Current status of multiscale simulations on GPCRs

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ScienceDirect Current status of multiscale simulations on GPCRs Serdar Durdagi1,2, Berna Dogan1, Ismail Erol1,3, Gu¨lru Kayık1 and Busecan Aksoydan1,2 Membrane receptors couple signaling pathways using various mechanisms. G Protein-Coupled Receptors (GPCRs) represent the largest class of membrane proteins involved in signal transduction across the biological membranes. They are essential targets for cell signaling and are of great commercial interest to the pharmaceutical industry. Recent advances made in molecular biology and computational chemistry offer a range of simulation and multiscale modeling tools for the definition and analysis of protein–ligand, protein–protein, and protein– membrane interactions. The development of new techniques on statistical methods and free energy simulations help to predict novel optimal ligands, G protein specificity and oligomerization. The identification of the ligand-binding activation mechanisms and atomistic determinants as well as the interactions of intracellular binding partners that bind to GPCR targets in different coupling states will provide greater safety in human life. In this review, recent approaches and applications of multiscale simulations on GPCRs were highlighted. Addresses 1 Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey 2 Neuroscience Program, Graduate School of Health Sciences, Bahcesehir University, Istanbul, Turkey 3 Department of Chemistry, Gebze Technical University, Kocaeli, Turkey Corresponding author: Durdagi, Serdar ([email protected]) URL: http://durdagilab.com (S. Durdagi).

Current Opinion in Structural Biology 2019, 55:93–103 This review comes from a themed issue on Theory and simulation: demystifying GPCRs Edited by Shoba Ranganathan and Tom L Blundell For a complete overview see the Issue and the Editorial Available online 10th May 2019 https://doi.org/10.1016/j.sbi.2019.02.013 0959-440X/ã 2019 Elsevier Ltd. All rights reserved.

Introduction G protein-coupled receptors (GPCRs) include the superfamily of structurally related receptors for hormones, neurotransmitters, inflammatory mediators, proteinases, taste and olfactory molecules, and photons. GPCRs are integral membrane proteins characterized by seven www.sciencedirect.com

transmembrane (TM) helices, comprise an extracellular (EC) N-terminus, three EC and intracellular (IC) loops, and an IC C-terminal tail within their structure. Ligands typically bind to the GPCR and upon activation, they recruit intracellular binding partners to couple their signal transduction elements. It is estimated that about 30% of all currently available therapeutics act on GPCRs. Especially in recent years, advances in computer technologies and progress in parallel computing methods, rapid developments in high performance computing and simulation techniques have made it possible to simulate many physiological environments with high accuracy. In this short review paper, (i) enhanced sampling methods; (ii) recent advances in multi-scale simulations, (iii) GPCR oligomerization; and (iv) databases/web servers that are focusing in particular GPCR studies were highlighted.

Enhanced sampling methods in GPCRs Several well-known key mechanistic elements belonging to the transition event toward active state of GPCRs have been shown such as the reorganization of rotamer toggle switch side, TM5-6-7 helices (especially NPxxY pattern in TM7) and increment of water molecules at the helical core. Recent advances in hardware and force fields have made molecular dynamics (MD) simulations a suitable method for studying the biological systems, but typical simulation times often do not adequately able to sample binding and unbinding events. Although there are successful examples in which classical MD simulations have been applied to study the activation mechanisms of GPCRs (for example in the explanation of deactivation mechanism of muscarinic acetylcholine M2 receptor [1]), in this method, it is difficult to overcome the high-energy barriers on potential energy surfaces of proteins. Whereas enhanced sampling techniques are valuable because they take advantage of biasing the systems to desired conformational landscapes. In this section, we will focus on some of the recently published research reports that include the applications of molecular simulation methods (mainly enhanced sampling techniques) in the study of the activation mechanisms and ligand binding/unbinding events in GPCRs. A replica exchange with solute scaling (REST2) MD simulations was used by Cong et al. [2] to explain the role of agonist (neurotensin) and transmission switch residues (Trp3216.48, Phe3587.42, Pro2495.50, Phe3176.44 (superscripts refer to the Ballesteros Weinstein nomenclature [2,3]) on the activation mechanism of neurotensin receptor type 1 (NTS1R). Effects of mutations (especially Phe3587.42), present in the X-ray structures of the Current Opinion in Structural Biology 2019, 55:93–103

