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Measuring the Conformational Distance of GPCR-related Proteins Using a Joint-based Descriptor

Scientific Reportsvolume 7, Article number: 15205 (2017) | Download Citation

Abstract

Joint-based descriptor is a new level of macroscopic descriptor for protein structure using joints of secondary structures as a basic element. Here, we propose how the joint-based descriptor can be applied to examine the conformational distances or differences of transmembrane (TM) proteins. Specifically, we performed three independent studies that measured the global and conformational distances between GPCR A family and its related structures. First, the conformational distances of GPCR A family and other 7TM proteins were evaluated. This provided the information on the distant and close families or superfamilies to GPCR A family and permitted the identification of conserved local conformations. Second, computational models of GPCR A family proteins were validated, which enabled us to estimate how much they reproduce the native conformation of GPCR A proteins at global and local conformational level. Finally, the conformational distances between active and inactive states of GPCR proteins were estimated, which identified the difference of local conformation. The proposed macroscopic joint-based approach is expected to allow us to investigate structural features, evolutionary relationships, computational models and conformational changes of TM proteins in a more simplistic manner.

Introduction

Transmembrane (TM) proteins are essential in cellular and biochemical processes that are related directly to the external environment. TM proteins serve as the primary targets of medicinal drugs because of their important functional activities, such as signal transduction1, ion channeling2,3, energy metabolism4, and drug recognition5. TM proteins can be divided into two types: α-helical and β-barrels. In particular, α-helical proteins are the major category of TM proteins that are present in the inner membrane of bacterial cells and the plasma membrane of eukaryotic cells. Approximately 27% of proteins are estimated to be α-helical TM proteins in humans6. The structures of helical TM proteins are strongly related to their physical properties, such as folding, stability, and functions7,8,9. Their structures also provide information about how TM proteins have evolved and connected with each other10. Therefore, a study on the structural or conformational features of helical TM proteins has been an important issue. For example, various studies on their TM topology11,12,13,14, helix-helix packing pattern15,16,17,18,19,20, and structural diversity21,22,23,24,25 have been performed.

Measuring the structural distance or difference in proteins is crucial26. This is strongly related to the classification of proteins in nature, prediction of the protein structures, and the design of artificial proteins. Various approaches have been developed to measure the structural distance of proteins27,28,29. They are generally based on a microscopic description of the protein structures. A representative example is to estimate the structural distance of proteins using the Cα atom-based RMSD (Root Mean Square Deviation)30,31,32,33. Indeed, such microscopic descriptor-based approaches are effective in measuring the structural distance of proteins at the atomic level. On the other hand, the structural distance or difference in proteins can be measured on the macroscopic level34,35. Quantifying the distance of protein structures based on the topology of secondary structures is a representative example of the macroscopic approach36,37. When dealing with large-scale proteins, such as multi-protein complexes or TM proteins, it is advantageous to use macroscopic approaches, even though they cannot provide detailed information on the atomistic scale. Despite the loss of information on the atomistic level, they allow an examination of the conformational features of proteins in a more simplistic and effective manner.

Recently, we proposed a new macroscopic descriptor of protein structures, called joint-based descriptor38. The descriptor uses the joints of secondary structures, such as α helices, β sheets, and loops as the basic constituents. In that descriptor, the dihedral angles of the joints are effective in defining the conformation of proteins. In that study, the approach was applied successfully to investigate the conformational features of the TM proteins by analyzing a dataset of non-homologous TM proteins. For example, the allowed and disallowed regions of their joint-based dihedral angles were examined, which provided information on the possible conformational space of the helical arrangement in TM proteins. Further analyses not only identified the common patterns of helical arrangement and extension, but also detected some geometrically symmetric protein pairs in a non-homologous TM dataset.

In this study, a joint-based descriptor was applied to measure the conformational distance of helical TM proteins on a macroscopic level. The basic strategy was to identify the joint-based dihedral angles specific to a TM protein family, and estimate how far the joint-based dihedral angles of an interesting target TM protein deviate from the identified angles of the TM protein family. Here, the strategy was implemented to measure the conformational distance between the GPCR A protein family and its related structures. The GPCR A protein family, which is one of the largest 7TM families, engages in most of the signaling activities and is a major target for drug discovery39,40. More specifically, the following three independent case studies were performed: (i) the approach was applied to identify how far the global and local conformations of the 7TM proteins in the PDB database are from the GPCR A family; (ii) the approach was used to validate the computational models of the GPCR structures at the joint-based coordinate level, and (iii) the approach was applied to study the conformational difference between the active and inactive states of the GPCR proteins.

Results

Macroscopic description of the 7TM structure using a joint-based descriptor

The joint-based descriptor defines a protein conformation through the dihedral angles of the joints of secondary structures, and the details of the descriptor were introduced in the previous report38. This section briefly reviews how the descriptor is applied to define TM conformations using the typical structure of a 7TM protein, which displays 7TM helices and 6 loops (Fig. 1). To present the structure based on the joint approach, a set of joints associating the helices and loops are selected. In particular, the C-alpha carbons of the starting and ending residues of each TM helix are considered as structural joining points and employed as the structural elements of a protein structure. Fourteen joint points (P 1–14 ) can be assigned to a 7TM protein composed of seven helices (H 1–7 ) and 6 loops (L 1–6 ). The first dihedral angle involving four joints (P 1–4 ) can be determined by measuring the angle between two planes made by (P 1–3 ) and (P 2–4 ). Similarly, the second dihedral angle can be measured by relating the structural joints (P 2–5 ), and the (P 3–6 ) joints are used to determine the third, and so on. The dihedral angles are classified into two types: Ω and λ types. The dihedral angle determined by the four joints in the Helix-Loop-Helix, such as the first and third dihedral angles, corresponds to the Ω type. In a similar manner, the dihedral angle determined by the four joints in a Loop-Helix-Loop, such as the second and fourth dihedral angle, is designated as the λ type. For the dihedral angles, the clockwise angle (from 0° to 180°) is assigned a positive value and the counter-clockwise angle is assigned a negative value (from 0° to −180°), as shown in the inset in Fig. 1. The conformation of a 7TM protein can be represented by a set of two types of dihedral angles, i.e., Ω1 to Ω6 and λ1 to λ5, at the macroscopic level. The detailed account to define the structural joints and the dihedral angles of the 7TM proteins used in this study are described in Methods.

