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Molecular Mechanics/Coarse-grain simulations as a structural prediction tool

for GPCRs/ligand complexes

Francesco Musiani1, Alejandro Giorgetti2,3,4,*, Paolo Carloni3,4,*

1 Scuola Internazionale Superiore di Studi Avanzati (SISSA/ISAS), Trieste, Italy.

2 Department of Biotechnology, University of Verona, Ca’ Vignal 1, Verona, Italy.

3 Computational Biophysics, German Research School for Simulation Sciences, Jülich,

Germany.

4 Institute for Advanced Simulation, Forschungszentrum Jülich, Jülich, Germany.

* To whom correspondence should be addressed: Alejandro Giorgetti, Department of

Biotechnology, University of Verona, Ca’ Vignal 1, Strada le Grazie 15, I-37134 Verona, Italy.

Phone: +39 045 8027905, Fax: +39 045 8027929, Mail: alejandro.giorgetti@univr.it. Paolo

Carloni, Computational Biophysics, German Research School for Simulation Sciences, Wilhelm-

Johnen-Straße, D-52425 Jülich, Germany, and Institute for Advanced Simulation,

Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-52425 Jülich, Germany; Phone: +49 2461

618941; Fax: +49 2461 614823; Mail: p.carloni@grs-sim.de.

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Abstract

G-protein coupled receptors (GPCRs) are the most common family of transmembrane

receptors in humans. Bioinformatics-based approaches have provided accurate structural

predictions of these proteins in complex with their agonist/antagonists when reliable template

could be identified. Unfortunately, the average sequence identity across GPCR’s is in the

majority of cases below 20%. In these cases, target selection and alignment required for

homology modelling is nontrivial, and subsequent standard docking procedures may suffer from

severe limitations. A hybrid “Molecular Mechanics/Coarse-Grained” (MM/CG) scheme,

developed by some of us and reviewed here, has been shown to improve the quality of structural

predictions in few cases and holds promises for high-throughput investigations of GPCR/ligand

complexes which do not possess a highly reliable structural template.

1. Introduction

G protein-coupled receptors (GPCRs) form the largest membrane-bound receptor family

expressed by humans (encompassing ca. 4% of the protein-coding genome) (Schoneberg et al.

2004). They are of paramount importance for pharmaceutical intervention (ca. 40% of currently

marketed drugs target GPCRs) (Overington, Al-Lazikani, and Hopkins 2006). GPCRs are

located in the plasma membrane and transduce signals through their interactions with both

extracellular ligands (or light in the case of rhodopsin) and intracellular heterotrimeric guanine

nucleotide-binding proteins (G proteins) to initiate signalling cascades that allow cells to react to

changes within their environment (Audet and Bouvier 2012). The resulting response regulates a

broad range of cellular processes engaged in the control of cell proliferation, differentiation,

motility, as well as apoptosis. Chemicals and light sensing also rely on GPCRs signalling. These

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proteins are also involved in a plethora of inflammatory diseases (Sun and Ye 2012),

cardiovascular diseases, neurological disorders and cancer (Dorsam and Gutkind 2007).

2. Structural determinants of GPCRs

GPCRs share a common scaffold comprising an extracellular N-terminal loop (N-term),

followed by seven trans-membrane (TM) α-helices (TM1 to TM7) connected by intracellular

(IL), extracellular (EL) loops, and an intracellular C-terminal loop (C-term) (Fig. 1A)

(Venkatakrishnan et al. 2013). GPCRs’ tertiary structure resembles a barrel, with the seven

transmembrane helices forming a cavity within the plasma membrane that serves as ligand-

binding domain, often covered by EL2. In several cases they can exist as homo- or hetero-dimers

or higher-order oligomers during their life cycle in vivo (Gurevich and Gurevich 2008).

