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Unsupervised classification of G-protein coupled receptors and their conformational states using IChem intramolecular interaction patterns Florian Koensgen, Franck Da Silva, Didier Rognan, and Esther Kellenberger J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.9b00054 • Publication Date (Web): 13 Aug 2019 Downloaded from pubs.acs.org on August 14, 2019

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Journal of Chemical Information and Modeling

Unsupervised classification of G-protein coupled receptors and their conformational states using IChem intramolecular interaction patterns

Florian Koensgen, Franck Da Silva, Didier Rognan, Esther Kellenberger*

Laboratoire d’innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, 74 Route du Rhin, F-67400 Illkirch

ACS Paragon Plus Environment

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ABSTRACT. Over the last decade, the ever-growing structural information on G–protein coupled receptors (GPCRs) has revealed the three-dimensional (3D) characteristics of a receptor structure that is competent for G-protein binding. Structural markers are now commonly used to distinguish GPCR functional states, especially when analyzing molecular dynamics simulations. In particular, the position of the sixth helix within the seven transmembrane domain (TM) is directly related to the coupling of the G-protein.

Here we show that the structural pattern defined by intramolecular hydrogen bonds (excluding backbone/backbone interactions), ionic bonds and aromatic interactions in the


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TM suits the comparison of GPCR 3D structures and the unsupervised distinction of the receptor states. Firstly, we analyze a microsecond long molecular dynamic simulation of the human ß2-adrenergic receptor (ADRB2). Clustering of the 3D structures by pattern similarity identifies stable states which match the conformational classes defined by structural markers. Furthermore, the method directly spots the few state-specific interactions. Transforming pattern into graph, we extent the method to the comparison of different GPCRs. Clustering all GPCR experimentally-determined structures by clique relative size firstly separates receptors, then their conformational states, thereby suggesting that the interaction patterns are specific of the receptor sequence and that the interaction signatures of conformational states are not shared across distant homologs.


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INTRODUCTION

G–protein coupled receptors (GPCRs) play a key role in the transmission of signal across the cell membrane. Typically, GPCRs modulate the activity of intracellular G-proteins in response to external stimuli such as the recognition of a ligand. 1,2 To do so, they adapt their three-dimensional (3D) structure to various allosteric modulators (ligands, membrane components, neighboring receptors, intracellular effectors). GPCRs are commonly viewed as oscillating between active and inactive states, which respectively triggers or impedes the cellular signaling. However, some GPCRs are able to trigger more than one pathway, selecting between different G-proteins or arrestins, therefore acting as complex molecular switches. 3–9

It has been known for a more than 30 years that GPCR structure is organized around a bundle of seven transmembrane helices. The relative location of helices revealed first in 2000 for bovine rhodopsin

10

has since been confirmed in 54 other receptors. By the

middle of 2018, the Protein Data Bank (PDB) contains 276 experimental 3D structures of GPCR, solved by X-ray crystallography (266), cryo-electron microscopy (9), or solid-state


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nuclear magnetic resonance spectroscopy (1). In twelve of them, the receptor is bound to a G-protein (full or mini form) or a ß-arrestin. Comparative analysis of the GPCR 3D structures has disclosed the structural basis of the receptor competent state for intracellular coupling.11,12 For the sake of simplicity, this competent state will be called

active form here on. Consequently, the non-competent state will be qualified as inactive. The NPxxY water lock 13 is hence mostly found in the active state while the DRY ionic lock

14

is a hallmark of the inactive one, although it is missing in many 3D structures of

inactive GPCRs. Also, GPCR coupling to Gαs or its mimics causes the opening of the intracellular receptor cavity, which is easily characterized by the increase of distance between helices 3 and 6. Recently, 3D structures of three different GPCRs in complex with Gαi/o have shown that the amplitude of this motion depends on the G-protein type. 15–19

The transition between two conformational states of several class A GPCRs has been modeled by unbiased molecular dynamics (MD).