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receptor, on maintaining the active or inactive-like features were illuminated and also relevant motions of the TM3–TM5–TM6–TM7 helices during the activation were pointed out [2]. In another REST2 MD simulations study [4], starting from the inactive state of the m-opioid receptor (mOR), the authors then introduced constitutively active mutation (Asn150Ala3.35) to the receptor. By determining the specific role of Trp2936.48 and allosteric Na+ ion, they showed that this mutation was involved in activation. Another recent GPCR activation mechanism study [5] emphasized the predominant role of Tyr2115.58 and Tyr3117.53 in the activation of sphingosine-1phosphate receptor (S1PR1). Upon activation by means of two replicas of 200 ns dual-boost accelerated MD simulations for the apo state of S1PR1, the distance changes and their overall conformational and solvation dynamics together with the orientation of Trp2696.48 on a rotamer toggle switch site underline the critical role of these residues. In addition, a possible role of activation on lipid (i.e. POPC) dynamics at TM1–TM7 extracellular site were also pointed out [5]. The protease-activated receptor (PAR1) is a GPCR, activated by binding of a tethered agonist peptide. Temperature accelerated MD simulations were applied by Bokoch et al. [6] in modeling the dissociation pathway of an antagonist vorapaxar from its receptor PAR1. In the absence of a certain experimental evidence on the location of the binding/unbinding pathway, they proposed the most probable vorapaxar exit site as TM6–TM7 lipid bilayer cavity besides the other alternative routes [6]. The metadynamics technique can be used to calculate the binding free energy along the physically relevant pathways. For example, Saleh et al. [7] performed a benchmark study on estimation of binding free energies of various GPCR–ligand systems using well-tempered multiple-walker metadynamics. In their approach, a funnel-like shaped geometry restraint was used on the ligand binding/unbinding pathway in order to effectively accelerate the sampling where the collective variable of simulations was chosen based on the distance from Trp6.48 residue directed perpendicular to membrane bilayer. Their calculations successfully yielded results that are in agreement with the experimentally determined binding affinities. In another study [8], several enhanced sampling techniques (i.e. umbrella sampling, funnel metadynamics and well-tempered metadynamics) were combined with classical MD simulations to shed light on the mechanism of association of the antagonist BPTU to its receptor P2Y1 (a class-A family GPCR) at the unusual allosteric site (between the membrane and nearby helices TM1–TM2–TM3) which was observed from the X-ray structure of the complex. The authors questioned whether this binding site was an artifact of the crystallization procedure that did not mimic the natural lipid environment. As a result of free energy calculations based on predefined collective variables, the crystallographic Current Opinion in Structural Biology 2019, 55:93–103

pose was confirmed. Moreover, a binding pathway including intermediate state structures were proposed, in which the BPTU–lipid and BPTU–protein interactions were described in detail. In an early study [9], on rhodopsin in 2007, retinal dissociation from the binding pocket was modeled by means of classical and random accelerated MD simulations. In that study, for transferring the system from trans-retinal to neutral intermediate state-prior breaking the covalent bond between Lys2967.43 and retinal, charged nitrogen of Lys2967.43 was neutralized, manually. In 2017, the mechanism of this deprotonation step was investigated with thermodynamic integration in conjunction with QM/MM Car-Parrinello MD simulations [10]. Moreover, the protonated intermediates were also characterized by the calculated absorption spectra with ZINDO/S method that matched the experimental data and also by judging from the comparison with the available experimental conformations. As a result, they illustrated how the conformational rearrangement of retinal, its nearby residues and hydrogen bonding network work in tandem for an organization of the deprotonation reaction. In another recent study, Dore et al. [11] combined classical and steered MD simulations and welltempered metadynamics simulations methods in order to identify the favorable binding pathway of an antagonist (CP-376395) through the reaching of the orthosteric pocket of a class B type GPCR corticotropin-releasing factor receptor type 1(CRF1R). The authors demonstrated that the antagonist favors the penetration from the extracellular region over the entrance from the membrane site by means of free energy calculations [11]. In the absence of experimentally determined structures, it is crucial to integrate molecular modeling tools, since it is important to designate the correct binding mode and pathway of a ligand, especially in the design of novel therapeutic compounds that target GPCRs. Markov state models offer opportunity for studying the rate kinetics and free energy profiling in transitions between states along with ligand binding induced effects on the conformational dynamics of GPCRs. For example, in the case of mOR, by constructing Markov state models based on well-tempered metadynamics and high-throughput MD simulations trajectories, Meral et al. [12] and Kapoor et al. [13] calculated the transition rate kinetics of these conversions, respectively. Molecular docking of small molecules at the binding pocket of target proteins may not able to capture the correct binding mode especially if the docked ligand is considerably different than the co-crystallized ligand. For instance, it is found that there is a discrepancy between the predicted docking pose of a dopamine D3 receptor antagonist (PF4363467) with the experimental mutagenesis analysis, thus Markov state modeling was introduced on the system dynamics to solve this problem [14]. Specifically, the simulations identified kinetically distinct orientations of the same antagonist at the inactive state of the receptor and one of them was proposed as the correct one. The authors www.sciencedirect.com