Currently, the PDB reports 23 unique experimental structures (as of October 2013), of which 18

from homo sapiens (as reported in the http://blanco.biomol.uci.edu/mpstruc web site)

(Rosenbaum, Rasmussen, and Kobilka 2009; Topiol and Sabio 2009; Sprang 2011;

Venkatakrishnan et al. 2013). Twenty of them belong to the rhodopsin family (Fig. 1B), one to

the frizzled/taste2 family (Wang et al. 2013) and two to the secretin family (Hollenstein et al.

2013; Siu et al. 2013) (Fig. 1B). A great effort is presently being carried out in order to extend

our structural knowledge on this receptor superfamily [i.e. the GPCR Network (Stevens et al.

2013)].

--- Place figure 1 here ---

Extensive molecular dynamics simulations have been often used to gain insights into the

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dynamical properties of GPCRs based on the crystal structures available so far [see refs. (Vanni

et al. 2009, 2010, 2011; Dror et al. 2011) for some examples on the adrenergic receptors and refs.

(Scarabelli et al. 2013; Provasi, Johnston, and Filizola 2010; Provasi, Bortolato, and Filizola

2009; Yuan, Vogel, and Filipek 2013) for the opioid receptors]. In cases for which the crystal

structure is not available, homology modelling techniques are the method of choice for GPCRs’

structural characterization.

3. Predictions based on templates from the rhodopsin subfamily

Several excellent bioinformatics studies have elucidated structure/functions relationships

of members of the rhodopsin subfamily (Petrel et al. 2004; de Graaf and Rognan 2009;

Bhattacharya et al. 2010) (Niv et al. 2006; Niv and Filizola 2008; Ivanov, Barak, and Jacobson

2009; Kufareva et al. 2011)1.

The GPCR Dock assessments (Michino et al. 2009; Kufareva et al. 2011) are community-

wide, blind structural predictions of agonist/antagonists in complex with GPCRs (so far human

proteins members of the rhodopsin subfamily). The predictions are then compared with the X-ray

structures, that was released after the assessment. In the first edition, GPCR Dock 2008 (Michino

et al. 2009), twenty-nine groups predicted the structure of the human A2A adenosine receptor

bound to the ligand ZM241385. Precise modelling of the extracellular loops, together with the

location of the disulphide bond and an accurate alignment of the TM regions have turned out to

be crucial ingredients of an accurate prediction. In the last reported competition, GPCR Dock

2010 (Kufareva et al. 2011) thirty-five groups predicted the structure of two GPCRs. The first is

dopamine D3 receptor in complex with eticlopride antagonist. Its structural determinants were

1 Because of the large number of studies, this Section cannot be exhaustive and only some studies will be reported.

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predicted fairly well using the adrenergic receptors as templates (SI ca. 40%). The second target

was CXCR4 in complex with isothiourea IT1t antagonists and CVX15 cyclic peptide antagonist.

The available structural templates are distant homologues and not unexpectedly the accuracy of

the prediction was less satisfactory. This shows that, indeed, in the absence of a suitable

template, GPCRs modelling still remains very challenging. It will be highly interesting to see if

the degree of accuracy in the prediction will increase significantly in the last competition, run

this year (http://gpcr.scripps.edu/GPCRDock2013).

The power of state-of-the-art structural predictions is shown, for instance, by a recent

study of Gutierrez de Teràn and co-workers (Rodriguez, Pineiro, and Gutierrez-de-Teran 2011).

Comparison with experiments shows that their prediction of the A2B adenosine receptor’s

binding cavity is very accurate. The sequence identity (SI) between the target and the templates

(A2A adenosine receptor) was around 60%. This approach was recently implemented in the

GPCR-ModSim web server (Gutierrez-de-Teran, Bello, and Rodriguez 2013). The same

procedure was also used for the successful structural prediction of the human neuropeptide

receptor Y2 (Fallmar et al. 2011). In another relevant example, Carlsson and collaborators have

docked over 3.3 million molecules against a homology model of the dopamine D3 receptor,

before the crystal structure was solved (Carlsson et al. 2011). They have then experimentally

tested the 26 molecules with the highest ranking. One of these novel ligands was therefore

optimized and followed as a potential drug candidate. This shows that predictions may be

reliable for drug design if based on a template of the same subfamily (Carlsson et al. 2011).