Figure 1
Figure 1

Joint-based description of 7TM protein structure. H1 to H7 are helices, L1 to L6 are loops, and P1 to P14 are joint points. Ω-type dihedral angles, such as Ω1, are defined by the four joint points in the Helix-Loop-Helix, such as P1, P2, P3, and P4. The λ-type dihedral angles, such as λ1, are defined by the four joint points in the Loop-Helix-Loop, such as P2, P3, P4, and P5. The figure presents the projection for ideal 7TM through Ω1–6 and λ1–5. The inset shows the example of the assignment of the positive and negative signs for dihedral angles using the projections for Ω1 and λ1, where the positive (+) and negative (−) signs represent the clockwise and counter-clockwise angles, respectively.

Strategy to measure global and local distances of a 7TM protein from GPCR A family

As mentioned in the Introduction, the primary objective of this study was to demonstrate how the joint-based descriptor can be applied to measure the conformational distance of an interesting 7TM protein from the GPCR A family. For this, a scoring function called the J-score was devised to quantify the differences between the dihedral angles of the joints specific to the GPCR A family and the corresponding dihedral angles of an interesting 7TM protein. This section describes how the joint-based dihedral pattern for the GPCR A family was obtained and how the J-score was defined. A strategy to measure the global and local distances between GPCR A family and a target 7TM protein is also proposed based on the estimated J-scores.

The first step to obtain the dihedral angle pattern specific to the GPCR A family was to select the representative proteins from the GPCR class A family proteins in the Protein Data Bank (PDB). For this, at least one receptor member type protein with high resolution was selected from each subfamily of the GPCR A family, which formed a non-redundant dataset composed of 27 proteins. The detailed procedure to obtain the 27 proteins is described in the Methods section. The proteins in the dataset were analyzed using the joint-based descriptor, which provided the 11 dihedral angles, as shown in Fig. 1 for each protein. SI Table 1 lists the PDB ID codes, subfamily types, and 11 dihedral angles of the 27 proteins. The mean and standard deviation (SD) of each dihedral angle estimated from SI Table 1 and are summarized in Table 1. A set of the estimated mean values for the 11 type dihedral angles, i.e., Ω1 to Ω6 and λ1 to λ5, was defined as a specific dihedral angle pattern for the GPCR A family.

Table 1 Mean and Standard Deviation of the dihedral angles for 27 GPCR_A structures.

Two types of J-score were devised to measure the local and global conformational distances between a target 7TM protein and GPCR A family. To measure the local conformational distance, the typical Z-score41,42,43, which suggests how far the observed value is away from the mean value by the number of SD, was employed and called Ji, i.e., the J-score for the dihedral angle, i. Equation (1) defines Ji, where Xi is Ωi or λi for a target protein. μi and σi are mean and SD of each Ωi or λi for GPCR A family in Table 1, respectively. The Ji presents how much the dihedral angle i of the target TM protein deviates from the mean dihedral angle i of the GPCR A family. To measure the global conformational distance, the J-scores for the 11 dihedral angles were normalized by the root mean square, called Jtot (Equation (2), N = 11 for 7TM protein). Jtot denotes how much the overall dihedral angle pattern of a target 7TM protein deviates from the overall dihedral angle pattern specific to the GPCR A family determined by the set of 11 mean dihedral angles.

$${{\bf{J}}}_{{\bf{i}}}=\frac{|{\bf{X}}{\bf{i}}-{\boldsymbol{\mu }}i|}{{\boldsymbol{\sigma }}{\bf{i}}}\,(|{\bf{X}}{\bf{i}}-{\boldsymbol{\mu }}i|\le {180}^{\circ }),\,{\bf{o}}{\bf{r}}\,\frac{\{{360}^{\circ }-|{\bf{X}}{\bf{i}}-{\boldsymbol{\mu }}i|\}}{{\boldsymbol{\sigma }}{\bf{i}}}\,(|{\bf{X}}{\bf{i}}-{\boldsymbol{\mu }}i| > {180}^{\circ })$$
(1)
$${{\bf{J}}}_{{\bf{tot}}}=[\sqrt{\frac{\sum {({{\bf{J}}}_{{\bf{i}}})}^{2}}{{\bf{N}}}}]$$
(2)

The calculated J-scores are interpreted in two ways. The first is a qualitative interpretation that a target protein is structurally closer to the GPCR A family as the measured J-score becomes smaller and more distant with increasing score. The other is a quantitative interpretation based on the values of the J-scores. For this, a set of J-scores for the selected 27 GPCR A family proteins are used as a reference. When the J-score of a target protein is in the range of J-scores for the reference set, the conformation of the target protein is considered to be “GPCR A family-like”. When the J-score of a target protein is more than the maximum value for the reference set, the target protein is classified as a “GPCR A family-near” or “GPCR A family-far” protein depending on its J-score. In this grouping, the score of 4 is used as a criterion, which is generally used to distinguish outliers in the Z-score statistics44. In summary, the target proteins are classified into the “GPCR A family-like”, “GPCR A family-near”, and “GPCR A family-far” when 0 ≤ Jscore ≤ Jmax of the reference set, Jmax of reference set <Jscore ≤ 4, and Jscore > 4, respectively.

Measurement of conformational distance between GPCR A and other 7TM proteins

A structural comparison between protein families or superfamilies provides information on how the proteins have been evolved structurally and functionally45,46,47,48. In addition, it can be applied to many areas of structural bioinformatics, including homology modeling, fold recognition, and structural genomics49. GPCR A family belongs to the rhodopsin-like superfamily in 7TM fold. As a case study, the global and local structural distances between the GPCR A family and other proteins sharing common 7TM topology were determined by measuring and comparing their J-scores. As mentioned in the previous section, all types of J-scores for the 27 GPCR A family proteins were measured (SI Table 2) and used as a reference to analyze the data.