20–22

The start and end points of

simulations are close to crystal structures of the receptor in different states. For example,


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the inactivation of the human ß2-adrenergic receptor (ADRB2) was modeled from the crystal structure of the Gαs-bound receptor after removal of the G-protein. 22 The closing of the intracellular cavity of the receptor, observed after 4 microseconds of classical simulation, was notably monitored by measuring the distance between key atoms positioned on helices 3 and 6.

However, is it possible to capture functionally relevant conformational changes without relying on predefined structural determinants? We assume here that the functional state of a GPCR is determined by its 3D structure, which in turn is encoded in structural patterns defined by hydrogen-bonds (H-bond), aromatic and ionic interactions. We focused on the seven transmembrane domain (TM) because the size and folding of this domain is conserved across the entire protein family. We ignored the H-bonds formed between two backbone atoms of the same helix, so that the interaction pattern does not represent the shape or position of the transmembrane helices of the receptor. For the same reason, we did not consider hydrophobic contacts, which cover the entire TM and would be predominant in the interaction pattern.


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We have adapted the tools for detection, analysis and comparison of ligand/protein interactions in the IChem software package for the detection, analysis and comparison of intramolecular interaction patterns of GPCR structures.

23

We showed that clustering by

similarity of interaction patterns the MD snapshots of ADRB2 inactivation MD trajectory defines stable conformational states with specific G-protein binding properties. The same analysis made on all available crystal structures of this receptor revealed conservation but also discrepancies between the interaction patterns derived from the experimental and MD simulated structures. Lastly, we compared all GPCR 3D structures from the PDB and concluded that TM interaction patterns are characteristic of the receptor full sequence.


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Figure 1. Encoding the transmembrane intramolecular interactions of GPCR. A. Types of interaction considered in the study. From left to right: intra-helix hydrogen bond, interhelix hydrogen bond, ionic bond, and aromatic stacking. Hydrogen bonds involve either two sidechains or one sidechain and one backbone. B. Interactions are encoded in a cloud of interaction pseudo-atoms. For the sake of illustration are represented the interactions detected in bovine rhodopsin (PDB ID: 1f88). C. Interactions are encoded in a binary 3D-matrix. Presence (1) or absence (0) of an interaction between two residues is reported using two dimensions. The third dimension gives the interaction type. Panels A and B show how an interaction is represented by a triplet of points. The first one is positioned on the ligand atom, the second on the protein atom and the third at middistance between the first two. In panels A, B and C, the color indicates the interaction type (blue: hydrogen bond, red: ionic bond and green: aromatic stacking).


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RESULTS AND DISCUSSION

In this study, we simplified a GPCR 3D structure of to a simple description of the noncovalent interactions observed in the TM. We considered inter-helix H-bonds, ionic bonds and aromatic interactions (Figure 1A). We also included the intra-helix H-bonds involving a side chain (Figure 1A). The set of interactions detected in a 3D structure was converted into either a cloud of pseudoatoms (Figure 1B) or a look-up table (Figure 1C), which were then used to compare GPCR structures. Noteworthy, the cloud of pseudoatoms was transformed into a generic graph thereby enabling the comparison of two different GPCRs, even distant homologs (e.g., class A vs class C).

Unsupervised analysis of MD trajectories detects different activation states

The MD trajectory of ADRB2 published by Dror et al.

22

has simulated the transition

between two receptor states, as distinguished by two geometrical descriptors deduced from the comparison of crystal structures of ADRB2 in complex with the partial inverseagonist carazolol (PDB ID 2RH124), and with the agonist BI-167107 and a G-protein mimetic nanobody (PDB ID 3P0G 25). The authors divided the trajectory in three sections


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(active, inactive and intermediate) based on the distance between Cα atoms of residues 3.50 and 6.34 and the deviation of the NPxxY atomic coordinates from the reference structure (Figure 2A). According to the authors' classification, the receptor remained in the active state for 350 ns, then experienced an intermediate state for 3.9 µs before reaching the inactive state. We analyzed the parts of the trajectory that contain the two state transitions, i.e. 1.4 µs of simulation. Clustering the MD snapshots by similarity of non-polar TM interactions patterns defined three states accounting for ca 1.1 µs of the trajectory (Figure 2B). Importantly, the three states match the distance-based activity classes: the active state, the inactive state and the intermediate state that precedes it. No consistent interaction patterns were detected during 300 ns immediately following the receptor inactivation (cluster 2 in Figure 2B and Figure S1), thus suggesting that this part of the trajectory does not correspond to a stable conformational state.