4. Predictions based on other templates

The SI between most GPCRs and their best templates for homology modelling is lower

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than 20% (Rayan 2010). These include all of olfactory and taste receptors (overall, more than

400 receptors). Side chains orientations, including those in the binding site, are surely poorly

predicted (Eswar et al. 2007). This problem, along with difficulties associated with target

selection and alignment required for homology modelling, as well limitations of docking

procedures1, calls for experimental validation (Khafizov et al. 2007; Biarnes et al. 2010; Carlsson

et al. 2011; Levit et al. 2012; Mobarec, Sanchez, and Filizola 2009; Yarnitzky, Levit, and Niv

2010; Brockhoff et al. 2010; Slack et al. 2010; Marchiori et al. 2013)2 and/or molecular

simulation-based structural refinement. The next Section focuses on our effort to address this

issue.

5. A simulation approach to structural predictions of targets with low SI with their

templates

As discussed in the previous section, we basically we do not know where side chains are

located when the SI between template and target is about 20% or lower (Tramontano et al.

2008)]. Hence, it might actually be better not to include them at all in the model rather than

including them in wrong orientations. Keeping this in mind, we have developed a computational

tool aimed to improve the structural prediction quality of GPCRs/ligand complexes. This is a

hybrid “Molecular Mechanics/Coarse-Grained” (MM/CG) scheme. In this approach, different

parts of the system are modelled at both different levels of theory, taking care in suitably

describing the coupling at the interface (Neri et al. 2005; Neri et al. 2008; Leguebe et al. 2012).

1 Standard and automatic docking procedures on homology modeling with such templates, such as those used in refs. (Garcia-Perez et al. 2011; Kothandan, Gadhe, and Cho 2012), may suffer from severe limitations. These include and neglecting the presence of explicit solvent (Camacho 2005). This is particularly important for GPCRs, as water molecules can be found in the binding site of these receptors and they may be crucial to stabilize the ligand (Angel, Chance, and Palczewski 2009; Nygaard et al. 2010). 2 One may identify residues that are important for ligand binding and validate the predictions by agonist/antagonist binding essays on target GPCR's mutants (Costanzi 2013; Marchiori et al. 2013).

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In other words, the GPCR’s ligand, the binding site and the water molecules around it are treated

using an atomistic force field, whilst the protein frame is described at CG level using a Go-like

model (Go and Abe 1981) (Fig. 2). This model includes only the Cα atoms of the protein. This

method is much cheaper than full-atom MD simulations (Leguebe et al. 2012).

--- Place figure 2 here ---

Theory of the MM/CG method. The potential energy function in the MM/CG scheme reads:

� = ��� + �� + ��/�� + � + �/� (Eq. 1)

where EMM, EI and ECG are the potential energy of the all atom (MM) region, the interface (I) and

the coarse grained (CG) region, respectively. EI/MM and ECG/I describe the interaction energy

between the interface and the MM region and that between the interface and the CG region,

respectively. EMM, EI and EI/MM have the same form from the GROMOS96 force field (Scott et al.

1999), whereas ECG and ECG/I take the form of the Go-like model. ECG/I ensures the integrity of

the protein backbone. This term includes the bonded interactions between the CG atoms and the

Cα atoms in the interface, as well as the non-bonded interactions between CG atoms and the Cα,

Cβ atoms in the interface. The Go-like model used in the MM/CG scheme to describe the CG

region whose energy. The � term reads:

� = �� ∑ ���|�� − ����|� − ������ ��� + ∑ ���1 − exp�− �!"#�� − �!# − ��!$%&�

�'! . (Eq 2)

The first term describes the interaction between consecutive CG beads (the Cα atoms),

where �� is the force constant and ��! is the equilibrium distance corresponding to the native

distance between CG atoms. Non-bonded interactions are taken into account in the second term

using a Morse-type potential, where ��= 5.3 kJ·mol-1 is the well depth and its modulating

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coefficient is �! = 6/��! nm−1. These two parameters have been already employed in

investigating both soluble and membrane enzymes (Neri et al. 2005; Neri et al. 2008). For

GPCRs the same value for �� were considered, whereas �! is set to 5 + 6/��! nm−1. This setup

ensures the stability of the protein inside its transmembrane site.

