Search for β2 adrenergic receptor ligands by virtual screening via grid computing and investigation of binding modes by docking and molecular dynamics simulations.

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Search for b2 Adrenergic Receptor Ligands by Virtual Screening via Grid Computing and Investigation of Binding Modes by Docking and Molecular Dynamics Simulations Qifeng Bai1,4, Yonghua Shao1, Dabo Pan1, Yang Zhang2, Huanxiang Liu3, Xiaojun Yao1,5* 1 Department of Chemistry, Lanzhou University, Lanzhou, China, 2 School of Information Science & Engineering, Lanzhou University, Lanzhou, China, 3 School of Pharmacy, Lanzhou University, Lanzhou, China, 4 School of Basic Medical Sciences, Lanzhou University, Lanzhou, China, 5 State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Taipa, Macau, China Abstract We designed a program called MolGridCal that can be used to screen small molecule database in grid computing on basis of JPPF grid environment. Based on MolGridCal program, we proposed an integrated strategy for virtual screening and binding mode investigation by combining molecular docking, molecular dynamics (MD) simulations and free energy calculations. To test the effectiveness of MolGridCal, we screened potential ligands for b2 adrenergic receptor (b2AR) from a database containing 50,000 small molecules. MolGridCal can not only send tasks to the grid server automatically, but also can distribute tasks using the screensaver function. As for the results of virtual screening, the known agonist BI-167107 of b2AR is ranked among the top 2% of the screened candidates, indicating MolGridCal program can give reasonable results. To further study the binding mode and refine the results of MolGridCal, more accurate docking and scoring methods are used to estimate the binding affinity for the top three molecules (agonist BI-167107, neutral antagonist alprenolol and inverse agonist ICI 118,551). The results indicate agonist BI-167107 has the best binding affinity. MD simulation and free energy calculation are employed to investigate the dynamic interaction mechanism between the ligands and b2AR. The results show that the agonist BI-167107 also has the lowest binding free energy. This study can provide a new way to perform virtual screening effectively through integrating molecular docking based on grid computing, MD simulations and free energy calculations. The source codes of MolGridCal are freely available at Citation: Bai Q, Shao Y, Pan D, Zhang Y, Liu H, et al. (2014) Search for b2 Adrenergic Receptor Ligands by Virtual Screening via Grid Computing and Investigation of Binding Modes by Docking and Molecular Dynamics Simulations. PLoS ONE 9(9): e107837. doi:10.1371/journal.pone.0107837 Editor: Roland Seifert, Medical School of Hannover, Germany Received January 21, 2014; Accepted August 23, 2014; Published September 17, 2014 Copyright: ß 2014 Bai et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 21175063) and the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT: IRT1137). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * Email: Introduction Grid computing can collect computer resources from different locations to deal with data more effectively and rapidly [1]. This advantage of grid computing has led to its wide and successful applications in many different fields, such as Hadron Collider [2], nuclear magnetic resonance (NMR) [3], image analysis [4] and so on. Especially, grid computing was successfully used in huge microbial sequence analysis [5–7] and biology medicine research [8–12]. Grid computing also played an important role in molecular simulations and computer aided drug discovery [13]. For example, the grid computing framework of folding@home [14] was used to simulate protein folding process by using the idle computer resources. Furthermore, the successful applications of grid computing in molecular docking suggested that virtual screening could also be integrated into grid computing environment [15] due to the fact that many molecular docking programs [16–21], and small molecule databases [22–24] are available. The screensaver project of grid computing could supply enough computing resource to perform effective virtual screening [25]. The use of virtual screening [26–28] based on molecular docking could improve the efficiency and save the cost of drug discovery. There have been several successful cases that use grid computing technology to perform virtual screening [29–35]. Molecular docking can only provide static