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Figure 2. Distinction of the conformational states of human ß2 adrenergic receptor simulated by molecular dynamics. A. Classification into active (green), inactive (red) and intermediate states (yellow) based on the distance between the Cα of Arg1313.50 and Leu2726.34 and the root mean square deviation to the coordinates of an inactive reference (PDB code: 2RH1) in the NP7.50xxY motif. 22 B. Clustering of snapshots based on similarity of transmembrane intramolecular interaction patterns. C. Clustering of snapshots based on similarity of intramolecular interaction patterns built from the structure of the TM and loops. In panels B and C, data points are colored in grey if the intra-cluster similarity is low (i.e. non-homogenous cluster); otherwise they are colored according to the predominant state in the cluster (active in green, inactive in red, or intermediate in yellow).


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Numerous works have outlined the role of loops in ligand binding and in activation process.26 Taking into account intracellular and extracellular loops in addition to the TM, the analysis of intramolecular interactions again distinguished the active state, the inactive state and the intermediate state that precedes it (Figure 2C). However, the state boundaries are more clearly delineated in the trajectory and two additional intermediate states were defined, following the active state. Therefore, the information provided by the TM was sufficient to identify important ADRB2 states, and some of the interactions formed by loops characterized sub states which are not obviously linked to activation.

The analysis of a second example confirmed the power of the approach. The accelerated MD approach developed by Miao et al.

has successfully simulated the

21

partial opening of the intracellular part of the muscarinic M2 receptor, starting from an active conformation of the receptor (PDB ID 4MQS

27)

that has been deactivated.

Clustering of the MD trajectory based on similarity of TM intramolecular interaction patterns yielded three stable states, covering less than half of the trajectory yet matching


12

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the authors' geometry-based definition of the inactive state and of two intermediate states (Figures S2 and S3). The use of loops confirmed this grouping of MD snapshots along the trajectory. Information provided by loops issued three additional clusters in the part of the trajectory where intramolecular transmembrane interactions fluctuated. The ADRB2 and ACM2 examples both suggested that TM alone bears the signature of some of the receptor states and that interactions involving loops help to define the receptor state when no clear pattern is found by the TM analysis.

The proposed computational method allowed an easy and rapid identification of statespecific interactions. In the ADRB2 example, we identified between 43 and 46 transmembrane interactions in the three clusters of the MD trajectory. There are almost only H-bonds, half of which occurring within the same α-helix all along the 7TMs. Almost two thirds of the H-bonds are common to the three states (Figure S4A), while each state is characterized by two to twelve interactions. At maximum three state-specific interactions are well preserved in the corresponding portion of the trajectory. The active state is hence characterized by three predominant H-bonds (Figures 3A and S5A). One


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is formed between the most conserved residues in the TM2 of class A GPCRs (Asp792.50) and a residue in TM3 neighboring the “transmission switch” involved in the initiation of GPCRs activation following ligand binding (Ser1203.39 precedes the key functional residue 3.40).

28

The two other H-bonds involve TM4, where no key mechanical sites have yet

been characterized. 29 By contrast, the two predominant H-bonds in the inactive state are related to the well documented ionic lock, the only non H-bond state-specific interaction (Figures 3A and S5B). 30 The one formed between the conserved residues 3.50 and 6.30 has been expected to strengthen the ionic lock. Similarly, the H-bond between residues 5.58 and 6.34 likely prevents the stabilization of the broken lock via a H-bond between 3.50 and 5.58. Overall, the method has detected key residues in GPCR activation without in depth analysis of local motion or sequence conservation.


