The Scientific World Journal

The Scientific World Journal / 2012 / Article

Research Article | Open Access

Volume 2012 |Article ID 691579 |

Yoko Ishino, Takanori Harada, "Novel Computational Methodologies for Structural Modeling of Spacious Ligand Binding Sites of G-Protein-Coupled Receptors: Development and Application to Human Leukotriene B4 Receptor", The Scientific World Journal, vol. 2012, Article ID 691579, 11 pages, 2012.

Novel Computational Methodologies for Structural Modeling of Spacious Ligand Binding Sites of G-Protein-Coupled Receptors: Development and Application to Human Leukotriene B4 Receptor

Academic Editor: M. Liu
Received12 Oct 2012
Accepted30 Oct 2012
Published10 Dec 2012


This paper describes a novel method to predict the activated structures of G-protein-coupled receptors (GPCRs) with high accuracy, while aiming for the use of the predicted 3D structures in in silico virtual screening in the future. We propose a new method for modeling GPCR thermal fluctuations, where conformation changes of the proteins are modeled by combining fluctuations on multiple time scales. The core idea of the method is that a molecular dynamics simulation is used to calculate average 3D coordinates of all atoms of a GPCR protein against heat fluctuation on the picosecond or nanosecond time scale, and then evolutionary computation including receptor-ligand docking simulations functions to determine the rotation angle of each helix of a GPCR protein as a movement on a longer time scale. The method was validated using human leukotriene B4 receptor BLT1 as a sample GPCR. Our study demonstrated that the proposed method was able to derive the appropriate 3D structure of the active-state GPCR which docks with its agonists.

1. Introduction

G-protein-coupled receptors (GPCRs), the largest family of membrane proteins (around 800 in humans), are involved in a variety of biological and pathological processes such as development and proliferation [1], neurological disorders [2], angiogenesis [3], and metabolic disorders [4]. GPCRs transmit molecular information from the extracellular side to the intracellular side of the cell, thus mediating many intracellular responses. Although nearly one half of currently marketed drugs target GPCRs [5], only 10% of these receptors have endogenous ligands [6]. Structurally, all GPCRs share an architecture formed by seven transmembrane (TM) helices connected by extracellular and intracellular loops. GPCRs can recognize structurally diverse ligands ranging from photons to ions, amino acids, small organic molecules, lipids, peptides, or proteins [7].

Due to limitations in the purification and crystallization of GPCRs, until recently only a limited number of three-dimensional (3D) structures of GPCRs have been resolved at high resolution by X-ray crystallography: rhodopsin [8], the β-adrenergic receptors (β2AR [9, 10], β1AR [11]), the adenosine receptor [12], and the opioid receptors (κ [13], μ [14]). The highly conserved general architecture including the TM helical bundle and some universally conserved residues allows predicting the 3D structure of unknown GPCRs by homology modeling, while using a few known GPCR structures as a template [15]. However, great cautions are needed when utilizing the homology-based models for detailed GPCR structural and functional annotations since helix kinks are often different in different receptors. Modeling these subtle distinctions, which is essential for ligand docking and screening, remains a major challenge [16]. In addition, since almost all known GPCR structures have been found in their inactive forms, the homology modeling has shown little power to practically predict the 3D structure of GPCRs in the active forms. It is known that the agonist-bound GPCRs take the active forms, whereas the antagonist-bound or inverse agonist-bound GPCRs take the inactive forms.

Ligand-induced activation of GPCRs results in multiple allosteric conformational changes that propagate throughout the receptor structure, ultimately triggering different signaling cascades [17]. In contrast to inactive states of GPCRs, no crystal structures of active states were available until 2011. Given the absence of experimental structural information, several investigators have applied computational strategies to predict activated models of GPCRs [1822]. Therein, it is reported that applying an appropriate long-term molecular dynamics (MD) simulation after homology modeling could predict plausible 3D structures of activated GPCRs in view of the induced fit mechanism [21, 22]. However, this approach needs to impose appropriate structural constraints prior to the MD simulation, which requires both the skills and experience of an MD specialist. Moreover, MD simulations on ordinary personal computers are generally limited to timescales up to the microsecond order in long-term studies, yet structural changes from fitting-induced fluctuations require calculations on the millisecond order. Due to its time-consuming nature, it is virtually impossible to simulate all processes involved in structural changes using only MD on common computers. For this reason, currently no high-accuracy methods are available for predicting the activated structure of GPCRs.

