Ab initio computational modeling of loops in G-protein-coupled receptors: lessons from the crystal structure of rhodopsin

The Intriguing Effects of Substituents in the N-Phenethyl Moiety of Norhydromorphone: A Bifunctional Opioid from a Set of "Tail Wags Dog" Experiments

(−)-N-Phenethyl analogs of optically pure N-norhydromorphone were synthesized and pharmacologically evaluated in several in vitro assays (opioid receptor binding, stimulation of [³⁵S]GTPγS binding, forskolin-induced cAMP accumulation assay, and MOR-mediated β-arrestin recruitment assays). “Body” and “tail” interactions with opioid receptors (a subset of Portoghese’s message-address theory) were used for molecular modeling and simulations, where the “address” can be considered the “body” of the hydromorphone molecule and the “message” delivered by the substituent (tail) on the aromatic ring of the N-phenethyl moiety. One compound, N-p-chloro-phenethynorhydromorphone ((7aR,12bS)-3-(4-chlorophenethyl)-9-hydroxy-2,3,4,4a,5,6-hexahydro-1H-4,12-methanobenzofuro[3,2-e]isoquinolin-7(7aH)-one, 2i), was found to have nanomolar binding affinity at MOR and DOR. It was a potent partial agonist at MOR and a full potent agonist at DOR with a δ/μ potency ratio of 1.2 in the ([³⁵S]GTPγS) assay. Bifunctional opioids that interact with MOR and DOR, the latter as agonists or antagonists, have been reported to have fewer side-effects than MOR agonists. The p-chlorophenethyl compound 2i was evaluated for its effect on respiration in both mice and squirrel monkeys. Compound 2i did not depress respiration (using normal air) in mice or squirrel monkeys. However, under conditions of hypercapnia (using air mixed with 5% CO₂), respiration was depressed in squirrel monkeys.

Modulation of opioid receptor affinity and efficacy via N-substitution of 9β-hydroxy-5-(3-hydroxyphenyl)morphan: Synthesis and computer simulation study

The enantiomers of a variety of N-alkyl-, N-aralkyl-, and N-cyclopropylalkyl-9β-hydroxy-5-(3-hydroxyphenyl)morphans were synthesized employing cyanogen bromide and K2CO3 to improve the original N-demethylation procedure. Their binding affinity to the μ-, δ-, and κ-opioid receptors (ORs) was determined and functional (GTPγ35S) assays were carried out on those with reasonable affinity. The 1R,5R,9S-enantiomers (1R,5R,9S)-(-)-5-(3-hydroxyphenyl)-2-(4-nitrophenethyl)-2-azabicyclo[3.3.1]nonan-9-ol (1R,5R,9S-16), (1R,5R,9S)-(-) 2-cinnamyl-5-(3-hydroxyphenyl)-2-azabicyclo[3.3.1]nonan-9-ol (1R,5R,9S-20), and (1R,5R,9S)-(-)-5-(3-hydroxyphenyl)-2-(4-(trifluoromethyl)phenethyl)-2-azabicyclo[3.3.1]nonan-9-ol (1R,5R,9S-15), had high affinity for the μ-opioid receptor (e.g., 1R,5R,9S-16: Ki=0.073, 0.74, and 1.99nM, respectively). The 1R,5R,9S-16 and 1R,5R,9S-15 were full, high efficacy μ-agonists (EC50=0.74 and 18.5nM, respectively) and the former was found to be a partial agonist at δ-OR and an antagonist at κ-OR, while the latter was a partial agonist at δ-OR and κ-OR in the GTPγ35S assay. The enantiomer of 1R,5R,9S-16, (+)-1S,5S,9R-16 was unusual, it had good affinity for the μ-OR (Ki=26.5nM) and was an efficacious μ-antagonist (Ke=29.1nM). Molecular dynamics simulations of the μ-OR were carried out with the 1R,5R,9S-16 μ-agonist and the previously synthesized (1R,5R,9S)-(-)-5-(9-hydroxy-5-(3-hydroxyphenyl-2-phenylethyl)-2-azabicyclo[3.3.1]nonane (1R,5R,9S-(-)-NIH 11289) to provide a structural basis for the observed high affinities and efficacies. The critical roles of both the 9β-OH and the p-nitro group are elucidated, with the latter forming direct, persistent hydrogen bonds with residues deep in the binding cavity, and the former interacting with specific residues via highly structured water bridges.