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Figure 3. Transmembrane intramolecular interaction patterns in the human ß2 adrenergic receptor. A. Receptor simulated by molecular dynamics. Diagram only shows the interactions which are specific of cluster 1 (“active state”, in green), cluster 3 (“intermediate state”, in yellow) and cluster 4 (“inactive state”, in red). B. Crystallographic structures in the Protein Data Bank. Diagram only shows the interactions which are specific of the active (in green) or inactive state (in red). In A and B, dots represent hydrogen bonds within a residue (diagonal), hydrogen bonds between two residues (bottom left half) and ionic or aromatic interactions between two residues (top right half). Dot size is proportional to the interaction frequency. Frequent interactions (> 30%) are indicated with residue Ballesteros-Weinstein numbers.


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Comparison of interaction patterns across experimental structures of the ß2-adrenergic receptor

We pursued our investigation by comparing the 19 available 3D structures of the ß2adrenergic receptor (Figure 3B). Clustering by similarity of interaction patterns separated the 13 3D structures of the receptor bound to an antagonist or an inverse agonist (inactive state) from the six 3D structures bound to an agonist and coupled to a trimeric G-protein or to an engineered G-protein mimetic nanobody (active state) (Figure S6). The interactions common to both states are almost the same as those observed in the abovedescribed MD trajectory (Figure S4B). By contrast, state specific interactions differ significantly (Figure 3B). In the active state, the predominant interactions are the three Hbonds already observed in the simulated 3D structures, and two additional H-bonds linking TM2 and TM3 in crystallographic 3D structures. In the inactive state, crystallographic structures also show more H-bonds than simulated 3D-structures, mostly in TM3 and TM5, but miss the ionic lock. Discrepancies are mainly explained by different experimental conditions, typically changes in the ADRB2 amino acid sequence, variable


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protonation schemes, crystallographic constraints (Figure S7). For example, the H-bond between residues 5.45 and 3.41, which is characteristic of inactive crystallographic structures and of active simulated structures, well illustrates effects of amino acid mutations (Figure S7A). The most important change in sequence concerns the third intracellular loop (ICL3), which has been replaced with lysozyme in the crystallized receptor while there is a gap in the simulated receptor. Consequently, Glu2686.30 is trapped by an arginine of lysozyme in the chimeric protein, and therefore does not engage into the ionic lock (Figure S7B). Lastly, interaction patterns also revealed effect of ligands binding the transmembrane cavity, and more especially in ADBR2 affecting serine residues in TM5 (Figure S7C).

Although there are no sequence differences and the same ligands are bound, variations are expected between the interaction networks obtained from the crystallographic and simulated structures of a receptor. A 3D structure solved by X-ray crystallography is a picture of a stable or stabilized state whereas MD snapshots give a more exhaustive description of the many conformations spanned by the receptor in a particular state.


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Recent experimental data show that a full continuum of states, ranging from several fully active to several inactive states, is accessible to a GPCR.31

Comprehensive analysis of interaction patterns in GPCRs 3D structures Do the intramolecular interaction patterns suit the distinction of similar conformation states of different GPCRs?

To answer this question, we analyzed the 53 GPCRs

described in the PDB (Table S1). The 265 available 3D structures were divided into three classes. The first class, comprising 12 GPCRs in the active state, includes receptor structures bound to either a G-protein, a G-protein fragment, a G-protein mimetic antibody, an engineered nanobody or a ß-arrestin. The second class, including 13 GPCRs in the intermediate state, describes uncoupled receptors bound to an agonist. The third class, comprising 47 GPCRs in the inactive state, corresponds to uncoupled receptors bound to an inverse agonist or an antagonist.

Table 1. Clustering of GPCR structures by similarity of transmembrane intramolecular interaction patterns.