This paper describes a novel method to predict activated GPCR structures with high accuracy on standard workstations, while aiming for the use of the predicted 3D structures in in silico virtual screening in the future. First, we have developed a new method for modeling thermal fluctuations in GPCRs by using combinations of fluctuations with different time scales. Based on this model, we propose a novel computational method to search for one of the best 3D structures of an activated GPCR.

2. Model and Framework

Proteins undergo thermal fluctuations of considerable magnitude. The time scale of thermal fluctuations in proteins ranges from femtoseconds to minutes, or even longer timescales [23]. In terms of spatial considerations, various levels of fluctuations exist, which include rapid motions within microspace (such as thermal oscillations between atoms and rotations of amino acid side chains), and relatively slow but large structural changes (such as local unfolding). Not all of these motions can be simulated because of the physical limits of currently available MD calculations. We therefore propose a new method for modeling GPCR thermal fluctuations, in which fluctuations are combined on different time scales. In this approach, we first simulate the oscillations and fluctuations of various atoms at the picosecond and nanosecond levels to obtain native structure within a short time span. We then simulate the thermal fluctuations of GPCRs in terms of their characteristic rotational motions around helical axes, which represent fluctuations of greater time scales. In our approach, some information on GPCRs that is known a priori is utilized. For instance, we know that TM helices are immobilized in a cell membrane, so their mobility is certainly low. Furthermore, this approach is based on the previous findings that when a GPCR is activated in a cell membrane, its structural changes occur by rotations around the axes of TM helices [24], and that especially TM3, TM5, and TM6 play an important role in the activation process in some GPCRs [18, 21]. Although this method does not guarantee the optimum from an exhaustive chemical calculation approach, it does have the clear advantage of reducing calculation load without sacrificing the quality of the solution obtained. We specifically focus on predicting the central part of the 7-TM helical bundle, a spacious ligand binding site, with good quality, while aiming for the use of the predicted 3D structures in in silico virtual screening in the future.

Based on this model, we propose a framework for structural search which comprises three stages, namely: (i) obtaining initial structures of a target GPCR by homology modeling using known GPCRs in their inactive forms as a template, (ii) obtaining average structures of a GPCR in a stable state after simulating fluctuations from several hundred picoseconds to a nanosecond using MD simulations, and (iii) obtaining a best 3D structure of a target GPCR in the active form through a machine-learning technique including evolutionary computation and modularized ligand-binding simulations. Our proposed method employs a real-coded genetic algorithm (GA) as an evolutionary computation to search appropriate rotation angles of helices of a target GPCR, in which discrete steps employ receptor-ligand docking simulations to evaluate the goodness of the rotation angles of the GPCR.

The core idea of the framework is that an MD simulation is used to calculate the 3D coordinates of all atoms of a target GPCR in a relaxed state in terms of heat fluctuation on the picosecond or nanosecond time scale, after which the evolutionary computation is used to determine the best ligand-bound rotation angles of helices of a GPCR as a movement on a longer time scale.