Structural modeling of G-protein coupled receptors: An overview on automatic web-servers

Despite the significant efforts and discoveries during the last few years in G protein-coupled receptor (GPCR) expression and crystallization, the receptors with known structures to date are limited only to a small fraction of human GPCRs. The lack of experimental three-dimensional structures of the receptors represents a strong limitation that hampers a deep understanding of their function. Computational techniques are thus a valid alternative strategy to model three-dimensional structures. Indeed, recent advances in the field, together with extraordinary developments in crystallography, in particular due to its ability to capture GPCRs in different activation states, have led to encouraging results in the generation of accurate models. This, prompted the community of modelers to render their methods publicly available through dedicated databases and web-servers. Here, we present an extensive overview on these services, focusing on their advantages, drawbacks and their role in successful applications. Future challenges in the field of GPCR modeling, such as the predictions of long loop regions and the modeling of receptor activation states are presented as well.

Detection and characterization of nonspecific, sparsely populated binding modes in the early stages of complexation

A method is proposed to study protein-ligand binding in a system governed by specific and nonspecific interactions. Strong associations lead to narrow distributions in the proteins configuration space; weak and ultraweak associations lead instead to broader distributions, a manifestation of nonspecific, sparsely populated binding modes with multiple interfaces. The method is based on the notion that a discrete set of preferential first-encounter modes are metastable states from which stable (prerelaxation) complexes at equilibrium evolve. The method can be used to explore alternative pathways of complexation with statistical significance and can be integrated into a general algorithm to study protein interaction networks. The method is applied to a peptide-protein complex. The peptide adopts several low-population conformers and binds in a variety of modes with a broad range of affinities. The system is thus well suited to analyze general features of binding, including conformational selection, multiplicity of binding modes, and nonspecific interactions, and to illustrate how the method can be applied to study these problems systematically. The equilibrium distributions can be used to generate biasing functions for simulations of multiprotein systems from which bulk thermodynamic quantities can be calculated.

Structure prediction of the second extracellular loop in G-protein-coupled receptors

G-protein-coupled receptors (GPCRs) play key roles in living organisms. Therefore, it is important to determine their functional structures. The second extracellular loop (ECL2) is a functionally important region of GPCRs, which poses significant challenge for computational structure prediction methods. In this work, we evaluated CABS, a well-established protein modeling tool for predicting ECL2 structure in 13 GPCRs. The ECL2s (with between 13 and 34 residues) are predicted in an environment of other extracellular loops being fully flexible and the transmembrane domain fixed in its x-ray conformation. The modeling procedure used theoretical predictions of ECL2 secondary structure and experimental constraints on disulfide bridges. Our approach yielded ensembles of low-energy conformers and the most populated conformers that contained models close to the available x-ray structures. The level of similarity between the predicted models and x-ray structures is comparable to that of other state-of-the-art computational methods. Our results extend other studies by including newly crystallized GPCRs.

Monte Carlo loop refinement and virtual screening of the thyroid-stimulating hormone receptor transmembrane domain

Metropolis Monte Carlo (MMC) loop refinement has been performed on the three extracellular loops (ECLs) of rhodopsin and opsin-based homology models of the thyroid-stimulating hormone receptor transmembrane domain, a class A type G protein-coupled receptor. The Monte Carlo sampling technique, employing torsion angles of amino acid side chains and local moves for the six consecutive backbone torsion angles, has previously reproduced the conformation of several loops with known crystal structures with accuracy consistently less than 2 Å. A grid-based potential map, which includes van der Waals, electrostatics, hydrophobic as well as hydrogen-bond potentials for bulk protein environment and the solvation effect, has been used to significantly reduce the computational cost of energy evaluation. A modified sigmoidal distance-dependent dielectric function has been implemented in conjunction with the desolvation and hydrogen-bonding terms. A long high-temperature simulation with 2 kcal/mol repulsion potential resulted in extensive sampling of the conformational space. The slow annealing leading to the low-energy structures predicted secondary structure by the MMC technique. Molecular docking with the reported agonist reproduced the binding site within 1.5 Å. Virtual screening performed on the three lowest structures showed that the ligand-binding mode in the inter-helical region is dependent on the ECL conformations.

Specific and non-specific protein association in solution: computation of solvent effects and prediction of first-encounter modes for efficient configurational bias Monte Carlo simulations

Weak and ultraweak protein-protein association play a role in molecular recognition and can drive spontaneous self-assembly and aggregation. Such interactions are difficult to detect experimentally, and are a challenge to the force field and sampling technique. A method is proposed to identify low-population protein-protein binding modes in aqueous solution. The method is designed to identify preferential first-encounter complexes from which the final complex(es) at equilibrium evolve. A continuum model is used to represent the effects of the solvent, which accounts for short- and long-range effects of water exclusion and for liquid-structure forces at protein/liquid interfaces. These effects control the behavior of proteins in close proximity and are optimized on the basis of binding enthalpy data and simulations. An algorithm is described to construct a biasing function for self-adaptive configurational-bias Monte Carlo of a set of interacting proteins. The function allows mixing large and local changes in the spatial distribution of proteins, thereby enhancing sampling of relevant microstates. The method is applied to three binary systems. Generalization to multiprotein complexes is discussed.