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Figure 1 agonist binding Arrestin signaling pathway

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Activation of GPCR by agonist initiates the signaling through transducers and mediates the majority of cellular responses to external stimuli such as light, odors, hormone, and so on. Three main intracellular binding partners that play a part in signaling are: G-proteins, G protein coupled receptor kineases (GRKs) and arrestins. G-proteins are composed of three subunits, Ga, Gb, and Gg that bound to GDP (alpha unit bound to GDP) in inactive form and dissociates into Ga and Gbg upon being activated by GTP binding. Four major G protein families have been classified based on their alpha unit: Gs, Gi/o, Gq, and G12. Some GPCR can couple to more than one G proteins depending on the signaling pathway they want to induce as well as many different GPCRs coupling to same G-proteins. GRKs are subdivided into three families based on sequence homology: GRK1 that include GRK1 and GRK7; GRK2 containing GRK2 and 3 and GRK5 which comprises GRK4, 5, and 6. They phosphorylate the C terminal tail of activated receptors and promote the binding of arrestin to receptor. There are four types of arrestins known to interact with GPCR: visual arrestin, cone arrestin (both are expressed almost exclusively in the retina), b-arrestin 1 and b-arrestin 2.

attributed the formation of the new binding mode to the rearrangement of the residues via dihedral shifts at the binding pocket, especially for Phe3466.52 and Phe1975.47 residues. Markov state models take advantage of interpreting and elucidating huge structural data, delivered by several MD simulations, in a mechanistic way that how GPCRs function. Kohloff et al. [15] used cloud computing from Google’s Exacycle platform and used milliseconds order of timescale MD trajectories in conjunction with Markov state models to study the b adrenergic receptor (b2AR) activation mechanism in ligand-bound form. They simulated the receptor complexed with an inverse antagonist (carazolol) and an agonist (BI-167107) in the inactive and active states as well as for the apo forms, correspondingly. They identified and explained in detail many structural motifs discriminating the conformational switches depending on ligand efficacy such that the relative motion of TM3–TM6, Met2155.54-Met2796.41 and Phe2085.47Ser2075.46-Ser2035.42 residues and the dynamical behavior of NPxxY pattern. These studies emphasized on utilizing Markov state models for a different purpose on GPCRs modeling is that; this computational technique has an ability to yield biologically relevant protein conformations.

Multiscale simulations of GPCRs with intracellular binding partners Together with the designation of the correct binding mode and pathway of ligand, it is also necessary to www.sciencedirect.com

understand how the information is communicated between the receptor and the intracellular regions of the recipient. Active GPCR structures in complex with their intracellular binding partners not only provide insights into GPCR mechanism, but also activation mechanisms of binding partners. The usage of these structures in molecular modeling studies, especially on different scales, has given an opportunity to shed light on some difficult questions. In this section, we will mention some computational modeling studies to clarify these questions; for general reviews about intracellular binding partners and their interactions with GPCRs, the reader should be referred to following studies [16–18]. An important question that needs to be clarified is about the differences between coupling characteristics of GPCR and intracellular binding partner complexes, in particular for G protein subfamily (Figure 1) that is coupling selectivity. In 2013, Kling et al. [19] performed the first miscrosecondscale atomistic MD simulations: (i) for b2AR with Gas complex (the first GPCR structure determined with whole G protein unit) and; (ii) dopaminergic D2 receptor (D2R)Gai with dopamine which they have built using homology models based on b2AR-Gas complex. They identified the hot spot residues that determine G protein selectivity and they found that specific amino acids within the a-subunit of G protein in a5-helix, the C-terminus of the Ga and the regions around aN-b1 and a4-b6 determine receptor Current Opinion in Structural Biology 2019, 55:93–103