First, the conformational distances of the proteins in the rhodopsin-like superfamily from GPCR A family were evaluated. The rhodopsin-like superfamily contains 4 different families other than the GPCR A family, i.e., Microbial and Algal rhodopsin, Class B (Secretin), Class C (Glutamate), and Class F (Frizzled). All the non-redundant proteins of the 4 families in the PDB were selected, and their joint-based dihedral angles and J-scores were quantified, as shown in SI Tables 3 and 4, respectively. Figure 2(a) shows the measured Jtot-scores of the 4 families with the reference score of the GPCR A family. The Jtot-scores of the proteins belonging to the Microbial and Algal rhodopsin family and GPCR Class C family (Glutamate) were clearly higher than those of the GPCR A family proteins, whereas the Jtot-scores of GPCR class B (Secretin) and GPCR class F (Frizzled) family proteins were very close to the Jtot-scores of the GPCR A family proteins. These results suggest that the proteins in the Microbial and Algal rhodopsin family and GPCR Class C (Glutamate) family are relatively distant from the GPCR A family in the global conformation compared to the GPCR class B (Secretin) and GPCR class F (Frizzled) family proteins. On the other hand, the Jtot-scores of the four families were all less than 4, which suggests that there are no proteins classified into “GPCR A family-far” in terms of global conformation. To examine their local conformational distances, the Ji-scores for the individual Ω angles or λ angles were also compared (Fig. 2(b) and (c)). The data shows that most of the Ji-scores for GPCR class B (Secretin) and GPCR class F (Frizzled) family proteins are closer to those of the GPCR A family proteins compared to the other two protein families. This suggests that the two family proteins have a similar conformation to the GPCR A family proteins in the local conformation. Most of the Ji-scores of the four family proteins were less than 4, indicating that local conformations of the proteins are in the regions of “GPCR A family like” or “GPCR A family-near”. These results are somewhat consistent with the analytical results of the global conformation study, but some distinct features could be detected in this local conformation study as follows. Obviously, the Ji-scores of λ1 and λ3 for the Microbial and Algal rhodopsin family proteins were higher than those of the GPCR A family proteins. In addition, they were mostly in the region of “GPCR A family-far”. For the GPCR Class C (Glutamate) family proteins, their Ji-scores for Ω2, λ4, and λ5 were higher than the respective Ji-scores of the GPCR A family proteins, and they were in the region of “GPCR A family-near”. These results denote the local dihedral angles that contribute to the global conformational distances between the two families and the GPCR A family. On the other hand, the Ji-scores of the four family proteins for the Ω4, Ω6, and λ2 were all in the range of scores for the GPCR A family, i.e. “GPCR A family-like” region. This suggests that the proteins in the rhodopsin-like superfamily maintain a well-conserved conformation in those dihedral angles.

Figure 2
Figure 2

J-scores of the rhodopsin-like superfamily proteins in comparison with GPCR A family. Family names are presented in x-axis and their J-scores are presented in y-axis. (a) Jtot-scores (b) Ji-scores for individual omega (Ω1–6) angles, and (c) Ji-scores for individual lambda (λ1–5) angles.

The conformational distances of the proteins in different 7TM superfamilies from the GPCR A family were quantified. In 7TM fold, there are 13 different superfamilies. The available non-redundant proteins in the superfamilies were selected from PDB, analyzed by the joint-based descriptor, and their J-scores were estimated (SI Tables 5 and 6). Figure 3(a) shows the Jtot-scores for the GPCR A family proteins and proteins in the different superfamilies in the 7TM fold. The J-scores of all the superfamilies were higher than the scores for the GPCR A family proteins. No proteins were observed in the region of “GPCR A family-like”. Only the adiponectin superfamily proteins showed the Jtot–score of “GPCR-A family-near” region. The Jtot–scores for other superfamily proteins were observed in the region of “GPCR-A family-far”. In particular, the methane monooxygenase superfamily proteins showed the highest Jtot-score. These results suggest that the proteins in the different superfamilies do not share the conformation with the GPCR A family globally at the joint-based coordinate level. The Ji-scores for individual dihedral angles were also measured and compared (Fig. 3(b) and (c)). In this local conformational level, some superfamilies share a local conformation with the GPCR A family proteins. For example, the Adiponectin, Bacterial Cytochrome C oxidase, Sweet transporters, Glutamate Ion Channel, Protein Yet J superfamilies showed Ji-scores for Ω5 in the range of the GPCR A family proteins. Interestingly, the J i -scores for Ω4 were lower than 4 and in the regions of “GPCR A family-like” or “GPCR A family-near” for most proteins except a few proteins in the Cation Channel superfamily. This suggests that the Ω4 dihedral angle is relatively well-conserved compared to other dihedral angles in the 7TM proteins. The Ω6 angle is the second well-conserved dihedral angle in 7TM proteins with a low Ji-score among entire superfamilies.

Figure 3
Figure 3

J-scores of the superfamilies in 7TM fold in comparison with GPCR A family. Superfamily names are presented in x-axis and their J-scores are presented in y-axis. (a) Jtot-scores (b) Ji-scores for individual omega (Ω1–6) angles, and (c) Ji-scores for individual lambda (λ1–5) angles.

Overall, the conformational distance of the GPCR A family and other 7TM proteins were measured based on the joint-based descriptor. The analysis allowed the distant and close families or superfamilies to the GPCR A family to be distinguished at a global conformation level. In addition, the conserved and diverse dihedral angles of the joint points in rhodopsin-like superfamily and in 7TM fold proteins could be identified. The above analyses showed the analytical results based on the Ω and λ angles. As reported previously38, the dihedral angles are related directly to the arrangement and extension of helices in the membrane. These results are interpreted in terms of the helical arrangement and extension pattern in the Discussion section.

Conformational validation of computational models for human GPCRs

Many TM protein structures still remain unexplored because of the difficulty in their crystallization. Therefore, computational structural modeling is believed to be an alternative tool to identify the unknown structures50,51,52,53. In particular, a number of approaches to model the GPCR structures from sequences were developed due to the biological importance and profound effect of GPCR proteins in drug discovery and translational medicine. One of the most efficient modeling methods for GPCR is the GPCR I-TASSER method54, which is a hybrid method combining threading, ab initio folding and experimental data for the 3D structure of GPCR proteins. The protocol was used to construct a GPCR HGmod database, including the 3D structural models of almost 1000 of human GPCR candidates54. In this study, a set of the computational models in the database was analyzed by the joint-based descriptor, and their J-scores were measured to validate the quality of the models based on the conformational features of the known 27 GPCR protein A family proteins.