An amino acid residue in the second extracellular loop determines the agonist-dependent tolerance property of the human D3 dopamine receptor

The D3 dopamine receptor is a therapeutic target for treating various nervous system disorders such as schizophrenia, Parkinson's disease, depression, and addictive behaviors. The crystal structure of the D3 receptor bound to an antagonist was recently described; however, the structural features that contribute to agonist-induced conformational changes and signaling properties are not well understood. We have previously described the conformation-dependent tolerance and slow response termination (SRT) signaling properties of the D3 receptor and identified the C147 residue in the second intracellular loop (IL2) of the D3 receptor as important for the tolerance property. Interestingly, while IL2 and the C147 residue, in particular, were important for dopamine- and quinpirole-induced tolerance, this residue did not affect the severe tolerance induced by the high affinity, D3 receptor-selective agonist, PD128907. Here, we used D2/D3 receptor chimeras and site-specific D3 receptor mutants to identify another residue, D187, in the second extracellular loop (EC2) of the human D3 receptor that mediates the tolerance property induced by PD128907, quinpirole, pramipexole, and dopamine. Molecular dynamics simulations confirmed the distinct conformation adopted by D3 receptor during tolerance and suggested that in the tolerant D3 receptor the D187 residue in EC2 forms a salt bridge with the H354 residue in EC3. Indeed, site-directed mutation of the H354 residue resulted in loss of PD1287907-induced tolerance. The mapping of specific amino acid residues that contribute to agonist-dependent conformation changes and D3 receptor signaling properties refines the agonist-bound D3 receptor pharmacophore model which will help develop novel D3 receptor agonists.

FINDSITE(X): a structure-based, small molecule virtual screening approach with application to all identified human GPCRs

We have developed FINDSITE(X), an extension of FINDSITE, a protein threading based algorithm for the inference of protein binding sites, biochemical function and virtual ligand screening, that removes the limitation that holo protein structures (those containing bound ligands) of a sufficiently large set of distant evolutionarily related proteins to the target be solved; rather, predicted protein structures and experimental ligand binding information are employed. To provide the predicted protein structures, a fast and accurate version of our recently developed TASSER(VMT), TASSER(VMT)-lite, for template-based protein structural modeling applicable up to 1000 residues is developed and tested, with comparable performance to the top CASP9 servers. Then, a hybrid approach that combines structure alignments with an evolutionary similarity score for identifying functional relationships between target and proteins with binding data has been developed. By way of illustration, FINDSITE(X) is applied to 998 identified human G-protein coupled receptors (GPCRs). First, TASSER(VMT)-lite provides updates of all human GPCR structures previously modeled in our lab. We then use these structures and the new function similarity detection algorithm to screen all human GPCRs against the ZINC8 nonredundant (TC < 0.7) ligand set combined with ligands from the GLIDA database (a total of 88,949 compounds). Testing (excluding GPCRs whose sequence identity > 30% to the target from the binding data library) on a 168 human GPCR set with known binding data, the average enrichment factor in the top 1% of the compound library (EF(0.01)) is 22.7, whereas EF(0.01) by FINDSITE is 7.1. For virtual screening when just the target and its native ligands are excluded, the average EF(0.01) reaches 41.4. We also analyze off-target interactions for the 168 protein test set. All predicted structures, virtual screening data and off-target interactions for the 998 human GPCRs are available at http://cssb.biology.gatech.edu/skolnick/webservice/gpcr/index.html .

Ligand-dependent conformations and dynamics of the serotonin 5-HT(2A) receptor determine its activation and membrane-driven oligomerization properties

From computational simulations of a serotonin 2A receptor (5-HT_2AR) model complexed with pharmacologically and structurally diverse ligands we identify different conformational states and dynamics adopted by the receptor bound to the full agonist 5-HT, the partial agonist LSD, and the inverse agonist Ketanserin. The results from the unbiased all-atom molecular dynamics (MD) simulations show that the three ligands affect differently the known GPCR activation elements including the toggle switch at W6.48, the changes in the ionic lock between E6.30 and R3.50 of the DRY motif in TM3, and the dynamics of the NPxxY motif in TM7. The computational results uncover a sequence of steps connecting these experimentally-identified elements of GPCR activation. The differences among the properties of the receptor molecule interacting with the ligands correlate with their distinct pharmacological properties. Combining these results with quantitative analysis of membrane deformation obtained with our new method (Mondal et al, Biophysical Journal 2011), we show that distinct conformational rearrangements produced by the three ligands also elicit different responses in the surrounding membrane. The differential reorganization of the receptor environment is reflected in (i)-the involvement of cholesterol in the activation of the 5-HT_2AR, and (ii)-different extents and patterns of membrane deformations. These findings are discussed in the context of their likely functional consequences and a predicted mechanism of ligand-specific GPCR oligomerization.