interaction between the ligand and protein. It cannot provide enough information about the details of dynamic interaction process of protein and ligand. Molecular dynamics (MD) simulations have been proved to be very useful to explore the dynamical interaction between protein and ligand. MD simulations have been successfully used to study the interaction mechanism of the active and inactive states of b2 adrenergic receptor (b2AR) in complex with different ligands [36]. Based on MD simulations and anisotropic network model, the ligands were considered as ‘‘computational probe’’ to distinguish the different conformations of b2AR [37]. Free energy calculations of agonist, antagonist and inverse agonist of b2AR indicated that different ligands had significant difference of binding affinity and free energy [38]. MD simulations also proved that the crystal waters in the pocket of b2AR could form hydrogen bonds network to stabilize the agonist-receptor interaction [39]. PLOS ONE | 1 September 2014 | Volume 9 | Issue 9 | e107837 Ligands Search for b2 Adrenergic Receptor by Virtual Screening Figure 1. The flowchart of virtual screening based on grid computing. MolGridCal can firstly submit the works into the server. Then the server distributed works into different nodes. The finished works would be gathered to find the candidate compounds. doi:10.1371/journal.pone.0107837.g001 The long unbiased MD simulations showed that there were two energetic barriers when the ligands entered into the pocket of b2AR [40]. At the same time, the dissociation pathway between ligands and b2AR was identified in the two secondary binding pockets in the extracellular part of b2AR [41]. MD simulations of b2AR in complex with Gs protein, which showed the interaction between the agonist BI-167107 and b2AR, supplied an activation mechanism of b2AR [42]. b2AR in complex with the inverse agonist, antagonist and agonist showed only the inverse agonist could induce the motion of Gas and Gbc domain by changing the conformation of b2AR [43]. In this work, we developed a new virtual screening program called MolGridCal based on grid computing. Furthermore, binding modes of possible ligands were further studied by combining molecular docking, MD simulations and free energy calculations. To test the use of the developed program and strategy, b2AR was selected as the model target. b2AR is distributed in the smooth muscle round the human body and plays an important role on the asthma and Alzheimer’s disease (AD) [44–46]. The ligands of b2AR could regulate the smooth muscle relaxation, the vasodilation of muscle and liver, the dilation of bronchial passages, the relaxation of uterine muscle, insulin release and treat asthma and pulmonary disease [47–54]. In this work, MolGridCal was used to screen the ligands of b2AR from small molecule database. The screened ligands were refined by more accurate docking and scoring methods. The dynamic interaction mechanism between ligands and b2AR was further investigated by MD simulations and free energy calculations. Our strategy of virtual screening could not only extract the confirmed ligands from the small-database successfully, but also provided a more useful and accurate way to screen a large number of small molecule database. Figure 2. The implementation process about of MolGridCal. doi:10.1371/journal.pone.0107837.g002 Materials and Methods Protein and Ligand Preparation To screen the ligands of b2AR, the active conformation of b2AR was extracted from PDB database (PDB code: 3SN6 [55]). The Gs protein and ligand were removed in molecular docking. AutoDockTools [18,56] was employed to add the Gasteiger charges and polar hydrogen on b2AR. The grid box in the pocket of b2AR was set to 30 A˚630 A˚630 A˚ around the position of ligand of b2AR. Autodock VINA [16] was chosen to screen the ligands of b2AR. Here, 50,000 drug-like molecules with ionization states at pH 7 were selected from ZINC database [22,57]. These 50,000 drug like molecules together with the agonist BI-167107 [55], neutral antagonist alprenolol [58] and inverse agonist ICI 118,551 [58] formed a small molecule database for virtual screening in grid computing environment. Grid Computing To perform virtual screening in the grid computing environ- ment, a program MolGridCal on basis of JPPF environment was designed in this work. The source codes of MolGridCal were freely available at In the MolGridCal environment, we could use two modes of load-balancing of JPPF ( one was ‘‘manual’’ mode which sent the