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recognition. In another study, Van Eps et al. [20] used Double Electron–Electron Resonance (DEER) data that were refined with modeling for Rhodopsin-Gi complex. Atomistic MD simulations were performed to verify the stability of the complex and to observe if there are any conformational changes especially in the Gs bound receptor structure [20]. They showed that engagement of Gi is distinct from Gs and that residues within the b6-sheet of the Ras-like domain of a-domain contributed to selectivity. Another question that needs to be clarified is the effect of intracellular binding partner on GPCR conformations. A related study has been performed by Wang et al. [21] using both the coarse-grained (CG) and all atom MD simulations for rhodopsin–arrestin complex to better understand their interactions. They showed that while formation of receptor–arrestin complex causes significant conformational changes in arrestin, the effects on receptor are minor. Latorraca et al. [22] also performed MD simulations for rhodopsin-arrestin-1, but their aim was to investigate the contributions of receptor transmembrane core and cytoplasmic tail in arrestin activation mechanism. They illustrated that both core and tail of receptors could stimulate arrestin activation independently albeit via different mechanisms. Additionally, their results could explain how some receptors which lacks phosphorylated tails still undergo internalization mediated by arrestin. Kling et al. [19] in the continuation of D2RGai study mentioned above, investigated the differences between partial and full agonists on ternary complex of ligand-D2R-Gai. Their aim was to rationalize the ligand induced specific conformations of the ternary complex and to explore molecular determinants of biased signaling. In 2017, Clark group applied enhanced sampling atomistic MD simulations to classify ligand effects on receptors in regards to biased signaling [23]. They examined the b2AR dynamics in complex with both the arrestin and Gas in their apo form as well as in the presence of well characterized four ligands that exerts different biases against arrestin and G protein. By calculating the binding free energies of intracellular binding partners for different ligands, they claim to identify the functional biases of ligands depending on specific changes observed in the binding pockets of b2AR-arrestin and b2AR-Gas complexes. It is known that receptor-mediated GDP release is accompanied by opening of the interface between the GTPase and helical domains in the Ga subunit. Alexander et al. [24] used b2AR-Gs as template to build rhodopsin-Gi heterotrimer complex with Rosetta program. They highlighted changes in the orientation of the Cterminal a5 helix bound to receptor compared to its orientation in inactive heterotrimer as an important factor in GDP release. In another study, Dror et al. [25] have performed very long (66 ms) all atom MD simulations initiated from either GDP-bound/unbound free Current Opinion in Structural Biology 2019, 55:93–103