The 5-HT_2A receptor for the neurotransmitter serotonin (5-HT) belongs to family A (rhodopsin-like) G-protein coupled receptors (GPCRs), one of the most important classes of membrane proteins that are targeted by an extensive and diverse collection of external stimuli. Recently we learned that different ligands targeting the same GPCR can elicit different biological responses, but the mechanisms remain unknown. We address this fundamental question for the serotonin 5-HT_2A receptor, because it is known to respond to the binding of structurally diverse ligands by producing similar stimuli in the cell, and to the binding of quite similar ligands with dramatically different responses. Molecular dynamics simulations of molecular models of the serotonin 5-HT_2A receptor in complex with pharmacologically distinct ligands show the dynamic rearrangements of the receptor molecule to be different for these ligands, and the nature and extents of the rearrangements reflect the known pharmacological properties of the ligands as full, partial or inverse activators of the receptor. The different rearrangements of the receptor molecule are shown to produce different rearrangements of the surrounding membrane, a remodeling of the environment that can have differential ligand-determined effects on receptor function and association in the cell's membrane.

Applications and limitations of in silico models in drug discovery

Drug discovery in the late twentieth and early twenty-first century has witnessed a myriad of changes that were adopted to predict whether a compound is likely to be successful, or conversely enable identification of molecules with liabilities as early as possible. These changes include integration of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches. The in silico models are facilitated by the availability of large datasets associated with high-throughput screening, bioinformatics algorithms to mine and annotate the data from a target perspective, and chemoinformatics methods to integrate chemistry methods into lead design process. This chapter highlights the applications of some of these methods and their limitations. We hope this serves as an introduction to in silico drug discovery.

In Silico Veritas: The Pitfalls and Challenges of Predicting GPCR-Ligand Interactions

Recently the first community-wide assessments of the prediction of the structures of complexes between proteins and small molecule ligands have been reported in the so called GPCR Dock 2008 and 2010 assessments. In the current review we discuss the different steps along the protein-ligand modeling workflow by critically analyzing the modeling strategies we used to predict the structures of protein-ligand complexes we submitted to the recent GPCR Dock 2010 challenge. These representative test cases, focusing on the pharmaceutically relevant G Protein-Coupled Receptors, are used to demonstrate the strengths and challenges of the different modeling methods. Our analysis indicates that the proper performance of the sequence alignment, introduction of structural adjustments guided by experimental data, and the usage of experimental data to identify protein-ligand interactions are critical steps in the protein-ligand modeling protocol.

Successful prediction of the intra- and extracellular loops of four G-protein-coupled receptors

We present results of the restoration of all crystallographically available intra- and extracellular loops of four G-protein-coupled receptors (GPCRs): bovine rhodopsin (bRh), the turkey β-1 adrenergic receptor (β1Ar), and the human β-2 adrenergic (β2Ar) and A2A adenosine (A2Ar) receptors. We use our Protein Local Optimization Program (PLOP), which samples conformational space from first principles to build sets of loop candidates and then discriminates between them using our physics-based, all-atom energy function with implicit solvent. We also discuss a new kind of explicit membrane calculation developed for GPCR loops that interact, either in the native structure or in low-energy false-positive structures, with the membrane, and thus exist in a multiphase environment not previously incorporated in PLOP. Our results demonstrate a significant advance over previous work reported in the literature, and of particular note we are able to accurately restore the extremely long second extracellular loop (ECL2), which is also key for GPCR ligand binding. In the case of β2Ar, accurate ECL2 restoration required seeding a small helix into the loop in the appropriate region, based on alignment with the β1Ar ECL2 loop, and then running loop reconstruction simulations with and without the seeded helix present; simulations containing the helix attain significantly lower total energies than those without the helix, and have rmsds close to the native structure. For β1Ar, the same protocol was used, except the alignment was done to β2Ar. These results represent an encouraging start for the more difficult problem of accurate loop refinement for GPCR homology modeling.