3. Methodology to Predict 3D Structure of Activated GPCR

The detailed procedure of the proposed structural search method is described as follows.(1)Determination of an initial structure: a known GPCR such as rhodopsin or beta-adrenergic receptor is used as a template. After alignment of the primary amino acid sequences of the template and target GPCR, the inactivated structure of the target GPCR is generated by regular homology modeling, which results in an initial structure. (2)Average molecular structures with respect to fluctuations on a short-time scale: an appropriate amount of water molecules are added to the surroundings of the target GPCR. The fluctuations of GPCR molecules are then calculated by MD simulations at the level of several hundred picoseconds. The average structures are defined as those for which the energy values stabilize. (3)Structural changes with respect to rotational motions of helices: hypothetical rotational motions around the axes of helices constituting the target GPCR (as fluctuations of greater-time scale) are generated. These are fed into a real-coded GA [25], as explained above, including simulations of binding with known ligands to determine optimal values. Details of the steps are described as follows. In addition, the schemes for generation alternation and offspring creation are shown in Figure 1. (3.1)Generation of initial populations. Individuals are assigned real-number vectors representing rotational angle changes relative to the average structures of various helices. These real-number vectors are treated as values within the space adjoining the two ends of the search space (i.e., toroidal space.) An initial population of individuals is randomly generated. (3.2)Creation of offspring for each generation. Parents (two individuals) for random crossover of population pools are selected to match with a third parent, so as to generate offspring by the unimodal normal distribution crossover (UNDX) method [26]. The third parent is used to determine the standard deviation of normal distribution in UNDX. This procedure is repeated times to provide offspring individuals. In total, the number of individuals in a generation is (offspring and two parents). (3.3)Selection in each generation. To evaluate each individual, receptor-ligand docking simulations are conducted using a known agonist and a known antagonist. An individual’s real-valued vector translates into a corresponding 3D structure, with its side chains optimized before the binding simulations. An evaluation function has been established to represent how good the binding is. For a single individual, ligand-GPCR docking simulations are conducted up to times, and each evaluation score is recorded. Upon obtaining the top score for each individual, the scores are then used to rank all individuals in a generation. The two individuals highest in ranking are selected (elite strategy) to replace the two parents in the population.(3.4)Reiteration. Steps (3.2) and (3.3) are repeated until some stop criteria are met.

The values for and can be determined based on previous researches and/or preliminary experiments. In our proposed search method, generation transition is achieved via the minimal generation gap (MGG) method [27]. MGG has good compatibility with UNDX and contributes to maintaining diversity in the population.

4. Test Case of Human Leukotriene B4 Receptor BLT1

This study reports on a novel approach to molecular modeling of 7-TM proteins that has been developed to build plausible active states of any given GPCR by using evolutionary computation. This heuristic approach starts from the amino-acid sequence of a target GPCR. We performed a validation test, where human leukotriene B4 receptor BLT1 was used as a sample target GPCR.

4.1. Leukotriene Receptor

Leukotriene (LT) is a bioactive lipid that serves as an important mediator of host defense, though it is also known to be implicated in bronchial asthma as a pathogenetic or precipitating factor. To date, four types of LT receptors have been cloned. One of these receptors, the high-affinity human LTB4 receptor BLT1 (GPCRDB entry ID: LT4R1_HUMAN; UniProt entry ID: Q15722), was selected as a target GPCR in our experiments. The length of the amino-acid sequence of BLT1 is 352aa.

4.2. Experimental Procedure

The sequence of BLT1 was retrieved from the UniProt database. ClustalX software [28] was used to align the sequence with the crystal structure of the bovine rhodopsin (UniProt entry ID: P02699) with which it shares a sequence similarity of more than 54%. One 3D structure of BLT1 obtained after running homology modeling using the bovine rhodopsin (PDB ID: 1L9H) as a template was selected from ModBase [29] as an initial rough 3D structure. ModBase is an open database of comparative protein structure models, theoretically calculated by the modeling pipeline ModPipe, which is maintained by Sali [30].

After annealing for atomic relaxation, MD simulations having distance restraints similar to a way proposed by Gouldson et al. [22] were run by using the TINKER software (ver. 4.2) [31]. The simulation conditions used were: force field = AMBER99; temperature = 310 K; pressure = 1 atm; time step = 1.0 fs. An average structure was determined after reaching a stable state, at which no steep drop in the molecular energy was observed. MD simulations for several hundred picoseconds were needed to achieve such a stable state.

