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BioMed Research International
Volume 2014, Article ID 873010, 12 pages
http://dx.doi.org/10.1155/2014/873010
Research Article

Potential Smoothened Inhibitor from Traditional Chinese Medicine against the Disease of Diabetes, Obesity, and Cancer

1School of Pharmacy, China Medical University, Taichung 40402, Taiwan
2School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung 40402, Taiwan
3Department of Acupuncture, China Medical University Hospital, Taichung, Taiwan
4Research Center for Chinese Medicine & Acupuncture, China Medical University, Taichung, Taiwan
5Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan
6School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan
7Human Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan

Received 14 February 2014; Accepted 15 February 2014; Published 1 July 2014

Academic Editor: Chung Y. Hsu

Copyright © 2014 Kuan-Chung Chen et al. 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.

Abstract

Nowadays, obesity becomes a serious global problem, which can induce a series of diseases such as type 2 diabetes mellitus, cancer, cardiovascular disease, metabolic syndrome, and stoke. For the mechanisms of diseases, the hedgehog signaling pathway plays an important role in body patterning during embryogenesis. For this reason, smoothened homologue (Smo) protein had been indicated as the drug target. In addition, the small-molecule Smo inhibitor had also been used in oncology clinical trials. To improve drug development of TCM compounds, we aim to investigate the potent lead compounds as Smo inhibitor from the TCM compounds in TCM Database@Taiwan. The top three TCM compounds, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid), have displayed higher potent binding affinities than the positive control, LY2940680, in the docking simulation. After MD simulations, which can optimize the result of docking simulation and validate the stability of H-bonds between each ligand and Smo protein under dynamic conditions, top three TCM compounds maintain most of interactions with Smo protein, which keep the ligand binding stable in the binding domain. Hence, we propose precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid) as potential lead compounds for further study in drug development process with the Smo protein.

1. Introduction

Nowadays, obesity, which is caused by the body’s inability to handle excessive energy intake, becomes a serious global problem. It can induce a series of diseases such as type 2 diabetes mellitus, cancer, cardiovascular disease, metabolic syndrome, and stroke [1, 2]. In fact, the diseases of diabetes, obesity, and cancer have the dysregulated intracellular signaling and altered metabolic state [3]. Nowadays, increasing numbers of distinct mechanisms of diseases have been determined [46]. According to these mechanisms, increasing numbers of potential target proteins for drug design against each disease have been identified [7, 8]. The hedgehog signaling pathway plays an important role in body patterning during embryogenesis [9]. Abnormalities in hedgehog signaling pathway can lead to diabetes, obesity, and cancer [1014]. As hedgehog pathway genes encoding patched homologue 1 (Ptch1) and smoothened homologue (Smo), Smo protein had been indicated as the drug target, and the small-molecule Smo inhibitor had been used in oncology clinical trials [1518].

Many in silico researches had indicated that compounds extracted from traditional Chinese medicine (TCM) can be used as potential lead compounds for many different diseases [19], such as cancer [2023], diabetes [24], inflammation [25], influenza [26], metabolic syndrome [27, 28], stroke [2932], viral infection [33], and some other diseases [34, 35]. To improve drug development of TCM compounds, we aim to investigate the potent lead compounds as Smo inhibitor from the TCM compounds in TCM Database@Taiwan [36]. As structural disordered residues in the protein may lead to the side effect and influence the ligand to bind with target protein [37, 38], the disordered residues of Smo protein were predicted before virtual screening. After virtual screening of the TCM compounds, as the interactions between protein and ligand in the docking simulation may not be stable under dynamic conditions, the molecular dynamics (MD) simulations were performed to validate the stability of those interactions.

2. Materials and Methods

2.1. Data Collection

The X-ray crystallography structure of the human smoothened receptor (Smo) was obtained from RCSB Protein Data Bank with PDB ID: 4JKV [39]. PONDR-Fit [40] protocol was employed to predict the disordered amino acids for the sequence of Smo protein from Swiss-Prot (UniProtKB: Q99835). For the protein preparation, Prepare Protein module in Discovery Studio 2.5 (DS2.5) was employed to protonate the final structure of protein with Chemistry at HARvard Macromolecular Mechanics (CHARMM) force field [41] and remove crystal water. The binding site for virtual screening was defined by the volume of the cocrystallized antitumor agent, LY2940680. Prepare Ligand module in DS2.5 was employed to protonate the final structure of TCM compounds from TCM Database@Taiwan [36], and Lipinski’s Rule of Five [42] was applied to filter the TCM compounds after virtual screening.

2.2. Docking Simulation

LigandFit protocol [43] in DS 2.5 was employed to virtually screen the TCM compounds by docking ligands into the binding site using a shape filter and Monte-Carlo ligand conformation generation. The result of docking was then optionally minimized with CHARMM force field [41] and evaluated with Dock Score energy function as follows:

The clustering of saved docking pose was performed to reject the similar poses.

2.3. Molecular Dynamics (MD) Simulation

Gromacs [44] was employed to simulate each protein-ligand complex under dynamic conditions using classical molecular dynamics theory. The pdb2gmx protocol of Gromacs and SwissParam program [45] were employed to provide topology and parameters for Smo protein with charmm27 force field and each ligand, respectively. The Gromacs program sets the dimensions of the cubic box based upon setting the box edge approx 12 Å from the molecules periphery and solvated using TIP3P water model. Steepest descent [46] is one of the common algorithms for minimization. For this algorithm, new positions are calculated by the equation as follows: where is the vector of all 3N coordinates, is the maximum displacement and initial is given in unit of 0.01 nm, and is the force or the negative gradient of the potential .