From the GPCR HGmod database54, 20 computational models were selected randomly, and their J-scores considering a total of 11 dihedral angles and individual angles were calculated. SI Tables 7 and 8 list the analyzed dihedral angles and J-scores of the 20 models, respectively. The J-scores were compared with those of the 27 GPCR A family proteins (Fig. 4). Among the 20 computational models, 6 models (Opsin receptor, Opsin 1 receptor, Thromboxane receptor, Taste receptor type 2, 5-hydroxytryptamine receptor 6 and Alpha-1A adrenergic receptor) showed Jtot-scores in the region of the “GPCR A family-like” conformation, and the other 14 models showed Jtot-scores corresponding to the “GPCR A family-near” conformation (Fig. 4(a)). An analysis of the Ji-scores for individual dihedral angles (Fig. 4(b) and (c)) showed that most of the scores were also in the range of “GPCR A family-like” or “GPCR A family-near”. These results indicate that the 20 computational models have a relatively close distance to the global and local conformations of the GPCR A proteins. Presumably, the conformations of the modeled structures mostly resemble the native GPCR structures because the experimental restraints were used in the computational modeling of the structures54,55. On the other hand, some Ji-scores of 7 computational models were found in the range of “GPCR A family-far” (Ω1 of Olfactory receptor 5AC1, Ω2 and λ1 of Gastric inhibitory polypeptide receptor, Ω2, Ω3, and Ω5 of Neuropeptide FF receptor 2, Ω2 of Neuromedin-K receptor, Ω2 of Olfactory receptor, λ1 of Glucagon-like peptide 2 receptor, and λ4 of GPCR 2 Secretin-like receptor). This indicates that the local conformations related to the dihedral angles in the modeled structures somewhat deviate from the native 27 GPCR structures. To check whether the templates used in the GPCR I-TASSER modeling are related to these local deviations, 24 templates of GPCR structures used in the modeling were validated by estimating their J-scores against our 27 GPCR dataset. It was observed that all of the 24 templates showed J-tot and Ji-scores of “family-like” or “family-near” range, and there were no templates showing J-scores of “family-far” (data not shown). Therefore, at least the 24 templates used in the GPCR I-TASSER modeling might not induce the Ji-scores of “family-far” in the 7 models. It is presumed that the local deviations of the models are induced in the next modeling steps such as threading, ab initio modeling, and energy minimization.

Figure 4
Figure 4

J-scores of the computational models of GPCR proteins obtained from the HGmod database in comparison with GPCR A family. 20 HGmod GPCR Model IDs are in x-axis, and their J-scores are in y-axis. (a) Jtot-scores (b) Ji-scores for individual omega (Ω1–6) angles, and (c) Ji-scores for individual lambda (λ1–5) angles.

In summary, how much the computational models reproduce the native conformation of GPCR A proteins could be estimated at global and local conformational level. None of the validated 20 models deviated significantly from the native GPCR protein in terms of the global conformation, but some models showed locally different conformations. The deviated local angles in some models can be interpreted in two ways. One is that the computational models are correct and their real structures have the dihedral angles with a deviation from those of 27 native structures. The other is that the modeling of the local structure may not be correct. Of course, this may not be confirmed before their structures are experimentally identified.

Measurement of conformational difference between active GPCR and inactive GPCR

In general, the activation of GPCR proteins is triggered by the binding of diverse ligands. The binding induces conformational changes in the GPCR proteins specific to the receptor types, which in turn activates the associated G protein. This eventually leads to modulation of various intercellular signaling pathways and changes in the downstream canonical cellular biochemistry. Understanding the conformational changes in the GPCR proteins from an inactive state to active state is crucial in receptor-ligand interactions and the subsequent signal pathways. Many studies have been performed at the molecular level, which provided useful information on the changes in the TM helical interactions in the activation20,33,56,57. In this study, an attempt was made to measure the global and local conformational distance of activated and inactivated GPCR proteins by comparing their J-scores to understand their conformational change at the macroscopic joint-based dihedral level.

To study the conformational distance between inactive states and active states, the dataset for active states were constructed by selecting 10 non-redundant active-like structures (4UHRA: Adenosine receptor A2a, 3SN6R: β2 adrenergic receptor, 5GLHA: Endothelin B receptor, 4MQSA: Muscarinic acetylcholine receptor, 4GRVA: Neurotensin receptor type 1, 4PXFA: Opsin receptor, 4X1H: Bovine rhodopsin, 4XT1A: Viral GPCR, 5C1MA: Opioid mu receptor, and 4IB4A: 5-hydroxytryptamine receptor) from all the available active-like state structures of Class A GPCR in PDB. Dihedral angles of the active-like conformations were calculated and tabulated in SI Table 9. First, the J-scores of the 10 active states were estimated by using the scoring function devised on the basis of the initial 27 inactive dataset as a reference (SI Table 1), which indicates the distance of each active state from the average of inactive conformation. As a control, J-scores of the 10 active states were also calculated using the scoring function devised on the basis of the 10 active states as a reference dataset, which indicates the distance of each active state from the average of active conformation. As shown in Fig. 5(a), the 10 active structures against inactive reference set showed slightly but clearly higher values than the control in the Ji-score for λ5, whereas their other J-scores were almost similar to those of the control. Second, the analyses were replicated with the 27 inactive GPCR proteins against the active reference and inactive reference, leading to the almost same pattern (Fig. 5(b)). These results imply that there is a marginal but a clear conformational difference between the active and inactive states, related to the local λ5 dihedral angle of the joint-based coordinate.

Figure 5
Figure 5

J-scores of active states or inactive states against active reference and inactive reference. (a) J-scores of 10 active structures against the inactive reference (red) and the active reference (green), and (b) J-scores of 27 inactive structures against the inactive reference (red) and the active reference (green).