The following are the processes of an evolutionary computation method in terms of optimizing the rotation angles of TM helices. The previous work performing amino-acid residue substitution analysis and spectroscopic experiments found that “TM3, TM5, and TM6 play an important role in activation of the leukotriene receptor [32].” This let us bound the free rotation space searched to these three helices; hence, the solutions we seek are the rotation angles of the three helices. Our proposed method employs a real-coded GA as a search algorithm and the ligand-binding simulation as an evaluation tool. An individual, which is defined as a real number vector representing the rotational angles of TM3, TM5, and TM6, has a unique value. In the evolutionary search process, the genetic operations mentioned in the previous section are repeated until the point when the stop criteria are met (the maximum generation number is 200, determined based on a preliminary experiment). The computational parameters in the evolutionary search are as follows: the initial population size is 50, and the number of offspring in a generation is 8. For evaluation of individuals, two sets of ligand-binding simulations are independently performed using a BLT1 agonist 12-keto-LTB4 (PubChem CID: 5280876) and a BLT1 antagonist pranlukast (PubChem CID: 115100). Docking of these ligands into the 3D structure of each individual, which is restored from the individual’s vector representing the rotational angles of three helices to the 3D structure through structural relaxation by an ab initio (first-principle approach) computational method, is carried out with the GOLD software (ver. 3.1) [33]. The evaluation function in the evolutionary process is determined using the GOLD scores obtained from the docking simulations. The maximum GOLD scores derived from the docking with the BLT1 agonist and the BLT1 antagonist are assigned as and , respectively. The evaluation function value is shown below.

When , evaluation function value = ; for all cases other than this condition, evaluation function value = .

Finally, a best BLT1 structure obtained through the proposed method is evaluated by executing the docking simulations with many ligands other than ligands used in the evolutionary search process.

4.3. Results and Discussion

A computer with an Intel Xeon 3.6 GHz CPU (dual processor) was used for computation. For MD processing of 219.3-ps simulations, 524 hours were required. We investigated the changes in GPCR molecular energy values (kcal/mol) for every 0.1 ps time elapsed in MD, as shown in Figure 2. Lower-energy values suggest corresponding increases in structural stability. At the point of approximately 200 ps, energy values ceased to further fall, which suggests that a region of stability had been reached. As our study focused on MD simulations for structural changes on a short-time scale (meaning that the objectives were to simulate structural relaxation), we considered 200 ps to be a sufficient level and therefore stopped at 219.3 ps.

For subsequent evolutionary computation, 899 hours were required for calculations for 200 generations. Figure 3 shows the trends of scores in evolutionary computation (the higher the scores, the better). The optimal solutions for rotational angles after 200 generations were: TM3 = 11°, TM5 = 14°, and TM6 = 255°.

Figure 4 shows the state of binding between the LT receptor and its agonist in one of the best 3D structures computed. The 3D structures of binding are shown in Figure 4(a) with a view from above the cell plasma membrane and in Figure 4(b) with a cross-section of the membrane (the upper part being the extracellular space). In order to highlight receptor-ligand interactions, the main chains (backbones) of the helices are shown as ribbons, while the BLT1 agonist 12-keto-LTB4 is drawn as a ball-and-stick structure. From Figure 4, it is clear that the BLT1 agonist binds to a recessed region (pocket) formed by TM3, TM5, and TM6. Upon further analysis regarding the binding state of the LT receptor and its ligand, these helices responsible for interacting with the LT ligand are composed mostly of hydrophobic amino acid residues, suggesting that the BLT1 ligand is drawn into a strongly hydrophobic environment. Since the BLT1 agonist has many hydrophobic groups, it is likely that ligand-receptor binding is contributed to mainly by hydrophobic interactions, though several hydrogen bonding sites contribute to the binding. The following interactions are worth detailing.(i)Hydrophobic interactions between TM3 and the alkyl side chain near the central carbonyl (–C=O) group of the BLT1 agonist.(ii)Hydrophobic interactions between TM5 and the alkyl side chain near the central carbonyl group of the BLT1 agonist.(iii)Hydrogen bonding between the –OH group in the carboxyl (–COOH) group of the BLT1 ligand and the carbonyl group of Asn241 on TM6 (C=O H–O intermolecular distance 2.32 Å.).(iv)Hydrogen bonding between the central carbonyl group of the BLT1 agonist and the main chain (–N–H peptide bond) of Gly246 on the loop joining TM6 and TM7 (C=O H–O intermolecular distance 2.13 Å.).(v)Hydrophobic interactions between TM6 and all alkyl side chains of the BLT1 agonist.In addition to TM3, TM5, and TM6, we speculate that TM7 also contributes to hydrophobic interactions with the alkyl side chains of the BLT1 agonist.