The thermal and viscous solvent effects acting on the system is mimicked by using the

Langevin equation with a potential of mean force, ��*�� (Nadler et al. 1987):

,�-./0-1. = −,�2�

-*0-1 + ��*�� + 3��4� (Eq. 3)

where 2� is the friction coefficient and 3� is a stochastic noise satisfying the relations: ⟨3��4�⟩ =0 and ⟨3��4�3!�4′�⟩ = 9�!9�4 − 4:�2�<=2�; where �< is the Boltzmann constant and T is the

temperature. If the I and MM regions are solvent exposed, the solvent is treated in an explicit

way using the SPC water model (Berweger, van Gunsteren, and Müller-Plathe 1995). In the

framework of the MM/CG approach: a drop of water molecules is centered around the MM and I

regions and if a molecule exits from the water shell, its velocity is reflected towards the inside.

Within this approach, water properties are very similar to those of the bulk water in proximity of

the all-atom region, but approaching the drop border located approximately at the interface

region, the water density lowers, providing a rough approximation of the bulk behavior (Neri et

al. 2005).

The presence of implicit membrane is realized by introducing five repulsive walls

�>�, @ = 1,2 … 5� into the system (Fig. 2) (Leguebe et al. 2012). The five walls, around the

protein are described by five corresponding functions using a level-set approach (Osher and

Sethian 1988). The region of points B where all the five >��B� are positive characterizes the

protein site. The wall i itself is formed by the set of points for which >� vanishes. Two planar

walls (>� , @ = 1,2) coincide with the height of the heads of membrane lipids. Two hemispheric

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walls ('outer walls’, >�, @ = 3,4), capping the extracellular and cytoplasmic ends of the protein

are described by the functions >��B� = E� − ‖B − GH�‖ defined only outside the membrane

region. The center GH� of each hemisphere is located at the height of the heads of phospholipids,

above/under the center of mass of the protein. The radius E� of each hemisphere is defined such

that the minimum distance between any protein atoms and the wall is 15 Å. This creates a droplet

of waters around the MM region similar to ref. (Neri et al. 2005; Neri et al. 2008). The

membrane wall >I is defined by >I�B� = EJ − min!NB − G!N, where the distance between the

point r and the closest initial position of Cα atoms G! is computed, and EJ is a distance parameter

with a default value 2.0 Å. Additionally, a smoothing technique (Leguebe et al. 2012) is applied

to avoid discontinuities in the wall.

Boundary potentials ���O��@ = 1,2 … 5� are added to the MM/CG potential energy

function. They are defined as functions of a distance O of an atom from the corresponding walls:

���O� = �- for @ = 1,2; (Eq. 4)

���O� = 4S TUV-W� − V

-X for @ = 3,4,5. (Eq. 5)

In particular, the potential applied to an atom is the one corresponding to the closest wall

>� from that atom, i.e. �� �@ ∶ minB:�B − >��B′�� = O�. �� �@ = 1,2� is purely repulsive; �� �@ =3,4,5� is a softened Lennard-Jones-type potential; ε is the depth of the potential well; and σ is the

finite distance at which the potential �� �@ = 3,4,5� is zero. The minimum of the potential is at

O = 2Z = EJ. Waters, Cα atoms of both MM and CG regions, and atoms belonging to external

aromatic residues Trp and Tyr are influenced by these potentials. The membrane wall potential

�I constrains the shape of the protein while providing a good degree of flexibility. This model

neither includes electrostatics nor allows distinguishing between different types of bilayers.