fixed number of tasks to the each node; the other was ‘‘autotuned’’ algorithm which used the adaptive heuristic algorithm of Monte Carlo algorithm to transfer tasks to the calculation nodes. The total running time of MolGridCal was calculated as equation 1: X t~ (AizBizCi) i~1,2,3::: i ð1Þ Ai was the time of initial program to download ligand files from PLOS ONE | 2 September 2014 | Volume 9 | Issue 9 | e107837 Ligands Search for b2 Adrenergic Receptor by Virtual Screening Figure 3. Total running time versus number of tasks. (A) Running time using autotuned algorithm. (B) Running time using manual algorithm. The size represented the number of tasks. doi:10.1371/journal.pone.0107837.g003 the FTP server. Bi represented the time to run molecular docking. Ci was the time of current tasks to end to upload the docking results. The parameters of MolGridCal can be set easily. Firstly, it needs to know the IP address of FTP server, the small molecule database and uploading directory on FTP server. Secondly, it needs to specify which program of molecular docking will be called. In order to save the memory of computers, we recommend about 50,000 molecules per virtual screening in one folder. Of course, if the computers have enough memory for the cache store, the number of molecules for per virtual screening can be raised in one folder. If more than ten million small molecules are needed for virtual screening, we can use a program called VSBath in MolGridCal software package. VSBath can read the folder name to carry out the grid computing task one by one. The memory of computers will be able to release in time. The final network in our experiment contained 40 computers as computation nodes and 1 computer as the server node. Refinement by Accurate Molecular Docking The virtual screening was carried out based on the crystal structure of b2AR using MolGridCal and Autodock VINA. In order to obtain more reliable and accurate virtual screening results, several other docking software including LibDock [59], CDOCKER [60] and Flexible Docking modules of Discovery Studio 2.5 (DS2.5) were employed. In Libdock, the ligands were docked into the protein based on the polar and nonpolar hotspot of the features of protein active sites. In this experiment, 100 grids were generated in the region of radius of 8 A˚ around the agonistbound sites of b2AR. CHARMM force field [61] was employed for energy minimization. CDOCKER was a semi-flexible docking program on basis of molecular dynamics. High temperature molecular dynamics simulation was used to search the flexible conformation of ligands. Simulated annealing method was employed to optimize the conformation on the active site of receptor. The heating target temperature was set to 700 K, and the heating steps were assigned to 2000. In DS Flexible Docking, PLOS ONE | 3 September 2014 | Volume 9 | Issue 9 | e107837 Ligands Search for b2 Adrenergic Receptor by Virtual Screening Figure 4. The GUI of MolGridCal and the binding pocket of b2AR. (A) The displayed information of GUI for monitoring MolGridCal. (B) The top three compounds, agonist, antagonist, inverse agonist bound to the pockets of b2AR. Different ligands were represented by different colors. TOP1,2,3, agonist BI-167107, antagonist alprenolol and inverse agonist ICI 118,551 were colored in blue, red, gray, orange, yellow and green, respectively. doi:10.1371/journal.pone.0107837.g004 the side chain clusters were generated using ChiFlex method. DS Flexible Docking based on CDOCKER method was used in simulated annealing and energy minimization. In the DS Flexible Docking, the residues of radius of 4 A˚ around BI-167107 in the b2AR were chosen as the flexible residues. The same parameters with CDOCKER method was used in the energy minimization and simulated annealing. Molecular Dynamics Simulations The crystal structure of b2AR was obtained from PDB database (PDB code: 3SN6 [55]). The T4 lysozyme, nanobody (Nb35) and BI-167107 were deleted from the crystal structure of b2AR. The lack loop sequence (FHVQNLSQVEQDGRTGHGLRRSSKF) of TM5 and TM6 was built by MODELLER program [62]. The obtained loop region was further optimized by loop model algorithm [62]. b2AR was then modeled in complex with the agonist BI-167107, antagonist alprenolol and inverse agonist ICI 118,551, respectively. The explicit membrane around the transmembrane region was constructed using the 1-palmitoyl-2-oleoyl- sn-glycero-3-phosphocholine (POPC) lipids. The sizes of membrane were 120 A˚6120 A˚ . The complex of b2AR