Overall, the joint-based macroscopic descriptor with the J-score measurement could be used to detect the conformational distance between the active and inactive state structures of GPCR at the macroscopic level. The most distant dihedral angle between the two states was λ5. From this finding, activation of the GPCR by ligand-binding is believed to cause the local conformational change, particularly related to the λ5 dihedral angle. In the Discussion section, an attempt is made to interpret the conformational change in GPCR by relating the λ5 dihedral angle variation to the TM helical arrangement and extension pattern in the GPCR protein.

Discussion

The joint-based descriptor was applied to quantify the conformational distance of the 7TM proteins from the GPCR A family, to examine the conformational difference between the active and inactive states of GPCR, and to validate the GPCR computational models. A prominent feature of the approach is to measure the structural distance at the macroscopic level, which permits an analysis of the conformational difference of complex proteins, such as TM proteins, in a more simplistic way. This study focused on GPCR proteins and their related structures, but the approach can also explore the geometrical similarities and diversities that are particular to any TM topology. The structural features, evolutionary relationships, computational models, and conformational changes of TM proteins can be studied in a more effective way if the joint-based approach is combined with the microscopic approaches that are popularly utilized for measuring the structural difference.

In general, the more the protein structural descriptor is macroscopic, the more the local microscopic information about protein structure is lost. The joint-based descriptor is a macroscopic one that employs only the dihedral angles of joints of secondary structures as a coordinate, and therefore it cannot detect many important local structural features of TM structures such as helical bending or kinks, interhelical contacts, loop variations, and the tilt of the first and the last helices. These local features can be efficiently captured through more microscopic approach such as RMSD of C-alpha atoms. Therefore, it should be noted that there may be no direct correlation between -based RMSD and the joint-based distance. Despite the limitation of joint-based approach in the detection of microscopic structural features, the use of the joint-based approach might be meaningful in the aspect that protein topology can be studied in a new viewpoint, using the dihedral angles of the joints of secondary structures as structural coordinate, which was not previously explored. It is expected that the joint-based approach can be a tool to study protein structures together with existing approaches.

As reported in our previous study38, dihedral angles between the joints can be roughly related to the arrangement and extension patterns of the TM helices in the membrane proteins at the macroscopic level. Briefly, the bending and kinked angles of most TM helices are known to be comparatively low (less than 20 degrees)58, and the TM helices are assumed to be straight lines of the joint points, as shown in SI Fig. 1. The Ωi dihedral angle represents how the i+1th TM helix (Hi+1) is arranged or tilted against the ith TM helix (Hi). The λi dihedral angle provides information on how the TM helices Hi, Hi+1, and Hi+2 are extended or packed. Most helices in TM proteins are relatively parallel and therefore the relative position of the four joint points for λi can be roughly related to the extension of the three continuous helices. Then, the local distances measured in the conformational study of the GPCR A family proteins and the other 7TM proteins can be related to their helical arrangement/extension patterns. For example, the proteins in the microbial and algal rhodopsin family showed much higher Ji-scores for λ1 and λ3 than the other dihedral angles. This suggests that the family has a very different conformation from the GPCR A family in the extension patterns of H1, H2, & H3 and H3, H4, & H6. Another example is that the Ω4 dihedral angle is a relatively conserved dihedral angle in the entire 7TM proteins analyzed, which suggests that the 7TM proteins have a relatively similar local conformation in terms of the helical arrangement of H5 against H4, compared to the other helical arrangement and extension patterns.

In the study on the validation of computational models, we attempted to check how much the computational models of GPCR proteins that were already validated in many aspects are close to the native GPCRs only at the level of the joint-based coordinate. However, it should be noted that the joint-based validation alone cannot be used to validate the computational models properly in the validation of raw computational models, because, as mentioned above, the joint-based descriptor cannot detect many important local structural features of TM structures. It should be used together with other microscopic validation tools which can detect other structural features such as interhelical contacts of TM proteins. The joint-based approach is expected to be an additional tool that can validate the conformational topology of computational models.

In the study on the conformational distance between the inactive and active GPCR proteins, λ5 was identified as the major dihedral angle that was most commonly and prominently changed. Based on the relationship between the dihedral angle type and the arrangement or extension pattern of the TM helices, the change in the λ5 dihedral angle in the GPCR conformation shows that there is a conformational change in the extension pattern in H5, H6, and H7. This conformational change is consistent with previous reports showing that the cytoplasmic ends of H6 and H7 in GPCR regularly incline to be tilted from the helix bundle during the receptor-ligand interactions33,59,60,61,62,63. To better understand the conformational change related to GPCR activation, the λ5 dihedral angles of the active and inactive states were compared directly, and their geometrical relationship with the extension pattern of H5, H6, and H7 was analyzed further. The λ5 angles of the inactive and active states of the ten pairs were identified to be in the range of −116° to −173°, and +133° to +177°, respectively. These values suggest that the conformational change by activation is consistent and somewhat symmetrical. λ5 is defined based on the four joint points (P10, P11, P12, and P13) in H5, H6, and H7 of Fig. 1. Therefore, this study examined whether there is real symmetry and what causes the symmetrical conformational change by comparing the arrangement of joint points in H5, H6, and H7 in the PDB structures. The cytoplasmic end of TM6 was bent slightly toward the TM7 by activation, leading to symmetrical variations of the helical extension pattern. Figure 6 presents an example of the identified symmetrical difference in the active-inactive pairs at the joint coordinate level.

Figure 6
Figure 6

Comparison of conformational difference between inactive (red) and active-like (green) GPCR_A structures. (a) Side view of the linearly ordered TMH5, TMH6, and TMH7 helices of GPCR structures. P10a and P10b are the joint points belong to the cytoplasmic ends of TM5 of inactive and active structures, respectively. TMH6 has P11a and P11b; P12a and P12b and P13a and P13b belong to extracellular ends of TMH7. (b) Top view of the arrangement of three consecutive helices TMH5-TMH6-TMH7. GPCR activation causes the macroscopic transition at the cytoplasmic end of TMH6 towards TMH7 and induces the rearrangement of P11a to P11b, which leads to the change of λ5. The figures present the example of inactive [2RH1]a and active [3SN6]b pairs.