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The force due to the presence of the wall is derived from the following equations:

[\]]̂ �E� = − _`0_- ∇O�E�. (Eq. 6)

The cut-off distance of the force is set to 7 Šfor the repelling walls �� �@ = 1,2�, and to

1.5EJ for the outer walls and membrane wall �� �@ = 3,4,5�. The first value is chosen such that a

water molecule cannot pass through this distance during one time step, while the second value

guarantees that the force does not affect the MM region. The force is shifted so that it is

continuous at the cut-off distance to avoid a sharp disruption. In addition, it is set to a finite value

(1000 kJ·mol−1·nm−1) near the wall to prevent too large forces acting on the system.

Testing and applying the method for structural predictions. The predictive power of the MM/CG

approach has been tested on a system for which the X-ray structure is available. This is the

human β2 adrenergic receptor (β2AR) in complex with its inverse agonist S-Carazolol (S-Car)

and its agonist R-Isoprenaline (R-Iso) (Cherezov et al. 2007). We performed simulations using

(i) directly the β2AR X-ray structure (Leguebe et al. 2012) and then (ii) a β2AR homology

model built on squid rhodopsin (PDB id: 2Z73) (Murakami and Kouyama 2008) template whose

displays a sequence identity of 20% with the target (Marchiori et al. 2013).

(i) We have used an all-atom MD simulation performed on the same system by Vanni et al.

(Vanni et al. 2011). The MM/CG simulations were carried out for up to 800 ns. In both MM/CG

simulations, the MM region consisted of ca. 10% of the overall systems. This allows us to

achieve a 15-fold speedup compared to MD simulations of the same system (Leguebe et al.

2012). The MM/CG simulations carried out on the β2AR/S-Car and on the β2AR/R-Iso systems

were both in agreement with the corresponding ones with all-atom MD on the same system (Fig.

3). In a second step of the procedure with the aim of eliminating putative bias due to the original

positioning of the ligands and to gain insights into the predictive power of our method, we ran

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additional simulations in which we have translated an rotated the ligand S-Car in a position

different from the crystallographic pose, lacking all the interactions with the residues found in

the X-ray structure of β2AR/S-Car complex. In these new simulations, the ligand migrates to the

correct pose between 150 and 200 ns, forming the key interactions (Leguebe et al. 2012).

(ii) In the case of β2AR model structure, after 0.8 µs of MM/CG simulation time, the β2AR

structure in complex with S-Car is similar to the X-ray structure (root-mean-square-deviation of

the Cα atoms 2 Å). The interactions observed between the ligand and the protein present in the

X-ray structure are reproduced also in the MM/CG simulation (Marchiori et al. 2013).

The same procedure used in the case of β2AR model structure was then applied to one of

the family for which there is no structural template. This is the T2R family. We focus on the

human TAS2R38 (hTAS2R38) receptor in complex with its agonists phenylthiocarbamide (PTC)

and propylthiouracil (PROP) (Fig. 4A) (Marchiori et al. 2013). In this case, two models (A and

B) of hTAS2R38 were selected out of 200 built by multi-template homology modelling. Models

A and B were selected on the basis of a clustering approach. The principal difference between

the models regarded the highly variable extracellular loop EL2 (Fig 1A). Model A showed a loop

conformation closed over the binding cavity while in model B the EL2 loop was in an open

conformation far from the binding pocket. To study the interactions with PTC and PROP, the

two models were then funnelled through a standard docking protocol using the information

driven Haddock program (Marchiori et al. 2013). We have then selected two representative

docking poses for each of the models. Although these models largely satisfied the existing

experimental data (Biarnes et al. 2010), in order to obtain a more accurate description of the

binding poses and the specific receptor-ligand interactions, the four models underwent μs-long

MM/CG simulations, each at room temperature (Fig. 4B). MM/CG identified the best binding

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pose of each agonist. Then, new site-directed mutagenesis experiments were carried out, which

conformed the predicted models (Marchiori et al. 2013). The calculations pointed out key

interactions between hTAS2R38 and its agonists, which would have been impossible to capture

with the standard bioinformatics/docking approach (Marchiori et al. 2013). Moreover, after

MM/CG simulations validated with site-directed mutagenesis experiments, we concluded that

the EL2 loop conformation may resemble the open conformation.