At the beginning of evaluation, we calculated the root mean square deviation (RMSD) of BLT1 structures to investigate the structure change. Firstly, we obtained initial structures from homology modeling as structure I, MD relaxed structures as structure II, and structures generated from post-MD evolutionary computation as structure III to calculate the RMSD of 1,316 atoms forming the backbones. The resultant RMSDs were 4.49 Å for structures I and II, 1.55 Å for structures II and III, and 4.63 Å for structures I and III. Based on these values, the transition from structure I to structure II involved a large change. The reason why the transition from structure II to structure III was a smaller change is that the evolutionary search process only altered the rotation angle of TM3, TM5, and TM6. Nevertheless, what is interesting is that in the transition from structure II to structure III, the amount of rotational angle change in TM6 was clearly large compared with the other two TM helices: TM3 = 11°, TM5 = 14°, and TM6 = 255°. This is consistent with other previous research showing that the TM6 region plays a very important role in the transition from inactive-state to active-state rhodopsin [34]. Since GPCRs including the leukotriene receptor are allosteric proteins, the conformational change from inactive to active state likely affects the state of engagement with the associated G-protein. However, details of this mechanism require additional research.

In order to check the validity of LT receptor structures obtained by our method, we further used 6 known BLT1 agonists (including 12-keto-LTB4) and 14 known BLT1 antagonists (including pranlukast) to independently perform binding simulations with BLT1 of the optimal 3D structure, from which the corresponding maximum GOLD scores were determined. The results are as shown in Figure 5 such that the agonists and antagonists are plotted in order of decreasing scores. The agonist group produced scores that were visibly higher than those of the antagonist group. By using the Wilcoxon signed rank test (which makes no assumptions on normality of distribution), the GOLD scores of the agonist group and the antagonist group were found to be significantly different ( ). This strongly suggests that the binding pocket obtained by our search method selectively binds with the BLT1 agonists.

Next, in order to assess the quality of the agonist binding states, we have challenged the best 3D structure model with a set of 144 GPCR ligands to determine whether the structure indeed shows a good preference for BLT1 agonists. The ligand set included 6 BLT1 agonists (including 12-keto-LTB4), 14 BLT1 antagonists (including pranlukast), and 124 other GPCR ligands (32 against serotonin receptors, 17 against histamine receptors, 17 against adenosine receptors, 14 against prostanoid receptors, 10 against dopamine receptor, 8 against adrenoceptors, 8 against angiotensin receptors, 7 against cannabinoid receptors, and 11 against other GPCRs), as shown in Table 1. Being GPCR ligands, these ligands automatically have “drug-like” properties, so the only filtering on the ligand selection was to ensure that BLT1- and non-BLT1 ligands had a similar molecular mass distribution. The ligand-binding simulations were individually performed using the GOLD software. The overall docking results for the most highly ranked conformations are shown in Figure 6. Based on the GOLD binding score (higher indicates better binding), all 6 BLT1 agonists were ranked in the top 11. The enrichment curve in Figure 6 shows that there is immediate enrichment, and the results are good compared to related studies [22, 35]. This also suggests that the binding pocket obtained by our search method selectively binds with the BLT1 agonists.