Methods

Datasets used in the study

All the proteins analyzed in this study were selected from PDB and high-resolution (<3.5 Å) structures. The dataset of 27 representative GPCR-A family proteins were achieved as follows. First, all the x-ray crystal structures belonging to GPCR_A family, and 155 monomeric chain structures were found. Subsequently, 27 chains of inactive states were filtered as a non-redundant dataset by selecting all the available different subfamily receptor types. The dataset for 10 active states was prepared by selecting non-redundant proteins showing different subfamily receptors from 32 structures annotated as active-like conformations in PDB. To obtain proteins that represent four different families in the rhodopsin-like superfamily, the proteins in microbial and algal rhodopsin, Class B (Secretin), Class C (Glutamate), and Class F (Frizzled), were collected and the non-redundant sequences were extracted. The proteins for 12 superfamilies (Bacterial Cytochrome C Oxidase, Methane Monooxygenase, Maltose Transporters, Zinc Metalloprotease, Human γ secretase, Glutamate Ion Channel, Protein YetJ, Metal Transport, Prolipoprotein Diacylglyceryl transferase, Sweet Transporter and Cation Channel proteins) were also selected based on their sequence redundancy. A dataset of 20 computational models was isolated from the GPCR-HGmod database, which is the library of human GPCR-predicted models generated through GPCR I-TASSER54. Approximately 1000 GPCR models are publicly available to download from http://zhanglab.ccmb.med.umich.edu/GPCR-HGmod/ and are assigned by unique HG ID and UniProt ID. There are 1 to 5 models for each entry, which are assisted by the TM-score and RMSD values. They have also been assigned a confidence score for each top model, which ranges between the values −5 to 2; a higher score indicates the quality of the model. Ten high TM-score models [P08100, Q0PJU0, Q14332, P48146, Q6IEZ2, P21731, P58182, P59536, P0C628, and Q5CZ62], and 10 low TM-score models [B9EIL6, P21453, P48546, Q9Y5X5, P29371, P50406, O95838, P35348, Q6ZMH4, and P28223] were selected randomly.


Joint-based representation and Ω/λ dihedral measurements

The beginning and ending residue Cα atoms of the TM segment were projected as a joint coordinate for the dihedral calculation, as described elaborately in a previous report38. Selection of the structural joints was scrutinized visually for the Cα XYZ coordinates from the corresponding PDB file. OPM was referred to define the helix boundary and TM segments for the crystal structures64. In addition, for all the selected sequences and predicted models, their TM boundaries were defined by the membrane topology prediction tool called the TOPCONS suite65. While establishing a connection of the joint residues, a new description of the overall protein structure was portrayed. The developed program parses the query structures and the Cα XYZ coordinates preselected from each joint were exploited for the dihedral measurements, as described previously. The resulting number of dihedral angles for each protein is directly proportional to the number of helices and loops present in them. The compiled data set was used for the dihedral angle measurements by the joint based approach and used for the structural diversity assessments.

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References

  1. 1.

    Sigalov, A. B. New therapeutic strategies targeting transmembrane signal transduction in the immune system. Cell Adhes Migr 4, 255–267, https://doi.org/10.4161/cam.4.2.10746 (2010).

  2. 2.

    Waszkielewicz, A. M. et al. Ion Channels as Drug Targets in Central Nervous System Disorders. Current Medicinal Chemistry 20, 1241–1285 (2013).

  3. 3.

    Bagal, S. et al. Ion Channels as Therapeutic Targets: A Drug Discovery Perspective. J Med Chem 56, 593–624, https://doi.org/10.1021/jm3011433 (2013).

  4. 4.

    Winther, T., Xu, L., Berg-Sorensen, K., Brown, S. & Oddershede, L. B. Effect of Energy Metabolism on Protein Motility in the Bacterial Outer Membrane. Biophys J 97, 1305–1312, https://doi.org/10.1016/j.bpj.2009.06.027 (2009).

  5. 5.

    Osornio, Y. M., Manni, L. S., Aleandri, S. & Landau, E. Designed Functional Lipidic Biomaterials: Applications in Molecular Recognition, Drug Delivery and Membrane Protein Crystallization. Biophys J 110, 41a–41a (2016).

  6. 6.

    Almen, M. S., Nordstrom, K. J., Fredriksson, R. & Schioth, H. B. Mapping the human membrane proteome: a majority of the human membrane proteins can be classified according to function and evolutionary origin. BMC Biol 7, 50, https://doi.org/10.1186/1741-7007-7-50 (2009).

  7. 7.

    Haltia, T. & Freire, E. Forces and Factors That Contribute to the Structural Stability of Membrane-Proteins. Bba-Bioenergetics 1228, 1–27, https://doi.org/10.1016/0005-2728(94)00161-W (1995).

  8. 8.

    Popot, J. L. & Engelman, D. M. Membrane-Protein Folding and Oligomerization - the 2-Stage Model. Biochemistry-Us 29, 4031–4037, https://doi.org/10.1021/bi00469a001 (1990).

  9. 9.

    Jayasinghe, S. Energetics, stability, and prediction of transmembrane helices. Biophys J 82, 529a–529a (2002).

  10. 10.

    Tusnady, G. E. & Simon, I. Topology of membrane proteins. J Chem Inf Comput Sci 41, 364–368 (2001).

  11. 11.

    von Heijne, G. Membrane-protein topology. Nat Rev Mol Cell Bio 7, 909–918, https://doi.org/10.1038/nrm2063 (2006).

  12. 12.

    Rapp, M., Granseth, E., Seppala, S. & von Heijne, G. Identification and evolution of dual-topology membrane proteins. Nat Struct Mol Biol 13, 112–116, https://doi.org/10.1038/nsmb1057 (2006).

  13. 13.

    Lee, S., Lee, B., Jang, I., Kim, S. & Bhak, J. Localizome: a server for identifying transmembrane topologies and TM helices of eukaryotic proteins utilizing domain information. Nucleic Acids Res 34, W99–W103, https://doi.org/10.1093/nar/gkl351 (2006).

  14. 14.

    Tusnady, G. E., Sarkadi, B., Simon, I. & Varadi, A. Membrane topology of human ABC proteins. FEBS Lett 580, 1017–1022, https://doi.org/10.1016/j.febslet.2005.11.040 (2006).

  15. 15.

    Engelman, D. M. et al. Helix interactions in membrane protein folding and oligomerization. Biophys J 70, Tuam4–Tuam4 (1996).