The current version of MM/CG code has been implemented within the GROMACS 4.5

code (Berendsen, van der Spoel, and van Drunen 1995; Lindahl, Hess, and van der Spoel 2001;

Van Der Spoel et al. 2005; Hess et al. 2008; Pronk et al. 2013). Multiple systems can be

simulated in parallel (Pronk et al. 2013). Hence, massively parallel architectures could be

efficiently exploited in HPC-based ligand screening, even performing free energy calculations.

To reach this goal, our lab is currently implementing grand canonical ensemble simulations.

6. Perspectives

In this chapter we have reviewed the efforts undertaken by the scientific community

aimed at characterizing the interactions between GPCRs (for which there is a lack of structural

information) and their cognate agonists. When reliable GPCR templates belonging to the same

subfamily of the target can be identified, state-of-art approaches of homology modeling, docking

and extensive all-atom MD simulations are able to offer a high-resolution description of the

interaction, even at the level of undertaking a structure-based drug design studies. In all the other

cases, which involve the majority of GPCRs at present, state-of-art modeling protocols produce

just low-resolution models. These can hardly be used as the initial step of successful docking

approaches. Indeed, side-chain positioning is likely to be wrong. Extensive MD simulations may

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be then used to explore the conformational space. Unfortunately, long all-atom MD simulations

of a receptor embedded in the membrane are extremely demanding in terms of computational

power. In cases like this, multiscale-hybrid approaches may help. A protocol combining low-

resolution homology modeling, docking and MM/CG simulations has been developed by us to

study the interaction of GPCRs and their cognate agonists, even when the available templates do

not belong to the same subfamily. The encouraging results reported so far leads us to suggest

that in the near future combined methodologies such as those described here may help structure-

based drug design studies.

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Figure captions

Figure 1. (A) Representation of GPCR fold. Trans-membrane helices (TM) are reported as

cylinders. Intracellular (IL) and extracellular (EL) loops are indicated. (B) The human GPCR’s

phylogenetic tree according to the GRAFS system (glutamate, rhodopsin, adhesion,

frizzled/taste2, secretin) (Bjarnadottir et al. 2006). The rhodopsin family can be divided in four

sub-branches (named α, β, γ, and δ). The Frizzled/taste2 group includes two distinct clusters, the

frizzled receptors and the TAS2 receptors. The GRAFS system excludes the olfactory receptors

(located in a sub-branch of the rhodopsin δ-branch) and pheromone receptors of type 1.

Figure 2. MM/CG model of human β2AR; MM and I regions are represent by grey tube, CG

region is represent by spheres, waters are shown in liquorice representation. Five walls around

the GPCR are used to mimic the presence of lipid bilayer: the planar walls (φ1,2) are the grey

sheets located at the height of the membrane lipids head, the outer walls (φ3,4) are the two

hemispheres, the membrane wall (φ5) is the surface around the protein.

Figure 3. Root Mean-Square Fluctuations (RMDF) of β2AR’s backbone atoms. Results for all-

atom simulations (Vanni et al. 2011), MM/CG simulations and CG simulations are shown in

light grey, black and dark grey lines respectively. Results for β2AR/R-Iso and β2AR/S-Car

complexes are shown in the left and right panel, respectively. Residues included in the MM and I

regions are indicated by grey bars. Adapted from (Leguebe et al. 2012).

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Figure 4. (A) Schematic structure of PTC and PROP, agonists of the TAS2R38 receptor. (B)

MM/CG representation of the TAS2R38 receptor in complex with PTC. Water molecules and

residues belonging to the MM and I regions are represented as lines. PTC atoms are represented

as spheres. (C) Binding of PTC and PROP to the TAS2R38 bitter receptor as emerging from

MM/CG simulations and experiments. Adapted from (Marchiori et al. 2013).

Figures

Figure 1.

Figure 2

22

Figure 3.

Figure 4.