No. PubChem ID Receptor name Ligand GLIDA ID Molecular weight

1 5280492 Leukotriene receptor Agonist L000354 336
2 5280745 Leukotriene receptor Agonist L000056352
3 5283156 Leukotriene receptor Agonist L000050 320
4 5280876 Leukotriene receptor Agonist L000049 334
5 5283129 Leukotriene receptor Agonist 336
6 5280877 Leukotriene receptor Agonist L000055 366
7 159476 Leukotriene receptor Antagonist L001349 304
8 3081307 Leukotriene receptor Antagonist L011298 414
9 6444688 Leukotriene receptor Antagonist L002037 502
10 6439064 Leukotriene receptor Antagonist L002263 360
11 6449854 Leukotriene receptor Antagonist L000610 361
12 6540750 Leukotriene receptor Antagonist L015882 346
13 132425 Leukotriene receptor Antagonist L006854 358
14 6439436 Leukotriene receptor Antagonist L009515 536
15 115100 Leukotriene receptor Antagonist L000493 481
16 6442838 Leukotriene receptor Antagonist L001488 455
17 204055 Leukotriene receptor Antagonist L006005 537
18 177941 Leukotriene receptor Antagonist L001468 544
19 196905 Leukotriene receptor Antagonist L006944 539
20 192617 Leukotriene receptor Antagonist L011726 600
21 68555 Dopamine receptor Agonist L013392 307
22 55483 Dopamine receptor Agonist L013398 356
23 5760 Dopamine receptor Agonist L000836 303
24 3341 Dopamine receptor Agonist L000254 306
25 5281878 Dopamine receptor Antagonist L000188 434
26 4748 Dopamine receptor Antagonist L000919 403
27 28864 Dopamine receptor Antagonist L000349 338
28 5265 Dopamine receptor Antagonist L000569 395
29 2818 Dopamine receptor Antagonist L000195 327
30 5074 Dopamine receptor Antagonist L001003 477
31 4038180 Adrenoceptors Agonist L000090 309
32 3251 Adrenoceptors Agonist L000768 581
33 13109 Adrenoceptors Agonist L000895 312
34 2419 Adrenoceptors Antagonist L000137 385
35 3372 Adrenoceptors Antagonist L000257 437
36 5640 Adrenoceptors Antagonist L000080 401
37 47811 Adrenoceptors Antagonist L000474 314
38 2369 Adrenoceptors Antagonist L000125 307
39 2790 Histamine receptor Antagonist L000192 308
40 55482 Histamine receptor Antagonist L000593 441
41 41376 Histamine receptor Antagonist L000313 321
42 3219 Histamine receptor Antagonist L001093 302
43 5533 Histamine receptor Antagonist L000771 371
44 65895 Histamine receptor Antagonist L000115 380
45 3947 Histamine receptor Antagonist L000944 418
46 2678 Histamine receptor Antagonist L000655 388
47 3957 Histamine receptor Antagonist L000667 382
48 2342 Histamine receptor Antagonist L000844 404
49 4830 Histamine receptor Antagonist L000727 375
50 475096 Histamine receptor Antagonist L000558 440
51 4940 Histamine receptor Antagonist L000807 340
52 4066 Histamine receptor Antagonist L000704 322
53 3348 Histamine receptor Antagonist L000869 501
54 124488 Histamine receptor Antagonist L001444 608
55 5282450 Histamine receptor Antagonist L001378 392
56 132059 Serotonin receptor Agonist L000336 411
57 127728 Serotonin receptor Agonist L000873 465
58 197706 Serotonin receptor Agonist L000746 403
59 57347 Serotonin receptor Agonist L000797 415
60 219050 Serotonin receptor Agonist L001062 346
61 3408722 Serotonin receptor Agonist L000428 365
62 4440 Serotonin receptor Agonist L000432 335
63 56971 Serotonin receptor Agonist L000682 401
64 91273 Serotonin receptor Agonist L000863 383
65 71351 Serotonin receptor Agonist L001106 420
66 5311258 Serotonin receptor Agonist L000364 351
67 5761 Serotonin receptor Agonist L000352 323
68 5311097 Serotonin receptor Agonist L000365 351
69 37816 Serotonin receptor Agonist L000794 340
70 3292447 Serotonin receptor Agonist L001346 486
71 16362 Serotonin receptor Antagonist L000494 461
72 3559 Serotonin receptor Antagonist L000288 375
73 55216 Serotonin receptor Antagonist L000503 346