Open conformation of adipokinetic hormone receptor from the malaria mosquito facilitates hormone binding

Insect flight requires rapid mobilization of energy reserves during flight, which is mediated and regulated by hormonal control via adipokinetic hormones. The structure of the G-protein receptors to which these hormones bind, are crucial in understanding many of the physiological processes in which they play a central role. To date no 3D structure of an insect G-protein coupled receptor (GPCR) is available. Here, the first models of the 3D structures of a GPCR from the malaria mosquito are presented. Homology modeling of the receptor identified from the genome of Anopheles gambiae was used to construct two models of the receptor. The 7 transmembrane helical bundles of these two models are based on the crystal structures of beta2-adrenergic receptor and rhodopsin. The flexible loop regions were modeled using high temperature simulated annealing and constrained molecular dynamic simulations. The two receptor models differ in a number of critical features, the most important of which is that the rhodopsin-based model has a 'closed' structure while the beta2-based structure is 'open'. The 'open' conformation provides easy access of the hormone to the binding pocket. Docking calculations with the insect adipokinetic hormones, AKH-1 (pGlu-Leu-Thr-Phe-Thr-Pro-Ala-Trp-NH(2)) from the malaria mosquito and Del-CC (pGlu-Lys-Asn-Phe-Ser-Pro-Asn-Trp-Gly-Asn-NH(2)) from the blister beetle showed that while the binding motif of the two is similar, AKH-1 has more than 30 times higher affinity than Del-CC, which strongly suggests that the binding is specific, and that the correct binding site was identified. Using these models it is possible to design antagonists, which block the binding site and are thus species-specific insecticides.

Probing the structural determinants for the function of intracellular loop 2 in structurally cognate G-protein-coupled receptors

Intracellular loop 2 (IL2) in G-protein-coupled receptors (GPCRs) is functionally important, e.g., in binding to G-protein and β-arrestin. Differences in secondary structure of IL2 in the crystal structures of the very similar β(1)- and β(2)-adrenergic receptors (β(1)AR and β(2)AR, respectively), i.e., an α-helix and an L-shaped strand, respectively, emphasize the need to understand the structural basis for IL2 functionality. We studied the properties of IL2 in the context of experimental data using a Monte Carlo-based ab initio method. The procedure was validated first by verifying that the IL2 structures in β(1)AR and β(2)AR crystals were correctly reproduced, even after conformational ensemble searches at >1200 K where most secondary structure had been lost. We found that IL2 in β(1)AR and β(2)AR sampled each other's conformation but adopted different energetically preferred conformations, consistent with the crystal structures. The results indicate a persistent contextual preference for the structure of IL2, which was conserved when the IL2 sequences were interchanged between the receptors. We conclude that the protein environment, more than the IL2 sequence, regulates the IL2 structures. We extended the approach to the molecular model of 5-HT(2A)R for which no crystal structure is available and found that IL2 is predominantly helical, similar to IL2 in β(1)AR. Because the P3.57A mutation in IL2 had been shown to decrease β-arrestin binding and internalization, we predicted the effects of the mutation and found that it decreased the propensity of IL2 to form helix, identifying the helical IL2 as a component of the GPCR active form.

Antibodies as a model system for comparative model refinement

Predicting the conformations of loops is a critical aspect of protein comparative (homology) modeling. Despite considerable advances in developing loop prediction algorithms, refining loops in homology models remains challenging. In this work, we use antibodies as a model system to investigate strategies for more robustly predicting loop conformations when the protein model contains errors in the conformations of side chains and protein backbone surrounding the loop in question. Specifically, our test system consists of partial models of antibodies in which the "scaffold" (i.e., the portion other than the complementarity determining region, CDR, loops) retains native backbone conformation, whereas the CDR loops are predicted using a combination of knowledge-based modeling (H1, H2, L1, L2, and L3) and ab initio loop prediction (H3). H3 is the most variable of the CDRs. Using a previously published method, a test set of 10 shorter H3 loops (5-7 residues) are predicted to an average backbone (N-C alpha-C-O) RMSD of 2.7 A while 11 longer loops (8-9 residues) are predicted to 5.1 A, thus recapitulating the difficulties in refining loops in models. By contrast, in control calculations predicting the same loops in crystal structures, the same method reconstructs the loops to an average of 0.5 and 1.4 A for the shorter and longer loops, respectively. We modify the loop prediction method to improve the ability to sample near-native loop conformations in the models, primarily by reducing the sensitivity of the sampling to the loop surroundings, and allowing the other CDR loops to optimize with the H3 loop. The new method improves the average accuracy significantly to 1.3 A RMSD and 3.1 A RMSD for the shorter and longer loops, respectively. Finally, we present results predicting 8-10 residue loops within complete comparative models of five nonantibody proteins. While anecdotal, these mixed, full-model results suggest our approach is a promising step toward more accurately predicting loops in homology models. Furthermore, while significant challenges remain, our method is a potentially useful tool for predicting antibody structures based on a known Fv scaffold.