heterotrimeric G protein (Gi or Gt) or receptor-bound GDP unbound (b2AR-Gs) structures. In their work, they highlighted that although the Ras and helical domain separation observed in free G protein, GDP release could only be catalyzed by receptor binding as it causes internal rearrangements in Ras domain and weakens the interactions of G protein with GDP. In a recent study, Pachov et al. [26] has also considered b2AR-Gs complex as a model system and they used multi-scale conformational Kino-Geometric Sampling procedure to examine the collective motions of receptor and G protein. They suggested that G protein could couple to agonist-free GPCR and the complex then could access an intermediate conformation that recruit an agonist for full activation and stabilization of the complex. Alhadeff et al. [27], in contrast, applied CG-MD simulations along with Monte Carlo calculations to chart the free energy landscape of b2AR activation process that leads to GDP-GTP exchange. Their work was focused on changes in energetics and relevant activation barriers of process as they emphasized that the movement between different conformations could only be achieved by crossing the energy barriers and dynamic behavior was not the cause but a result of change in energy. Another noteworthy study related to G protein activation mechanism was conducted by Sun et al. [28] in 2018. By combining three computational models, namely metadynamics, Markov state models and Correlation of All Rotameric and Dynamical States (CARDS) analysis of correlated motions they uncover the GDP release mechanism for Gaq that has one of the slowest release rates. Although they have performed simulations for receptor unbound G protein system, their analysis, specifically using the CARDS method, focused on uncovering the most direct routes for communication between the GPCR-binding and nucleotide-binding sites. By this way, not only they have identified a previously unobserved intermediate that defines the rate limiting step for GDP release, but they have also found that an allosteric network that incorporates the hNs1 loop, b-strands S1–S3, and the HG helix of G protein. Unfortunately, there is not much structural information about the interaction of GPCR kinases (GRKs) with activated GPCRs, most likely due to short transient interaction of GRKs with receptors. Two model structures of GPCR bound GRKs (i.e. b2AR-GRK5 [29] and Rhodopsin-GRK1 [30]) were proposed in recent years through the usage of various experimental techniques as well as MD simulations. In another study, Jones Brunette et al. [31] have also considered Rhodopsin-GRK1 complex. However, they considered only the N-terminus residues of GRK1. They used Rosetta server to perform docking studies of GRK1 N-terminus to Rhodopsin and orientation of GRK in complex with the receptor to G protein, specifically in complex with Gt C-terminal and visual arrestin is compared. They www.sciencedirect.com

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suggested that all intracellular binding partners bound to a common site on Rhodopsin [31].

Oligomerization and cooperativity in GPCRs Conventionally, GPCRs are considered to function in monomeric form; however, in silico, in vitro, and in vivo evidences have accumulated to show functional and physiological relevant GPCR homo/heterooligomers (see Figures 2 and 3). In this section, we will briefly introduce recent advances and applications of multiscale modeling techniques that applied to study GPCR oligomerization. For a general review of this phenomenon, readers should be referred to following studies [32,33]. In 2016, Jiang et al. [34] applied elastic network model (ENM) and the protein structure network to study the effect of the dimerization on the dynamics behavior and the allosteric communication path associated with functional mechanism for the three class A GPCR homodimers b1 adrenergic receptor (b1AR), kappa opioid receptor (kOR) and chemokine receptor (CXCR4). Two different interfaces were used, TM1,2,8 for kOR and b1AR, and TM5,6 for CXCR4. In ENM analysis, they observed similar functional motions for all studied systems such as asymmetrical motions in ECL2 and TM6, which play an important role in ligand binding and G protein coupling, respectively. In the protein structure network analysis, regardless of dimer interfaces and GPCR types, they identified Phe6.44, Trp6.48, and X7.39 as important hubs in the communication pathways in all three studied systems [34]. In another dimerization study,

Liao et al. [35] constructed five different interfaces (TM4,5; TM4; intracellular loop (ICL)3 – TM1,2; TM1,2-ICL3 and TM1,8) of adenosine A2A receptor (A2AR) – dopamine D2 receptor (D2R) heteromeric complex, and these GPCR complexes were simulated using different resolutions such as all atom, all atom and CG (hybrid-mixed), and CG models to elucidate and propose the most plausible interfaces. TM4,5 was found to be the most stable one among other interfaces on the ns-to-ms timescales. ICL3 of D2R was found to be more mobile compared to TMs, and authors suggested that GPCR coupling to signaling partners (G proteins and arrestin) could be affected due to ICL3 fluctuations [35]. In 2018, Zhang et al. [36] used ROSETTA docking to obtain mOR dimers with a TM1,2,8 interface (two different dimer pairs; as inactive–inactive (I–I) and active–inactive (A–I)). Multiscale MD simulations of mOR showed that, in I–I dimer pair, negative cooperativity was observed. In A–I pair, dimerization was found to prevent deactivation of active protomer. Regardless of other protomers which couples to I protomer; either I or A, was found to increase the constitutive activation. Also, in protein structure network analysis of A–I pair, significant changes were observed in communication pathways from the orthosteric binding region to the signaling partner binding pocket in I-protomer [36]. In 2016, Guixa`-Gonza´lez et al. [37] investigated the effect of varying amounts of an important omega-3 fatty acid (i.e. docosahexaenoic acid-DHA) on the self-assembly behavior of A2AR and D2R receptor homomer and heteromer using extensive