74 4431 Serotonin receptor Antagonist L000685 393
75 5073 Serotonin receptor Antagonist L000510 410
76 2726 Serotonin receptor Antagonist L000182 318
77 55752 Serotonin receptor Antagonist L000720 440
78 5684 Serotonin receptor Antagonist L000012 422
79 28693 Serotonin receptor Antagonist L000396 403
80 4106 Serotonin receptor Antagonist L000397 356
81 68848 Serotonin receptor Antagonist L000394 362
82 37459 Serotonin receptor Antagonist L000016 361
83 107780 Serotonin receptor Antagonist L000275 497
84 3378093 Serotonin receptor Antagonist L000538 520
85 1229 Serotonin receptor Agonist L000011 321
86 3654103 Serotonin receptor Agonist L000142 406
87 4585 Serotonin receptor Antagonist L000455 312
88 133083 Angiotensin receptor Antagonist L008043 490
89 114899 Angiotensin receptor Antagonist L000467 480
90 3749 Angiotensin receptor Antagonist L000319 428
91 60921 Angiotensin receptor Antagonist L000533 610
92 2541 Angiotensin receptor Antagonist L000156 440
93 5281037 Angiotensin receptor Antagonist L000248 424
94 60846 Angiotensin receptor Antagonist L000621 435
95 5833 Angiotensin receptor Antagonist L001221 416
96 472880 Follicle stimulating hormone Agonist L000979 352
97 125672 Melanocortin hormone Agonist L000639 342
98 6434259 Prostanoid receptor Agonist L000194 424
99 6436393 Prostanoid receptor Agonist L000160 350
100 5311027 Prostanoid receptor Agonist L001211 415
101 5311503 Prostanoid receptor Agonist L000634 384
102 119304 Prostanoid receptor Agonist L000151 368
103 5311493 Prostanoid receptor Agonist L024041 350
104 6433212 Prostanoid receptor Agonist L000478 354
105 6439022 Prostanoid receptor Agonist L000570 362
106 122021 Prostanoid receptor Antagonist L000153 459
107 160 Prostanoid receptor Antagonist L000705 354
108 5311213 Prostanoid receptor Antagonist L024043 442
109 50294 Prostanoid receptor Antagonist L001326 371
110 132836 Prostanoid receptor Agonist L000098 388
111 5311035 Prostanoid receptor Agonist L000150 408
112 164437 Adenosine receptor Agonist L000290 388
113 104795 Adenosine receptor Agonist L000437 308
114 164305 Adenosine receptor Agonist L001390 363
115 93205 Adenosine receptor Agonist L000708 385
116 123807 Adenosine receptor Agonist L000165 369
117 3086599 Adenosine receptor Agonist L001283 499
118 5311506 Adenosine receptor Antagonist L000893 337
119 1329 Adenosine receptor Antagonist L000233 304
120 393595 Adenosine receptor Antagonist L000629 403
121 176408 Adenosine receptor Antagonist L000548 345
122 5697 Adenosine receptor Antagonist L000963 428
123 64627 Adenosine receptor Antagonist L001406 356
124 1970 Adenosine receptor Agonist L001216 399
125 9576912 Adenosine receptor Agonist L001516 393
126 122246 Adenosine receptor Agonist L000951 521
127 5287468 Adenosine receptor Agonist L000058 526
128 123739 Adenosine receptor Antagonist L001123 386
129 16078 Cannabinoid receptor Agonist L000023 314
130 5281969 Cannabinoid receptor Agonist L000111 347
131 5689 Cannabinoid receptor Agonist L000626 426
132 5488671 Cannabinoid receptor Agonist L000222 395
133 104895 Cannabinoid receptor Agonist L000004 376
134 39860 Cannabinoid receptor Agonist L000994 372
135 5311257 Cannabinoid receptor Antagonist L000363 383
136 4172142 Melatonin receptor Agonist L000306 348
137 4004 Melatonin receptor Antagonist L001138 330
138 5311198 Melatonin receptor Antagonist L000326 376
139 6024 Calcitonin receptor Agonist L000905 396
140 91498 Calcitonin receptor Agonist L024117 421
141 446284 Metabotropic glutamate group I Agonist L001256 302
142 6324636 Metabotropic glutamate group I Antagonist L000366 323
143 5311262 Metabotropic glutamate group I Antagonist L000372 383
144 123885 GABA-B subtype 1 Antagonist L000172 401