and Gs (b2ARGs) protein was immersed into TIP3P water [63] box along Z direction. To get the neutral system, seven sodium ions were added into the water box. The entire system, whose dimension was 120 A˚6120 A˚6150 A˚ , contained the lipids, water, ions, ligands, a-helix domain, Gas, Gbc and b2AR. The atomic number of final system was about 200,010 per periodic cell. The CHARMM force field parameters of BI-167107, alprenolol and ICI 118,551 were modeled using VMD Paratool Plugin v1.2 [64,65] and Gaussian 98 Revision A.9 [66]. The geometry optimization and single point calculation were both performed at the theory of RHF/6–31G* level and tight SCF convergence criteria. MD simulations were performed on the b2AR in complex with different ligands. The lipid tail was minimized for 100 ps and equilibrated for 1000 ps at the constant temperature of 300 K and constant pressure of 1 bar firstly. By constraining the protein and ligand, the studied systems were further minimized for 100 ps based on the conjugate gradient method and equilibrated for Table 1. The docking scores of different ligands using Autodock VINA, LibDock, CDOCKER and Flexible Docking. BI-167107 Alprenolol ICI 118,551 Top 1 Top 2 Top 3 AutoDock VINA (kcal/mol) 210.6 27.2 28.3 212.0 211.8 211.6 doi:10.1371/journal.pone.0107837.t001 LibDock Score 155.846 111.429 110.088 129.346 125.073 128.991 PLOS ONE | 4 CDOCKER (kcal/mol) 250.842 234.728 221.709 217.769 210.599 29.783 Flexible Docking (kcal/mol) 251.008 238.276 228.418 223.556 214.274 214.167 September 2014 | Volume 9 | Issue 9 | e107837 Ligands Search for b2 Adrenergic Receptor by Virtual Screening Figure 5. Analysis of conformation of different ligands. Delineating conformational differences between (A) TOP1-TOP3, (B) TOP1-TOP2, (C) TOP2-TOP3, (D) TOP1-BI-167107 in the pocket of b2AR. The black oval showed the key atoms for the binding pocket of b2AR. doi:10.1371/journal.pone.0107837.g005 1000 ps. Then the whole systems were equilibrated freely for 5 ns. At last, 10 ns MD simulations were run. All MD simulations were performed using NAMD (version 2.9b3) [67] with CHARMM 27 force field [61] in the periodically infinite lipid and explicit solvent. The particle-mesh Ewald (PME) [68] method was used to calculate the electrostatics with a 12 A˚ nonbonded cutoff. The constant temperature of 300 K and pressure of 1 bar employed the langevin thermostat and langevin barostat [69] method, respectively. Time step was set to 2 fs. The trajectory frames were save every 1 ps for analysis. All MD simulations were carried out on 12 cores of an array of two 2.66-GHz Intel Xeon 5650 processors and 4 pieces of NVDIA Tesla C 2050 GPU computing processors. To study the antagonist and inverse agonist in their native crystal receptor, we built two MD simulation systems based on the crystal structures of b2AR in complex with antagonist alprenolol (PDB ID: 3NY8) and inverse agonist ICI 118,551 (PDB ID: 3NYA). The size of POPC membrane was set to 80 A˚680 A˚ . The final dimension of system boxes were 80 A˚680 A˚6100 A˚ . MD simulations on these two complexes were ran using the same parameters with above agonist-bound b2AR. Free Energy Calculations Adaptive biasing force (ABF) method [70–72] can provide details about the free energy of dissociation between the ligand and protein using the biasing force which could offset the local barriers effectively. Here, the reaction coordinate (RC) was projected onto the Z direction. The free energy DG along the Z axis was defined as equation 2: ðjb DGa?b~{ Fjdj ja ð2Þ Fj represented the biasing force. The center of A119, F208, T283 and N318 was selected as the reference point. Because the length of Z equilibrium value of MeCpG steps (,+14 deg.) [31,44]. In comparison, methylation has a significantly lower stability cost when happening at major groove positions, such as 211 and 21 base pair from dyad (mutations 9 and 12), where the roll of the nucleosome bound conformation (+10 deg.) is more compatible with the equilibrium geometry of MeCpG steps. The nucleosome destabilizing effect of cytosine methylation increases with the number of methylated cytosines, following the same position dependence as the single methylations. The multiple-methylation case reveals that each major groove meth- PLOS Computational Biology | 3 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning ylation destabilizes the nucleosome by around 1 kJ/mol (close to the average estimate of 2 kJ/mol obtained for from individual methylation studies), while each