Structural and dynamic determinants of ligand binding and regulation of cyclin-dependent kinase 5 by pathological activator p25 and inhibitory peptide CIP

The crystal structure of the cdk5/p25 complex has provided information on possible molecular mechanisms of the ligand binding, specificity, and regulation of the kinase. Comparative molecular dynamics simulations are reported here for physiological conditions. This study provides new insight on the mechanisms that modulate such processes, which may be exploited to control pathological activation by p25. The structural changes observed in the kinase are stabilized by a network of interactions involving highly conserved residues within the cyclin-dependent kinase (cdk) family. Collective motions of the proteins (cdk5, p25, and CIP) and their complexes are identified by principal component analysis, revealing two conformational states of the activation loop upon p25 complexation, which are absent in the uncomplexed kinase and not apparent from the crystal. Simulations of the uncomplexed inhibitor CIP show structural rearrangements and increased flexibility of the interfacial loop containing the critical residue E240, which becomes fully hydrated and available for interactions with one of several positively charged residues in the kinase. These changes provide a rationale for the observed high affinity and enhanced inhibitory action of CIP when compared to either p25 or the physiological activators of cdk5.

Modeling the possible conformations of the extracellular loops in G-protein-coupled receptors

This study presents the results of a de novo approach modeling possible conformational dynamics of the extracellular (EC) loops in G-protein-coupled receptors (GPCRs), specifically in bovine rhodopsin (bRh), squid rhodopsin (sRh), human beta-2 adrenergic receptor (beta2AR), turkey beta-1 adrenergic receptor (beta1AR), and human A2 adenosine receptor (A2AR). The approach deliberately sacrificed a detailed description of any particular 3D structure of the loops in GPCRs in favor of a less precise description of many possible structures. Despite this, the approach found ensembles of the low-energy conformers of the EC loops that contained structures close to the available X-ray snapshots. For the smaller EC1 and EC3 loops (6-11 residues), our results were comparable with the best recent results obtained by other authors using much more sophisticated techniques. For the larger EC2 loops (25-34 residues), our results consistently yielded structures significantly closer to the X-ray snapshots than the results of the other authors for loops of similar size. The results suggested possible large-scale movements of the EC loops in GPCRs that might determine their conformational dynamics. The approach was also validated by accurately reproducing the docking poses for low-molecular-weight ligands in beta2AR, beta1AR, and A2AR, demonstrating the possible influence of the conformations of the EC loops on the binding sites of ligands. The approach correctly predicted the system of disulfide bridges between the EC loops in A2AR and elucidated the probable pathways for forming this system.

Intramolecular disulfide bonds of the prolactin receptor short form are required for its inhibitory action on the function of the long form of the receptor

The short form (S1b) of the prolactin receptor (PRLR) silences prolactin-induced activation of gene transcription by the PRLR long form (LF). The functional and structural contributions of two intramolecular disulfide (S-S) bonds within the extracellular subdomain 1 (D1) of S1b to its inhibitory function on the LF were investigated. Mutagenesis of the paired cysteines eliminated the inhibitory action of S1b. The expression of the mutated S1b (S1bx) on the cell surface was not affected, indicating native-like folding of the receptor. The constitutive JAK2 phosphorylation observed in S1b was not present in cells expressing S1bx, and JAK2 association was disrupted. BRET(50) (BRET(50) represents the relative affinity as acceptor/donor ratio required to reach half-maximal BRET [bioluminescence resonance energy transfer] values) showed decreased LF/S1bx heterodimeric-association and increased affinity in S1bx homodimerization, thus favoring LF homodimerization and prolactin-induced signaling. Computer modeling based on the PRLR crystal structure showed that minor changes in the tertiary structure of D1 upon S-S bond disruption propagated to the quaternary structure of the homodimer, affecting the dimerization interface. These changes explain the higher homodimerization affinity of S1bx and provide a structural basis for its lack of inhibitory function. The PRLR conformation as stabilized by S-S bonds is required for the inhibitory action of S1b on prolactin-induced LF-mediated function and JAK2 association.

Identifying conformational changes of the beta(2) adrenoceptor that enable accurate prediction of ligand/receptor interactions and screening for GPCR modulators

The new beta(2) Adrenoceptor (beta(2)AR) crystal structures provide a high-resolution snapshot of receptor interactions with two particular partial inverse agonists, (-)-carazolol and timolol. However, both experimental and computational studies of GPCR structure are significantly complicated by the existence of multiple conformational states coupled to ligand type and receptor activity. Agonists and antagonists induce or stabilize distinct changes in receptor structure that mediate a range of pharmacological activities. In this work, we (1) established that the existing beta(2)AR crystallographic conformers can be extended to describe ligand/receptor interactions for additional antagonist types, (2) generated agonist-bound receptor conformations, and (3) validated these models for agonist and antagonist virtual ligand screening (VLS). Using a ligand directed refinement protocol, we derived a single agonist-bound receptor conformation that selectively retrieved a diverse set of full and partial beta(2)AR agonists in VLS trials. Additionally, the impact of extracellular loop two conformation on VLS was assessed by docking studies with rhodopsin-based beta(2)AR homology models, and loop-deleted receptor models. A general strategy for constructing and selecting agonist-bound receptor pocket conformations is presented, which may prove broadly useful in creating agonist and antagonist bound models for other GPCRs.