Consequently, we have developed a search method capable of predicting the 3D structures of active forms of GPCRs, which is broadly consistent with known facts of the target proteins and is reasonably persuasive as a model.

5. Discussion and Conclusions

With no crystal structures of active-state GPCRs other than mutant rhodopsin [34], it is indispensable to develop a practical method to predict 3D structures of activated GPCRs from their sequence information. In this study, we proposed a method for finding one of the best structures of an activated GPCR at a level of accuracy acceptable to virtual screening. The method was validated using human leukotriene B4 receptor BLT1 as a sample GPCR. Our study demonstrated that the proposed method including homology modeling, MD simulations, and evolutionary computing was able to provide the appropriate 3D structure of the activated leukotriene receptor to specifically dock with its agonists.

The first remarkable feature of this study is that the proposed method models GPCR thermal fluctuations on multiple time scales: the short-term heat fluctuation on the picosecond or nanosecond time scale and the rotational motions of TM helices on a longer time scale. This is a kind of coarse approximation compared with other methods like rigorous long-term MD simulations. However, the proposed method provides a significant reduction in computational cost when predicting the 3D structure of GPCRs and finally leads us to obtain the structure on common personal computers.

The second feature is that a machine learning technique including evolutionary computation and modularized ligand-binding simulations is employed for the structural prediction. Though to date, there have been methods using GAs to generate various conformations of ligands to explore a most suitable conformation in ligand-receptor docking [36], our method is the first approach using a GA employing a modularized ligand-binding simulation to find a best conformation on the receptor side.

However, some improvements might remain in the evaluation function of our evolutionary computing. Essentially, a scoring system capable of closely reflecting the binding-free energy would be desirable for the conformation evaluation. In our method, the GOLD scores produced by the GOLD software are directly used for the evaluation function. Although the GOLD scores are related with the values of the binding-free energy, they are not identical. We should accordingly investigate this issue in the future.

In addition, it should be noted that our method has a tendency to predict the central part of the 7-TM helical bundle, a spacious ligand binding site, with good quality. That is, small deviations would be observed in a spacious binding pocket area, whereas larger deviations would be observed in the distal section of the helices and even larger in the loop regions. This is because the evaluation function in evolutionary computing is naturally based on the ligand-binding score, closely related with the binding free energy. Hence, our method may predict another structure having energetically highly evaluated conformations of seven helices and the loops as a best structure if another search is performed. However, the structure of the spacious ligand-binding site would emerge again. We originally have the purpose of using the predicted 3D structure for virtual screening of candidate chemicals. Our method is able to provide 3D structure accurately enough for this purpose as shown in the case of the leukotriene receptor, since the structural information of the spacious binding pocket area is the most important for virtual screening.

We anticipate that parallelization of the proposed algorithm would contribute to an improvement in computation time. Future study designs will incorporate such. Furthermore, we plan to use GPCRs other than the leukotriene receptor to test the applicability and robustness of our search method. For such investigations, it would be important to carefully select an appropriate target GPCR. Based on sequence similarity within the seven TMs, GPCRs can be grouped into six families: the rhodopsin family A, the secretin family B, the glutamate receptor family C, the fungal pheromone family D, the cAMP receptor family E, and the frizzled/smoothened receptor family F [37]. In general, GPCRs belonging to class A fit the prediction, because all known GPCRs used as templates in homology modeling are classified into the class A. In contrast, other GPCRs having low amino-acid homology would need extra processing at the preliminary stage of homology modeling.


This work was in part supported by the Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology of Japan.


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Copyright © 2012 Yoko Ishino and Takanori Harada. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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