minor groove methylation destabilizes it by up to 5 kJ/mol (average free energy as single mutation is around 6 kJ/mol). This energetic position-dependence is the reverse of what was observed in a recent FRET/SAXS study [30]. The differences can be attributed to the use of different ionic conditions and different sequences: a modified Widom-601 sequence of 157 bp, which already contains multiple CpG steps in mixed orientations, and which could assume different positioning due to the introduction of new CpG steps and by effect of the methylation. The analysis of our trajectories reveals a larger root mean square deviation (RMSD) and fluctuation (RMSF; see Figures S2– S3 in Text S1) for the methylated nucleosomes, but failed to detect any systematic change in DNA geometry or in intermolecular DNA-histone energy related to methylation (Fig. S1B, S1C, S4–S6 in Text S1). The hydrophobic effect should favor orientation of the methyl group out from the solvent but this effect alone is not likely to justify the positional dependent stability changes in Figure 2, as the differential solvation of the methyl groups in the bound and unbound states is only in the order of a fraction of a water molecule (Figure S5 in Text S1). We find however, a reasonable correlation between methylation-induced changes in hydrogen bond and stacking interactions of the bases and the change in nucleosome stability (see Figure S6 in Text S1). This finding suggests that methylation-induced nucleosome destabilization is related to the poorer ability of methylated DNA to fit into the required conformation for DNA in a nucleosome. Changes in the elastic deformation energy between methylated and un-methylated DNA correlate with nucleosomal differential binding free energies To further analyze the idea that methylation-induced nucleosome destabilization is connected to a worse fit of methylated DNA into the required nucleosome-bound conformation, we computed the elastic energy of the nucleosomal DNA using a harmonic deformation method [36,37,44]. This method provides a rough estimate of the energy required to deform a DNA fiber to adopt the super helical conformation in the nucleosome (full details in Suppl. Information Text S1). As shown in Figure 2, there is an evident correlation between the increase that methylation produces in the elastic deformation energy (DDE def.) and the free energy variation (DDG bind.) computed from MD/TI calculations. Clearly, methylation increases the stiffness of the CpG step [31], raising the energy cost required to wrap DNA around the histone octamers. This extra energy cost will be smaller in regions of high positive roll (naked DNA MeCpG steps have a higher roll than CpG steps [31]) than in regions of high negative roll. Thus, simple elastic considerations explain why methylation is better tolerated when the DNA faces the histones through the major groove (where positive roll is required) that when it faces histones through the minor groove (where negative roll is required). Nucleosome methylation can give rise to nucleosome repositioning We have established that methylation affects the wrapping of DNA in nucleosomes, but how does this translate into chromatin structure? As noted above, accumulation of minor groove methylations strongly destabilizes the nucleosome, and could trigger nucleosome unfolding, or notable changes in positioning or phasing of DNA around the histone core. While accumulation of methylations might be well tolerated if placed in favorable positions, accumulation in unfavorable positions would destabilize the nucleosome, which might trigger changes in chromatin structure. Chromatin could in fact react in two different ways in response to significant levels of methylation in unfavorable positions: i) the DNA could either detach from the histone core, leading to nucleosome eviction or nucleosome repositioning, or ii) the DNA could rotate around the histone core, changing its phase to place MeCpG steps in favorable positions. Both effects are anticipated to alter DNA accessibility and impact gene expression regulation. The sub-microsecond time scale of our MD trajectories of methylated DNAs bound to nucleosomes is not large enough to capture these effects, but clear trends are visible in cases of multiple mutations occurring in unfavorable positions, where unmethylated and methylated DNA sequences are out of phase by around 28 degrees (Figure S7 in Text S1). Due to this repositioning, large or small, DNA could move and the nucleosome