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Functional annotation Numbe Number Classification

r

of per

classes classc Activation

statea

(ex: inactive) Receptorb

(ex:

ADRB2_HUMAN) Receptor

and

activation

stateab

(ex:

inactive

Group

of

interaction

patterns

Number of

similar

Number

singletons of clusters d

Number

Correct

of

prediction

singletons

e

(%)

3

49-257

0

1

0

72.1

59

2-53

12

45

38

69.3

78

2-36

21

50

48

78.9

ADRB2_HUMAN) a active,

intermediate and inactive states define respectively GPCR bound to G-protein

(or a fragment, a mimetic antibody, an engineered nanobody or a ß- arrestin), uncoupled GPCR bound to an agonist, and uncoupled receptors bound to an inverse agonist or an antagonist. b

Receptors are distinguished by their UNIPROT ID.

c Minimal

and maximal number of 3D structures in a class.

d

Class which contains a single 3D structure.

e

Number of 3D structures predicted in the correct class or predicted as true singleton

divided by the total number of 3D structures.

Clustering of GPCRs 3D structures by similarity of interaction patterns did not group the 3D structures in the same state but distinguished receptors, as defined by their amino acid sequence (Table 1). Increasing the level of similarity within cluster further divided the


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3D structures of a receptor, mostly according to the receptor state. In total, 280 3D structures of 53 receptors were thus distributed into 50 homogenous clusters and 48 singletons with ca 80% of the 3D structures correctly grouped by receptor and activation state (Figures 4 and S8). For example, the inactive 3D structures of squid and bovine rhodopsin populate distinct clusters. About one third of the wrong predictions correspond to clusters containing two receptors of the same family and in the same state. For example, the human type 1 and 2 adenosine receptors form a single cluster. Familyspecific rather than sequence-specific interaction patterns are also found for adrenergic, muscarinic, orexin receptors and for the viral receptors. Similarly, the inactive 3D structures of delta-type opioid receptor define a species-independent interaction pattern. About 15% of the wrong predictions correspond to merging of the intermediate and active states of a receptor. All other inaccurate predictions represent 3D structures which were separated from their main cluster and ended up as minor clusters or as a singleton. These 42 3D structures show on average fewer intra-TM interactions. Some of them have been built from medium to low resolution data (27 and 13 3D structures with resolution ≥3.0 Å and ≥3.5 Å, respectively).


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Figure 4. Clustering of G-protein coupled receptors based on transmembrane intramolecular interaction patterns similarity. Each cluster is represented with its network of experimental three-dimensional structures. Receptors in a cluster are indicated with their gene name preceded by a letter for the source organism (b: bovine, f: frog, h: human, hpv1: human papillomavirus, hpv2: human papillomavirus strain AD169, m: mouse, r: rat, s: squid, t: turkey). If a cluster contains more than one protein, the predominant one is


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indicated first. The 3D structures are divided into three classes (active, intermediate and

inactive, see text) and colored accordingly (green, orange and red circles, respectively). Empty circles reveal peculiarities in the clustering: minority protein(s) in a cluster, minority class in a cluster, or the smaller of two clusters of the same receptor in the same class.

Visual inspection of the transmembrane intramolecular interactions in Class A GPCRs further confirmed that a simple common signature of receptor state has not emerged from the analysis. The three most frequent interactions of the inactive state appear in only 16% to 25% of the receptors (Figure 5). They nevertheless involve key functional sites of class A GPCRs, including the ionic lock between residues 3.50 and 6.30 and the allosteric sodium binding site, which connects TM2 to TM3 via conserved residues (2.49, 2.50, 3.35).

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The most frequent interactions in the active and intermediate states cover less

than 16% of the receptors, i.e. two receptors in each state. Moreover a few interactions are observed in two states, questioning the relevance of the functional annotation (Figure S9). Interestingly, several interactions are common to 40% or more of the receptors, independently of their state, and may therefore play a structural role in the helix bundle


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architecture. For example, H-bonds connect TM7 to TM1 and TM2 via the conserved residues Asn1.50 and Asp2.50 (Figure 5). The present analysis of transmembrane interaction patterns hence well complements the consensus scaffold of non-covalent contacts, as proposed by Venkatakrishnan et al.11 Both studies agree on the importance of TM3, which here appears in several state-specific as well as generic polar interactions, and which has previously been qualified as structural hub because of contacts occurring all along the helix with successively the second extracellular loop (ECL2), TM4, TM6, TM2, TM5 and the second intracellular loop (ICL2).