Figure 2 Bivalent ligand G Protein biased agonist

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GPCRs could exist and function in monomeric or dimeric (higher oligomeric) forms. As it is possible that same protomers could interact with each other to form homo-dimers (oligomers), they could also associate with different protomers to form hetero-dimers (oligomers). Two distinct types of agonist could activate GPCRs in two different signaling pathways; G-protein or arrestin mediated pathways. In the case of dimerization, the signaling pathway observed could be dramatically altered compared to monomeric form. Several scenarios could be observed; arrestin biased ligand would bind and activate dimer in G-protein mediated signaling or G-protein biased ligand would activate arrestin pathway in dimer form. Recently, high selective and potent bivalent ligands were designed to manipulate the desired pharmacological function of GPCR dimers. On the left side, we depicted the case of D2R homo-dimer which couples only to a single G-protein as extensively studied and characterized by Han et al. [82].

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Figure 3

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m-opioid receptor dimer (TM1,2,8 interface) inserted into %20 cholesterol and %80 POPC containing membrane bilayer. (a) Simulation box, chlorine atoms depicted as red, sodium atoms depicted as blue, membrane depicted as stick model (POPC depicted as pale green color, cholesterol depicted as orange color), water molecules depicted as surface model, each protomer depicted as cartoon model. (b) Side-view of dimer, (c) top-view of membrane-protein system. (d–f) Interface transmembrane helices colored green (TM1, TM2, and Helix 8) other TMs colored cyan, side, top and downview, respectively. (g, h) Coarse-grained representation of dimer (side-view) and membrane (downview), respectively (CHARMM-GUI and MARTINI22 forcefield used to generate models). Coarse-grained representation generated using Pymol by showing Ca atoms as spheres.

multiscale modeling. They demonstrated that in the DHA-enriched lipid medium, A2AR, and D2R were tended to aggregate into homooligomers and heterooligomers. However, in the DHA-depleted lipid environment, the protein–protein interaction between protomers was found to be decreased [37]. Pluhackova et al. [38] simulated CXCR4 in pure and cholesterol containing POPC bilayers using all atom and CG simulations and they identified cholesterol binding sites on the receptor. The authors claimed that dimerization of CXCR4 was somehow altered by cholesterol. The dimerization interface TM1/TM5,7 observed in pure POPC bilayer was eliminated and a symmetric TM3,4 interface was found with increasing cholesterol concentration [38]. GPCR oligomerization studies open a new avenue to design more selective and specific bivalent ligands that consist of linker spacer and two different or same pharmacophores. Successful applications of bivalent ligand design studies were reported for D2R/NTS1R heterodimer (TM1,2,8 interface) [39], chemokine receptor 5 Current Opinion in Structural Biology 2019, 55:93–103

(CCR5)/mOR heterodimer (TM5,6 interface) [40] and D2R homodimer (TM6 interface) [41] that have nanomolar and picomolar affinities. Recently, our group successfully generated homology models of D2HighR and D2LowR monomer and dimer pairs. b2AR active and inactive structures (PDB ID, 3SN6 and 3D4S, respectively) were used as templates for monomers and rhodopsin oligomer structure (PDB ID, 1N3M) was used for the dimer construction [42,43,44]. By means of ensemble molecular docking, MD simulations and free energy calculations, agonists dopamine, apomorphine, ACR16, and bromocriptine binding mechanisms were elucidated and it was found that ligands preferentially bind to D2HighR with higher affinities and stabilize symmetric TM4 interfacing dimer systems in line with experimental studies [42,43,44]. In 2017, Altwaijry et al. [45] developed a rapid and reproducible ensemble-based CG-MD method in which; two TM helices were inserted into the lipid bilayer and simulated to shed light into interacting helices and identify corresponding crucial amino acids in GPCR protomers. The developed CG-MD model was www.sciencedirect.com