Prediction of protein loop structures using a local move Monte Carlo approach and a grid-based force field

We have developed an improved local move Monte Carlo (LMMC) loop sampling approach for loop predictions. The method generates loop conformations based on simple moves of the torsion angles of side chains and local moves of backbone of loops. To reduce the computational costs for energy evaluations, we developed a grid-based force field to represent the protein environment and solvation effect. Simulated annealing has been used to enhance the efficiency of the LMMC loop sampling and identify low-energy loop conformations. The prediction quality is evaluated on a set of protein loops with known crystal structure that has been previously used by others to test different loop prediction methods. The results show that this approach can reproduce the experimental results with the root mean square deviation within 1.8 A for all the test cases. The LMMC loop prediction approach developed here could be useful for improvement in the quality the loop regions in homology models, flexible protein-ligand and protein-protein docking studies.

Molecular modeling of the second extracellular loop of G-protein coupled receptors and its implication on structure-based virtual screening

The current study describes the validation of high-throughput modeling procedures for the construction of the second extracellular loop (ecl2) of all nonolfactory human G Protein-coupled receptors. Our modeling flowchart is based on the alignment of essential residues determining the particular ecl2 fold observed in the bovine rhodopsin (bRho) crystal structure. For a set of GPCR targets, the dopamine D2 receptor (DRD2), adenosine A3 receptor (AA3R), and the thromboxane A2 receptor (TA2R), the implications of including ecl2 atomic coordinates is evaluated in terms of structure-based virtual screening accuracy: the suitability of the 3D models to distinguish between known antagonists and randomly chosen decoys using automated docking approaches. The virtual screening results of different models describing increasingly exhaustive receptor representations (seven helices only, seven helices and ecl2 loop, full model) have been compared. Explicit modeling of the ecl2 loop was found to be important in only one of three test cases whereas a loopless model was shown to be accurate enough in the two other receptors. An exhaustive comparison of ecl2 loops of 365 receptors to that of bRho suggests that explicit ecl2 loop modeling should be reserved to receptors where loop building can be guided by experimental restraints.

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.

Development and benchmarking of TASSER(iter) for the iterative improvement of protein structure predictions

To improve the accuracy of TASSER models especially in the limit where threading provided template alignments are of poor quality, we have developed the TASSER(iter) algorithm which uses the templates and contact restraints from TASSER generated models for iterative structure refinement. We apply TASSER(iter) to a large benchmark set of 2,773 nonhomologous single domain proteins that are < or = 200 in length and that cover the PDB at the level of 35% pairwise sequence identity. Overall, TASSER(iter) models have a smaller global average RMSD of 5.48 A compared to 5.81 A RMSD of the original TASSER models. Classifying the targets by the level of prediction difficulty (where Easy targets have a good template with a corresponding good threading alignment, Medium targets have a good template but a poor alignment, and Hard targets have an incorrectly identified template), TASSER(iter) (TASSER) models have an average RMSD of 4.15 A (4.35 A) for the Easy set and 9.05 A (9.52 A) for the Hard set. The largest reduction of average RMSD is for the Medium set where the TASSER(iter) models have an average global RMSD of 5.67 A compared to 6.72 A of the TASSER models. Seventy percent of the Medium set TASSER(iter) models have a smaller RMSD than the TASSER models, while 63% of the Easy and 60% of the Hard TASSER models are improved by TASSER(iter). For the foldable cases, where the targets have a RMSD to the native <6.5 A, TASSER(iter) shows obvious improvement over TASSER models: For the Medium set, it improves the success rate from 57.0 to 67.2%, followed by the Hard targets where the success rate improves from 32.0 to 34.8%, with the smallest improvement in the Easy targets from 82.6 to 84.0%. These results suggest that TASSER(iter) can provide more reliable predictions for targets of Medium difficulty, a range that had resisted improvement in the quality of protein structure predictions.