  16. 16.

    Adamian, L. A. & Liang, J. Ca2+ transporting activity of Ca2+-ATPase is correlated with packing interactions of TM helices. Biophys J 82, 525a–525a (2002).

  17. 17.

    Adamian, L. & Liang, J. Helix-helix packing and interfacial pairwise interactions of residues in membrane proteins. J Mol Biol 311, 891–907, https://doi.org/10.1006/jmbi.2001.4908 (2001).

  18. 18.

    Pabuwal, V. & Li, Z. Network pattern of residue packing in helical membrane proteins and its application in membrane protein structure prediction. Protein Eng Des Sel 21, 55–64, https://doi.org/10.1093/protein/gzm059 (2008).

  19. 19.

    Fuchs, A. & Frishman, D. Structural comparison and classification of alpha-helical transmembrane domains based on helix interaction patterns. Proteins 78, 2587–2599, https://doi.org/10.1002/prot.22768 (2010).

  20. 20.

    Yang, J., Jang, R., Zhang, Y. & Shen, H. B. High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling. Bioinformatics 29, 2579–2587, https://doi.org/10.1093/bioinformatics/btt440 (2013).

  21. 21.

    Anantharaman, V. & Aravind, L. Application of comparative genomics in the identification and analysis of novel families of membrane-associated receptors in bacteria. BMC Genomics 4, 34, https://doi.org/10.1186/1471-2164-4-34 (2003).

  22. 22.

    Lee, S. et al. Two mode ion channels induced by interaction of acidic amphipathic alpha-helical peptides with lipid bilayers. Biochim Biophys Acta 1191, 181–189 (1994).

  23. 23.

    Imamoto, Y., Kojima, K., Oka, T., Maeda, R. & Shichida, Y. Helical rearrangement of photoactivated rhodopsin in monomeric and dimeric forms probed by high-angle X-ray scattering. Photochem Photobiol Sci 14, 1965–1973, https://doi.org/10.1039/c5pp00175g (2015).

  24. 24.

    Langosch, D. & Arkin, I. T. Interaction and conformational dynamics of membrane-spanning protein helices. Protein Sci 18, 1343–1358, https://doi.org/10.1002/pro.154 (2009).

  25. 25.

    Arnold Emerson, I. & Gothandam, K. M. Residue centrality in alpha helical polytopic transmembrane protein structures. J Theor Biol 309, 78–87, https://doi.org/10.1016/j.jtbi.2012.06.002 (2012).

  26. 26.

    Xu, J. & Zhang, Y. How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 26, 889–895, https://doi.org/10.1093/bioinformatics/btq066 (2010).

  27. 27.

    Zhang, Y. & Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 33, 2302–2309, https://doi.org/10.1093/nar/gki524 (2005).

  28. 28.

    Palopoli, N., Monzon, A. M., Parisi, G. & Fornasari, M. S. Addressing the Role of Conformational Diversity in Protein Structure Prediction. PLoS One 11, e0154923, https://doi.org/10.1371/journal.pone.0154923 (2016).

  29. 29.

    Vogel, H. et al. Structural fluctuations between two conformational states of a transmembrane helical peptide are related to its channel-forming properties in planar lipid membranes. Eur J Biochem 212, 305–313 (1993).

  30. 30.

    Barth, P., Wallner, B. & Baker, D. Prediction of membrane protein structures with complex topologies using limited constraints. Proc Natl Acad Sci USA 106, 1409–1414, https://doi.org/10.1073/pnas.0808323106 (2009).

  31. 31.

    Stamm, M. & Forrest, L. R. Structure alignment of membrane proteins: Accuracy of available tools and a consensus strategy. Proteins 84, 1333, https://doi.org/10.1002/prot.25098 (2016).

  32. 32.

    Jamroz, M., Kolinski, A. & Kihara, D. Ensemble-based evaluation for protein structure models. Bioinformatics 32, i314–i321, https://doi.org/10.1093/bioinformatics/btw262 (2016).

  33. 33.

    Dalton, J. A., Lans, I. & Giraldo, J. Quantifying conformational changes in GPCRs: glimpse of a common functional mechanism. BMC Bioinformatics 16, 124, https://doi.org/10.1186/s12859-015-0567-3 (2015).

  34. 34.

    Fogolari, F. et al. Scoring predictive models using a reduced representation of proteins: model and energy definition. Bmc Struct Biol 7, 15, https://doi.org/10.1186/1472-6807-7-15 (2007).

  35. 35.

    Kolinski, A. Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 51, 349–371, doi:035001349 (2004).

  36. 36.

    Bagos, P. G., Liakopoulos, T. D. & Hamodrakas, S. J. Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method. BMC Bioinformatics 6, 7, https://doi.org/10.1186/1471-2105-6-7 (2005).

  37. 37.

    Donate-Macian, P., Bano-Polo, M., Vazquez-Ibar, J. L., Mingarro, I. & Peralvarez-Marin, A. Molecular and topological membrane folding determinants of transient receptor potential vanilloid 2 channel. Biochem Biophys Res Commun 462, 221–226, https://doi.org/10.1016/j.bbrc.2015.04.120 (2015).

  38. 38.

    Thangappan, J., Wu, S. & Lee, S. G. Joint-based description of protein structure: its application to the geometric characterization of membrane proteins. Sci Rep 7, 1056, https://doi.org/10.1038/s41598-017-01011-z (2017).

  39. 39.

    Lagerstrom, M. C. & Schioth, H. B. Structural diversity of G protein-coupled receptors and significance for drug discovery. Nat Rev Drug Discov 7, 339–357, https://doi.org/10.1038/nrd2518 (2008).

  40. 40.

    Venkatakrishnan, A. J. et al. Molecular signatures of G-protein-coupled receptors. Nature 494, 185–194, https://doi.org/10.1038/nature11896 (2013).

  41. 41.

    Zhang, L. & Skolnick, J. What should the Z-score of native protein structures be? Protein Sci 7, 1201–1207, https://doi.org/10.1002/pro.5560070515 (1998).

  42. 42.

    Vriend, G. WHAT IF: a molecular modeling and drug design program. J Mol Graph 8, 52–56, 29 (1990).