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first used to identify A2AR dimer interface and symmetric TM5 dimer interface, which is in excellent fit with the experimental data, is obtained. The same method was further used to reveal interfaces of rhodopsin (PDB ID, 1N3M), CXCR4 (PDB ID, 3ODU), b1AR (PDB ID, 4GPO), and TM4–TM5, symmetric TM5, and symmetric TM1 interfaces were identified, respectively. Along with the dimer interface elucidation, authors also suggested 30 replicas and 300 ns simulation time to get sufficient and consistent results [45]. In another study, Kim et al. [46] initially developed homology models of class C sweet taste receptors TAS1R2 and TAS1R3, then TM5,6 crystal GPCR interface was used to obtain TAS1R2/TAS1R3 heterodimer. During all atom MD simulations, authors observed that binding of agonists altered the interaction interface from TM5,6 to the TM6 [46]. In 2018, Borroto-Escuela et al. [47] mapped the interface of the A2AR-D2R heterodimer with a TM4,5 in combination with protein–protein docking and BRET experiments and the obtained model was refined by means of all atom MD simulations. Same heterodimer interface was also achieved by aligning A2AR and D2R protomers to the b1AR receptor (PDB ID, 4GPO). In this model, authors also include adenylyl cyclase (AC5) coupling to A2AR and D2R homodimers and resulting heterocomplex has the TM4,5 interface in the heterodimer (A2AR-D2R); and Gai-coupled D2R active — D2R inactive homodimer (TM6 interface), Gas-coupled A2AR active — A2AR inactive homodimer (TM6 interface) together with AC5. This heterocomplex was used to elucidate GPCR pre-coupling and hetero oligomerization [48]. In another study, Dijkman et al. [49] used seven interfaces for example, TM1,2,8 (PDB IDs, 4GPO, 4DKL, and 4DJH); TM3,4 (PDB ID, 3RZE); TM3,4,5 (PDB IDs, 3OE0 and 4GPO); and TM5,6 (PDB ID, 4DKL) to construct NTS1R homodimers. By using combinations of biophysical methods and CG-MD simulations, authors suggested multiple metastable interfaces of NTS1R and called this phenomenon as ‘rolling dimer’ model [49]. Recently, all possible GPCR dimer interfaces were identified from crystal packings by collecting all available crystal structures of GPCRs (i.e. 215 GPCR structures were available as of November 2017) and 31 parallel dimers with two-fold rotational symmetry were found [50].

Online databases and web servers in modeling of GPCRs studies The very first level of studying GPCRs via computational methods is based on the 3D structural information. This information can be obtained in different ways depending on experimental or homology modeling methods. GPCRs are further investigated for their interactions with ligands and intracellular binding partners; the signaling pathways, and for the forming of higher quaternary structures or in other words, oligomerization. Although the most of the databases and web servers developed in the last decade www.sciencedirect.com

for GPCRs and other biomolecular systems are broadly evaluated and reviewed [51–53], we briefly highlighted the most common and emerging databases and servers [54–73] available for several aims to study on GPCRs in Table 1.

Concluding remarks and future perspectives Computational biophysics is a rapidly emerging field. Predicting with high accuracy the binding free energies of ligands to macromolecules has great pragmatic value in identifying novel molecules that can bind to target receptors and act as therapeutic drugs. The ability to examine these interactions at the molecular level is extremely valuable, in terms of furthering our understanding of the functions of proteins of both clinical and biological importance, minimizing their side effects and in economic terms by refining drug development through directing and guiding experimental studies. Recent technological advances in computational biophysics have made such studies practical and are expected to have a great impact on human health by dramatically shortening the time required for drug-candidate identification, thus accelerating clinical trials, leading ultimately to new medical therapies. While X-ray crystallography or electron microscopy provides high-resolution static pictures of the experimentally resolved stable states of target membrane proteins, it is very difficult to detect the short-lived intermediate conformations occurring transiently during the activation. Therefore, there is lack of information about many of the key microscopic events underlying the activation mechanism, in which, computational approaches can be used to supplement the missing information by providing atomic models for the intermediates along the activation pathway. Starting from an initial pathway, the mean drifts of each image, which can be determined by unbiased MD simulations from swarms-of-trajectories, can be used to iteratively refine the string in the subspace of collective variables until the convergence is reached. By virtue of the thermal noise added from the swarms-of-trajectories, the ensemble of most possible pathways can be sampled. With increasing number of solved GPCR monomer or homo/hetero oligomer structures in recent years, in foreseeable future MD-based hybrid modeling will play a major role in GPCR computations. Tools such as Modeling Employing Limited Data (MELD) [74,75,76], which is a physics-based Bayesian computational method, can be applied for making rigorous inferences from limited or uncertain data. Flexible fitting is a powerful technique to construct the 3D structures of biomolecules from cryoEM density maps and one popular method is a crosscorrelation coefficient-based approach, where the MD simulation is carried out with the biasing potential that includes the cross-correlation coefficient between the Current Opinion in Structural Biology 2019, 55:93–103