Loopholes and missing links in protein modeling

This paper provides an unbiased comparison of four commercially available programs for loop sampling, Prime, Modeler, ICM, and Sybyl, each of which uses a different modeling protocol. The study assesses the quality of results and examines the relative strengths and weaknesses of each method. The set of loops to be modeled varied in length from 4-12 amino acids. The approaches used for loop modeling can be classified into two methodologies: ab initio loop generation (Modeler and Prime) and database searches (Sybyl and ICM). Comparison of the modeled loops to the native structures was used to determine the accuracy of each method. All of the protocols returned similar results for short loop lengths (four to six residues), but as loop length increased, the quality of the results varied among the programs. Prime generated loops with RMSDs <2.5 A for loops up to 10 residues, while the other three methods met the 2.5 A criteria at seven-residue loops. Additionally, the ability of the software to utilize disulfide bonds and X-ray crystal packing influenced the quality of the results. In the final analysis, the top-ranking loop from each program was rarely the loop with the lowest RMSD with respect to the native template, revealing a weakness in all programs to correctly rank the modeled loops.

Receptor-mediated activation of heterotrimeric G-proteins: current structural insights

G-protein-coupled receptors (GPCRs) serve as catalytic activators of heterotrimeric G-proteins (Galphabetagamma) by exchanging GTP for the bound GDP on the Galpha subunit. This guanine nucleotide exchange factor activity of GPCRs is the initial step in the G-protein cycle and determines the onset of various intracellular signaling pathways that govern critical physiological responses to extracellular cues. Although the structural basis for many steps in the G-protein nucleotide cycle have been made clear over the past decade, the precise mechanism for receptor-mediated G-protein activation remains incompletely defined. Given that these receptors have historically represented a set of rich drug targets, a more complete understanding of their mechanism of action should provide further avenues for drug discovery. Several models have been proposed to explain the communication between activated GPCRs and Galphabetagamma leading to the structural changes required for guanine nucleotide exchange. This review is focused on the structural biology of G-protein signal transduction with an emphasis on the current hypotheses regarding Galphabetagamma activation. We highlight several recent results shedding new light on the structural changes in Galpha that may underlie GDP release.

Development and application of hybrid structure based method for efficient screening of ligands binding to G-protein coupled receptors

G-protein coupled receptors (GPCRs) comprise a large superfamily of proteins that are targets for nearly 50% of drugs in clinical use today. In the past, the use of structure-based drug design strategies to develop better drug candidates has been severely hampered due to the absence of the receptor's three-dimensional structure. However, with recent advances in molecular modeling techniques and better computing power, atomic level details of these receptors can be derived from computationally derived molecular models. Using information from these models coupled with experimental evidence, it has become feasible to build receptor pharmacophores. In this study, we demonstrate the use of the Hybrid Structure Based (HSB) method that can be used effectively to screen and identify prospective ligands that bind to GPCRs. Essentially; this multi-step method combines ligand-based methods for building enriched libraries of small molecules and structure-based methods for screening molecules against the GPCR target. The HSB method was validated to identify retinal and its analogues from a random dataset of approximately 300,000 molecules. The results from this study showed that the 9 top-ranking molecules are indeed analogues of retinal. The method was also tested to identify analogues of dopamine binding to the dopamine D2 receptor. Six of the ten top-ranking molecules are known analogues of dopamine including a prodrug, while the other thirty-four molecules are currently being tested for their activity against all dopamine receptors. The results from both these test cases have proved that the HSB method provides a realistic solution to bridge the gap between the ever-increasing demand for new drugs to treat psychiatric disorders and the lack of efficient screening methods for GPCRs.

Ab initio computational modeling of long loops in G-protein coupled receptors

A newly developed approach for predicting the structure of segments that connect known elements of secondary structure in proteins has been applied to some of the longer loops in the G-protein coupled receptors (GPCRs) rhodopsin and the dopamine receptor D2R. The algorithm uses Monte Carlo (MC) simulation in a temperature annealing protocol combined with a scaled collective variables (SCV) technique to search conformation space for loop structures that could belong to the native ensemble. Except for rhodopsin, structural information is only available for the transmembrane helices (TMHs), and therefore the usual approach of finding a single conformation of lowest energy has to be abandoned. Instead the MC search aims to find the ensemble located at the absolute minimum free energy, i.e., the native ensemble. It is assumed that structures in the native ensemble can be found by an MC search starting from any conformation in the native funnel. The hypothesis is that native structures are trapped in this part of conformational space because of the high-energy barriers that surround the native funnel. In this work it is shown that the crystal structure of the second extracellular loop (e2) of rhodopsin is a member of this loop's native ensemble. In contrast, the crystal structure of the third intracellular loop is quite different in the different crystal structures that have been reported. Our calculations indicate, that of three crystal structures examined, two show features characteristic of native ensembles while the other one does not. Finally the protocol is used to calculate the structure of the e2 loop in D2R. Here, the crystal structure is not known, but it is shown that several side chains that are involved in interaction with a class of substituted benzamides assume conformations that point into the active site. Thus, they are poised to interact with the incoming ligand.