structure could assume a more compact and distorted conformation, as detected by Lee and Lee [29], or a slightly open conformation as found in Jimenez-Useche et al. [30]. Using the harmonic deformation method, we additionally predicted the change in stability induced by cytosine methylation for millions of different nucleosomal DNA sequences. Consistently with our calculations, we used two extreme scenarios to prepare our DNA sequences (see Fig. 3): i) all positions where the minor grooves contact the histone core are occupied by CpG steps, and ii) all positions where the major grooves contact the histone core are occupied by CpG steps. We then computed the elastic energy required to wrap the DNA around the histone proteins in unmethylated and methylated states, and, as expected, observed that methylation disfavors DNA wrapping (Figure 3A). We have rescaled the elastic energy differences with a factor of 0.23 to match the DDG prediction in figure 2B. In agreement with the rest of our results, our analysis confirms that the effect of methylation is position-dependent. In fact, the overall difference between the two extreme methylation scenarios (all-in-minor vs all-in-major) is larger than 60 kJ/mol, the average difference being around 15 kJ/ mol. We have also computed the elastic energy differences for a million sequences with CpG/MeCpG steps positioned at all possible intermediate locations with respect to the position (figure 3B). The large differences between the extreme cases can induce rotations of DNA around the histone core, shifting its phase to allow the placement of the methylated CpG steps facing the histones through the major groove. It is illustrative to compare the magnitude of CpG methylation penalty with sequence dependent differences. Since there are roughly 1.5e88 possible 147 base pairs long sequence combinations (i.e., (4n+4(n/2))/2, n = 147), it is unfeasible to calculate all the possible sequence effects. However, using our elastic model we can provide a range of values based on a reasonably large number of samples. If we consider all possible nucleosomal sequences in the yeast genome (around 12 Mbp), the energy difference between the best and the worst sequence that could form a nucleosome is 0.7 kj/mol per base (a minimum of 1 kJ/mol and maximum of around 1.7 kJ/mol per base, the first best and the last worst sequences are displayed in Table S3 in Text S1). We repeated the same calculation for one million random sequences and we obtained equivalent results. Placing one CpG step every helical turn gives an average energetic difference between minor groove and major groove methylation of 15 kJ/ mol, which translates into ,0.5 kJ/mol per methyl group, 2 kJ/ mol per base for the largest effects. Considering that not all nucleosome base pair steps are likely to be CpG steps, we can conclude that the balance between the destabilization due to CpG methylation and sequence repositioning will depend on the PLOS Computational Biology | 4 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning Figure 3. Methylated and non-methylated DNA elastic deformation energies. (A) Distribution of deformation energies for 147 bplong random DNA sequences with CpG steps positioned every 10 base steps (one helical turn) in minor (red and dark red) and major (light and dark blue) grooves respectively. The energy values were rescaled by the slope of a best-fit straight line of figure 2, which is 0.23, to source of circulating FGF-21. The lack of association between circulating and muscle-expressed FGF-21 also suggests that muscle FGF-21 primarily works in a local manner regulating glucose metabolism in the muscle and/or signals to the adipose tissue in close contact to the muscle. Our study has some limitations. The number of subjects is small and some correlations could have been significant with greater statistical power. Another aspect is that protein levels of FGF-21 were not determined in the muscles extracts, consequently we cannot be sure the increase in FGF-21 mRNA is followed by increased protein expression. In conclusion, we show that FGF-21 mRNA is increased in skeletal muscle in HIV patients and that FGF-21 mRNA in muscle correlates to whole-body (primarily reflecting muscle) insulin resistance. These findings add to the evidence that FGF-21 is a myokine and that muscle FGF-21 might primarily work in an autocrine manner. Acknowledgments We thank the subjects for their participation in this study. 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