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Table 1 Recent and common online databases and web servers available to use in the broad applications of proteins and specifically for GPCRs Database/Server

Main information

Reference

Protein Data Bank (PDB)

Mainly used to achieve the 3D structure information of macromolecule structures resolved by X-ray, NMR and EM/cryo-EM methods. A database to specifically browse all GPCR structures and the collections of receptor mutants, and it also contains diagrams and web tools. A recent semi-manually curated database designed to browse experimental PDB structures of all known GPCR structures. One of the most commonly used fully automated homology modeling server for all proteins. Specifically provides template suggestions and homology models of Class A GPCRs. A recent computational method designed for 3D structure prediction of GPCRs GPCR 3D restraint database Automatic homology modeling and ligand docking of GPCR receptors A novel web service to predict structures via advanced homology modeling tools with triple membrane-fitted quality. A recent and manually curated repository for experimental data related to GPCR–ligand interactions. Information on GPCR–ligand interactions. A database with quantitative information on drug targets and the prescription medicines and experimental drugs that act on them. A database holds specific information about the interactions between GPCRs and G protein subunits, especially with G protein alpha; and the interactions between G protein subunits and other effector proteins. Visualization of the networks are available, and the classification of each protein group is based on different approaches. Provides information supporting the computational and experimental studies such as the effects of oligomerspecific ligands and mechanism of activation. Pertaining to the GPCR oligomerization, G proteincoupled Receptor Interaction Partners DataBase. A sequence-based web server can also be used for the prediction of interfacial protein–protein residue contacts, with no structural template information. A hybrid method including the template-based modeling and conformational sampling specifically designed for second extracellular loops (ECL2) of GPCRs. Developed to facilitate molecular dynamics flexible fitting simulations in different environments including vacuum, implicit/explicit solvent, and membranes with an all-atom or coarse-grained (CG) model.

Berman et al. [54]

GPCRdb

GPCR-EXP

SWISS-MODEL, the ExPASy web server The GPCR-Sequence-Structure-Feature-Extractor (SSFE) 2.0 web server and database GPCR-I-TASSER GPCRRD GOMoDO GPCRM

GLASS (GPCR–Ligand Association) database BindingDB Guide to Pharmacology Database

Human-gpDB

GPCR-OKB

GRIP server GRIPDB ComplexContact

GalaxyGPCRloop server

CHARMM-GUI MDFF/xMDFF Utilizer

experimental and simulated density maps. Singharoy et al. [77,78] have developed two molecular dynamics flexible fitting (MDFF) methods called cascade MDFF and resolution exchange MDFF that help to resolve atomic models of biological molecules from cryo-EM images. Recently, flexible fitting method has been combined with enhanced sampling algorithms such as temperature-accelerated MD (TAMD) [79], self-guided Langevin dynamics [80], and replica-exchange methods [81]. All these recently developed techniques can be expected Current Opinion in Structural Biology 2019, 55:93–103

Pa´ndy-Szekeres et al. [55]

Chan et al. [56]

Waterhouse et al. [57] Worth et al. [58] Zhang et al. [59] Zhang et al. [60] Sandal et al. [61] Miszta et al. [62]

Chan et al. [63] Liu et al. [64] Harding et al. [65]

Satagopam et al. [66]

Khelashvili et al. [67], Skrabanek et al. [68] Nemoto et al. [69,70] Zeng et al. [71]

Won et al. [72]

Qi et al. [73]

to be front runners in the application of MD as a structuredetermination tool for GPCRs.

Conflict of interest statement Nothing declared.

Acknowledgements Authors thanks TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA). SD thanks The Scientific and Technological Research Council of Turkey (TUBITAK) for the support of part of this study (Project No: 214Z122). www.sciencedirect.com

Current status of multiscale simulations on GPCRs Durdagi et al. 101

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