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Figure 5. Transmembrane intramolecular interactions of class A G-protein coupled receptor. Colors distinguish common (in blue) from class-specific interactions (active in green, intermediate in yellow and intermediate in red). Dot size is proportional to the interaction frequency normalized by receptor. Dots represent hydrogen bonds within a residue (diagonal), hydrogen bonds between two residues (bottom left half) and ionic or aromatic interactions between two residues (top right half). Most frequent interactions are


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indicated on the diagram. Residue numbers meet the Ballesteros-Weinstein definition. Percent refers to the number of receptors in a class if the interaction is class specific, and otherwise to 63, which represents the cumulated number of receptors in the three classes.

CONCLUSION

Although hydrophobic contacts are predominant in the core of GPCR structure and more especially in the transmission switch which connects the orthosteric and the G protein docking sites, 11,28 registering directional and ionic interactions in GPCR transmembrane helices is sufficient to distinguish different activation states, independently of any userdefined structural characteristic.

Comparing GPCR 3D structures by similarity of interaction patterns constitutes a powerful method to compartmentalize the conformational space sampled during MD simulation. Importantly, the method indicates whether a pattern is stable or fluctuating, thus allowing to unambiguously determine which interactions are relevant of a state. In addition, patterns are information-rich although they are small, with fifty or less interactions. On the


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ADBR2 example, the method has revealed not only the functional sites conserved across class A GPCRs, but also the sequence-specific paired residues.

When applied to all the GPCRs of known experimental structures, the method groups the 3D structures of the same receptor (or close homologs) sharing the same state. Patterns are dominated by sequence-specific interactions. The small proportion of state-specific interactions are not common to all GPCRs. Overall, the method is unable to a priori indicate what is the activation state of a GPCR 3D structure, but it suits the partitioning of many 3D structures of the same receptor (or close homologs) according to activation state. It also allows the detection of important structural and functional sites of a receptor (or close homologs), as well as artifactual interactions caused by the receptor stabilization for experimental study.

EXPERIMENTAL SECTION

Detection of interactions


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Interactions were detected by using the INTS module of the IChem software.23 Distance thresholds to detect an ionic interaction were set to a 2.5-4.0 Å range. Distance and angle thresholds to detect aromatic interaction were 3.2 - 4.0 Å and 180° ± 30° (face-to-face interaction) or 90° ± 60° (face-to-edge interaction), respectively. Distance and angle thresholds to detect H-bond were 2.5 - 3.5 Å and 180° ± 60°, respectively. Distance criteria were smoothened when analyzing experimental structures (ionic interaction: 2.5 4.5 Å, H-bonds: 2.2 - 3.5 Å). GPCR structure preparation The two MD trajectories have been kindly provided by their authors.21,22 The complete ADRB2 trajectory (referred as simulation number 11 in original publication)22 represents 10 µs of simulation, out of which 8000 frames (1,4 µs) were analyzed. The complete muscarinic M2 receptor trajectory (referred as simulation number 10 of the M2IXO system in original publication) 21 represents 409.8 ns of simulation, out of which 4097 frames (409.7 ns) were analyzed. The snapshots were extracted from the trajectory using VMD version 1.9.2 33 and AMBER version 16.34


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The inventory of GPCR experimental structures in the PDB and their functional annotation were extracted from the GPCRdb

35

and GPCR-EXP databases. PDB files were

downloaded from the RCSB PDB.36 Hydrogen atoms were added using Protoss.37 GPCRs with five or more modifications in TM sequence (deletion or mutation) were filtered out. The 7TMs was defined according to Bissantz et al.,38 and encompassed residues 1.30 to 1.59, 2.38 to 2.67, 3.22 to 3.54, 4.40 to 4.62, 5.35 to 5.60, 6.30 to 6.55,7.33 to 7.53, numbered using the Ballesteros-Weinstein scheme. 39

Comparing the structures of the same GPCR Interactions are encoded in a binary matrix. Two dimensions represent residues in the TM: a bit is switched on if an interaction is detected between the two residues. The third dimension gives the interaction type (intra-helix hydrogen bond, inter-helix hydrogen bond, ionic bond, aromatic stacking). The similarity between two matrices is expressed as a Tanimoto coefficient, as follows:


28

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MD snapshots were clustered by hierarchical agglomerative method with complete linkage using the ward.D2 method in R-cran (“cluster” command line).40,41 The cut was done incrementally from the bottom to top of the dendrogam. The best cutting point was chosen to minimize the number of clusters while maximizing the proportion of the trajectory covered by homogenous clusters (Tables S2 and S3). A cluster was defined as homogenous whether the first quartile of its similarity score distribution is larger than the third quartile of the inter-cluster similarity score distribution. The trajectory was divided in non-overlapping portions made of ten consecutive snapshots. Each portion was assigned the homogenous cluster number that represented eight, nine or ten snapshots. If there was no predominant homogenous cluster, the portion was labelled null. Contiguous portions with the same cluster number were merged. If several discontinuous portions had the same cluster number after this fusion step, only the largest portion was labelled with the cluster number (the other were labelled null) and was therefore considered to compute the proportion of the trajectory covered by homogenous clusters. Comparing the structures of different GPCRs


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Interactions are encoded into a graph as previously described.42 Two graphs are compared by clique detection using the GRIM module of IChem. A similarity score is deduced from the maximal common subgraph as follows:

The structures were clustered by hierarchical agglomerative method with single linkage using an in-house python script. The cutoff used to generate the various clusters are shown in table S1. Plotting interaction patterns Interactions which are present in less than 10% of the structures in a cluster of MD snapshots were ignored, because of their dependency to the sampling.

IChem

is

available

for

nonprofit

academic

research

at

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to prepare input for IChem and to post-process IChem data are available upon request to the authors.


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ASSOCIATED CONTENT

Supporting Information. Table S1. Clustering of GPCR structures by transmembrane intramolecular interaction patterns.

Table S2. Determination of the number of

clusters representative of the β2 adrenergic receptor trajectory. Table S3. Determination of the number of clusters representative of the type 2 muscarinic receptor trajectory. Figure S1. Similarity between the snapshots of β2 adrenergic receptor trajectory. Figure S2. Similarity between the snapshots of type 2 muscarinic receptor trajectory. Figure S3. Distinction of the conformational states of type 2 muscarinic receptor simulated by molecular dynamics. Figure S4. Transmembrane intramolecular interaction patterns in β2 adrenergic receptor. Figure S5. Key intramolecular interactions observed in the MD simulation of β2 adrenergic receptor. Figure S6. Clustering of the crystallographic structures of β2 adrenergic receptor. Figure S7. Structural differences between crystallographic and simulated 3D structures of adrenergic receptor. Figure S8. Clustering of G-protein coupled receptors by similarity of transmembrane intramolecular interaction


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patterns yields 48 singletons. Figure S9. Transmembrane intramolecular interactions of class A G-protein coupled receptors.

AUTHOR INFORMATION

Corresponding Author *Email: [email protected], phone: +33 368854221

Author Contributions EK and FK designed the study. FK and FDS did the experiments. FK, and EK analyzed data; and EK, FK and DR wrote the paper. All authors have given approval to the final version of the manuscript.

Funding Sources


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The authors would like to thank the financial support of ANRS (France REcherche Nord&Sud Sida-hiv Hépatites) and the French National Research Agency (ANR) through the Programme d'Investissement d'Avenir (ANR-10-LABX-0034).

ACKNOWLEDGMENT The authors kindly acknowledge RO Dror and Y Miao for sharing data, Guillaume Bret for technical support, and the calculation center of the IN2P3 (CNRS, Villeurbanne, France) for allocation of computing time.

ABBREVIATIONS 3D, three-dimensional; ACM2, muscarinic M2 receptor; ADRB2, β2-adrenergic receptor; GPCRs, G–protein coupled receptors; PDB, Protein Data Bank; TM, transmembrane domain

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TABLE OF CONTENTS GRAPHIC


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Unsupervised classification of G-protein coupled ... - ACS Publications

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