  43. 43.

    Sippl, M. J. Recognition of errors in three-dimensional structures of proteins. Proteins 17, 355–362, https://doi.org/10.1002/prot.340170404 (1993).

  44. 44.

    Hooft, R. W., Vriend, G., Sander, C. & Abola, E. E. Errors in protein structures. Nature 381, 272, https://doi.org/10.1038/381272a0 (1996).

  45. 45.

    Scheeff, E. D. & Bourne, P. E. Structural evolution of the protein kinase-like superfamily. PLoS Comput Biol 1, e49, https://doi.org/10.1371/journal.pcbi.0010049 (2005).

  46. 46.

    Chung, R. & Yona, G. Protein family comparison using statistical models and predicted structural information. BMC Bioinformatics 5, 183, https://doi.org/10.1186/1471-2105-5-183 (2004).

  47. 47.

    Muller, A., MacCallum, R. M. & Sternberg, M. J. Structural characterization of the human proteome. Genome Res 12, 1625–1641, https://doi.org/10.1101/gr.221202 (2002).

  48. 48.

    Kinoshita, M. & Okada, T. Structural conservation among the rhodopsin-like and other G protein-coupled receptors. Sci Rep 5, 9176, https://doi.org/10.1038/srep09176 (2015).

  49. 49.

    Neumann, S., Fuchs, A., Mulkidjanian, A. & Frishman, D. Current status of membrane protein structure classification. Proteins 78, 1760–1773, https://doi.org/10.1002/prot.22692 (2010).

  50. 50.

    Szilagyi, A. & Zhang, Y. Template-based structure modeling of protein-protein interactions. Curr Opin Struct Biol 24, 10–23, https://doi.org/10.1016/j.sbi.2013.11.005 (2014).

  51. 51.

    Xu, D. & Zhang, Y. Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins 80, 1715–1735, https://doi.org/10.1002/prot.24065 (2012).

  52. 52.

    Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10, 845–858, https://doi.org/10.1038/nprot.2015.053 (2015).

  53. 53.

    Kim, D. E., Chivian, D. & Baker, D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 32, W526–531, https://doi.org/10.1093/nar/gkh468 (2004).

  54. 54.

    Zhang, J., Yang, J., Jang, R. & Zhang, Y. GPCR-I-TASSER: A Hybrid Approach to G Protein-Coupled Receptor Structure Modeling and the Application to the Human Genome. Structure 23, 1538–1549, https://doi.org/10.1016/j.str.2015.06.007 (2015).

  55. 55.

    Fanelli, F. & De Benedetti, P. G. Computational modeling approaches to structure-function analysis of G protein-coupled receptors. Chem Rev 105, 3297–3351, https://doi.org/10.1021/cr000095n (2005).

  56. 56.

    Sanematsu, K., Yoshida, R., Shigemura, N. & Ninomiya, Y. Structure, function, and signaling of taste G-protein-coupled receptors. Curr Pharm Biotechnol 15, 951–961 (2014).

  57. 57.

    Salon, J. A., Lodowski, D. T. & Palczewski, K. The significance of G protein-coupled receptor crystallography for drug discovery. Pharmacol Rev 63, 901–937, https://doi.org/10.1124/pr.110.003350 (2011).

  58. 58.

    Mai, T. L. & Chen, C. M. Computational prediction of kink properties of helices in membrane proteins. J Comput Aided Mol Des 28, 99–109, https://doi.org/10.1007/s10822-014-9734-2 (2014).

  59. 59.

    Ghanouni, P., Steenhuis, J. J., Farrens, D. L. & Kobilka, B. K. Agonist-induced conformational changes in the G-protein-coupling domain of the beta 2 adrenergic receptor. Proc Natl Acad Sci USA 98, 5997–6002, https://doi.org/10.1073/pnas.101126198 (2001).

  60. 60.

    Bhattacharya, S., Hall, S. E. & Vaidehi, N. Agonist-induced conformational changes in bovine rhodopsin: insight into activation of G-protein-coupled receptors. J Mol Biol 382, 539–555, https://doi.org/10.1016/j.jmb.2008.06.084 (2008).

  61. 61.

    Umanah, G. K., Huang, L. Y., Maccarone, J. M., Naider, F. & Becker, J. M. Changes in conformation at the cytoplasmic ends of the fifth and sixth transmembrane helices of a yeast G protein-coupled receptor in response to ligand binding. Biochemistry-Us 50, 6841–6854, https://doi.org/10.1021/bi200254h (2011).

  62. 62.

    Rubenstein, L. A., Zauhar, R. J. & Lanzara, R. G. Molecular dynamics of a biophysical model for beta2-adrenergic and G protein-coupled receptor activation. J Mol Graph Model 25, 396–409, https://doi.org/10.1016/j.jmgm.2006.02.008 (2006).

  63. 63.

    Trzaskowski, B. et al. Action of molecular switches in GPCRs–theoretical and experimental studies. Curr Med Chem 19, 1090–1109 (2012).

  64. 64.

    Lomize, M. A., Lomize, A. L., Pogozheva, I. D. & Mosberg, H. I. OPM: Orientations of proteins in membranes database. Bioinformatics 22, 623–625, https://doi.org/10.1093/bioinformatics/btk023 (2006).

  65. 65.

    Tsirigos, K. D., Peters, C., Shu, N., Kall, L. & Elofsson, A. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic Acids Res 43, W401–407, https://doi.org/10.1093/nar/gkv485 (2015).

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01056766 & 2015R1D1A1A01061125).

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Affiliations

  1. Department of Chemical Engineering, Pusan National University, Busan, 609-735, Republic of Korea

    • Jayaraman Thangappan
    • , Bharat Madan
    •  & Sun-Gu Lee
  2. Department of Physics, Pukyong National University, Busan, 608-737, Republic of Korea

    • Sangwook Wu

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Contributions

J.T., B.M., and S.G.L. designed research; J.T. performed research; J.T., B.M., S.W., and S.G.L. analyzed data; J.T., B.M., S.W., and S.G.L. wrote the paper. All authors reviewed the final manuscript.

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The authors declare that they have no competing interests.

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Correspondence to Sangwook Wu or Sun-Gu Lee.

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