predicted structure of bovine rhodopsin with the closed EC-II loop deviates from the crystal by 2.84 A coordinate root mean-square (CRMS) in the transmembrane region main-chain atoms. The predicted three-dimensional structures for other GPCRs can be validated only by predicting binding sites and energies for various ligands. For such predictions we developed the HierDock first principles computational method. We validate HierDock by predicting the binding site of 11-cis-retinal in the crystal structure of bovine rhodopsin. Scanning the whole protein without using any prior knowledge of the binding site, we find that the best scoring conformation in rhodopsin is 1.1 A CRMS from the crystal structure for the ligand atoms. This predicted conformation has the carbonyl O only 2.82 A from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 0.62 A CRMS from the crystal structure. We also used HierDock to predict the binding site of 11-cis-retinal in the MembStruk-predicted structure of bovine rhodopsin (closed loop). Scanning the whole protein structure leads to a structure in which the carbonyl O is only 2.85 A from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 2.92 A CRMS from the crystal structure. The good agreement of the ab initio-predicted protein structures and ligand binding site with experiment validates the use of the MembStruk and HierDock first principles' methods. Since these methods are generic and applicable to any GPCR, they should be useful in predicting the structures of other GPCRs and the binding site of ligands to these proteins.

To provide practical means for rapidly scanning the extensive experimental combinatorial chemistry libraries now available for high-throughput screening (HTS), it is essential to establish computational virtual ligand screening (VLS) techniques to rapidly identify out of a large library all active compounds against a particular protein target. Toward this goal we developed HierVLS, a fast hierarchical docking approach that starts with a coarse grain conformational search over a large number of configurations filtered with a fast but crude energy function, followed by a succession of finer grain levels, using successively more accurate but more expensive descriptions of the ligand-protein-solvent interactions to filter successively fewer cases. The final step of this procedure optimizes one configuration of the ligand in the protein site using our most accurate energy expression and description of the solvent, which would be impractical for all conformations and sites sampled in the coarse level. HierVLS is based on the HierDock approach, but rather than allowing an hour or more to determine the best binding site and energy for each ligands (as in HierDock), we have adapted our procedure so that it can lead to reliable results while using only 4 min (866 MHz Pentium III processor) per ligand. To validate the accuracy for HierVLS to predict the experimentally observed binding conformation, we considered 37 cocrystal structures comprising 11 target proteins. We find that HierVLS identifies the correct binding mode for all 37 cocrystals. In addition, the calculated binding energies correlate well with available experimental binding constants. To validate how well HierVLS can identify the correct ligand in an extensive library of decoys, we considered a library of over 10 000 molecules. HierVLS identifies 26 out of the 37 cases in the top 2% ranked by binding affinity among the 10 037 molecules. The failures result from either metalcontaining sites on the protein or water-mediated ligand-protein interactions, which we anticipate can be solved within the constraints of practical VLS. We then applied HierVLS to screen a 55000-compound virtual library against the target protein-tyrosine phosphatase 1B (ptp1b). The top 250 compounds by binding affinity included all six ptp1b cocrystal ligands added to the library plus three other experimentally confirmed binders. The best (top 1) binder is an experimentally confirmed positive. We conclude that HierVLS is useful for selecting leads for a particular target out of large combinatorial databases.

The use of graphene-based nanomaterials is being explored in the context of various biomedical applications. Here, we performed a molecular dynamics simulation of individual amino acids on graphene utilizing an empirical force field potential (Amber03). The accuracy of our force field method was verified by modeling the adsorption of amino acids on graphene in vacuum. These results are in excellent agreement with those calculated using ab initio methods. Our study shows that graphene exhibits bioactive properties in spite of the fact that the interaction between graphene and amino acids in a water environment is significantly weaker as compared to that in vacuum. Furthermore, the adsorption characteristics of capped and uncapped amino acids are significantly different from each other due to the desolvation effect. Finally, we conclude that when assessing protein-surface interactions based on adsorption of single amino acids, the minimum requirement is to use capped amino acids as they mimic residues as part of a peptide chain.