The algorithm stops when or complete the maximum number of iterations defined in the protocol. After a steepest descent minimization with a maximum of 5,000 steps was employed to remove bad van der Waals contacts, it created a neutral system using 0.145 M NaCl model. Then another steepest descent minimization with a maximum of 5,000 steps was employed to remove bad van der Waals contacts. For the equilibration, the position-restrained molecular dynamics with the Linear Constraint algorithm for all bonds was performed with NVT equilibration, Berendsen weak thermal coupling method, and Particle Mesh Ewald method. The Berendsen weak thermal coupling method mimics with first-order kinetics an external heat bath with given temperature 300 K and slowly corrected the temperature deviation of the system by the equation as follows: where is given temperature 300 K and τ is a time constant in unit of 0.1 ps.

The MD program was then employed to simulate a total of 5000 ps production simulation with time step in unit of 2 fs under Particle Mesh Ewald (PME) option and NPT ensembles. A series of protocols in Gromacs was employed to analyze the MD trajectories.

3. Results and Discussion

3.1. Disordered Protein Prediction

The disordered disposition for the sequence of Smo protein from Swiss-Prot (UniProtKB: Q99835) predicted by PONDR-Fit was illustrated in Figure 1. As the residues in the binding domain do not lie in the disordered region, the binding domain of Smo protein has a stable structure in protein folding.

fig1
Figure 1: Disordered disposition predicted by PONDR-Fit. Sequence alignment with disordered residues (yellow regions) and residues in the binding domain (magenta regions).
3.2. Docking Simulation

Before virtual screening, the cocrystallized antitumor agent, LY2940680, had been redocked by LigandFit protocol into the binding site defined by the volume of LY2940680 (Figure 2(a)) to validate the accuracy of LigandFit protocol. The Root-mean-square deviation value between crystallized structure and docking pose of LY2940680 is 0.5106 Å (Figure 2(b)). After virtual screening, the chemical scaffold top TCM compounds ranked by Dock Score [43] and LY2940680 are illustrated in Figure 3. The scoring function of Dock Score indicates that the top three TCM compounds have higher binding affinities than LY2940680. The top three TCM compounds, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid), are extracted from Abrus precatorius L., Rosmarinus officinalis L., and Ardisia japonica, respectively. According to the docking poses in Figure 4, for positive control, LY2940680, there exists a π interaction with residue Phe484 and hydrogen bonds (H-bonds) with residues Asn219 and Arg400. Precatorine has π interactions with residues Tyr394, Arg400, Phe484, and H-bonds with residue Lys395. Labiatic acid has a π interaction with residue Phe484 and H-bonds with residues Tyr207, Lys395, and Arg400. The top 3 compounds have π interactions with residues Tyr394, Arg400, Phe484, and H-bonds with resides Tyr394, Lys395, His470, and Asn521. The docking poses displayed in Figure 4 indicate that each compound has a π interaction with residue Phe484 and interaction with common residues Lys395 and Arg400. Those interactions stabilize each compound in the binding domain of Smo protein.

fig2
Figure 2: (a) Binding site of Smo protein defined as the volume of LY2940680. (b) Root-mean-square deviation value between crystallized structure (orange) and docking pose (green) of LY2940680.
873010.fig.003
Figure 3: Chemical scaffold of controls and top three TCM candidates with their scoring function and sources.
fig4
Figure 4: Docking pose of Smo protein complexes with (a) LY2940680, (b) precatorine, (c) labiatic acid, and (d) 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid).
3.3. Molecular Dynamics Simulation

The docking simulation performed by LigandFit protocol docks compounds into binding site using a shape-based docking. Although the Monte-Carlo techniques had been employed to simulate the flexible compound by generating sets of compound conformations, the structure of target protein is a rigid body of Smo protein from the crystal structure. As the interactions between protein and ligand in the docking simulation may not be stable under dynamic conditions, the molecular dynamics (MD) simulations were performed to validate the stability of those interactions. The root-mean-square deviations (RMSDs) for each protein and ligand were displayed in Figure 5. They indicate the atomic fluctuations during MD simulation for each protein and ligand. Figure 5 shows that the atomic fluctuations of each complex tend to be stable after 4700 ps of MD simulation. The variations of total energy for each complex during 5000 ps of MD simulation were illustrated in Figure 6, which indicate that Smo protein docking with the top three TCM compounds has similar variation of total energy, and there is no significant change of total energy for each complex during 5000 ps of MD simulation. The variation of radius of gyration and mean square displacement (MSD) for proteins in each complex during 5000 ps of MD simulation was illustrated in Figures 7 and 8, respectively. They indicate that Smo protein docking with the top three TCM compounds has similar compactness and diffusion constant under dynamic conditions as LY2940680. The variation of solvent accessible surface area in Figure 9 can also be used to discuss the effect of each ligand for the Smo protein after docking. In Figure 9, it can be seen clearly that the Smo protein in each complex has similar solvent accessible surface area when the RMSDs tend to be stable after 4700 ps of MD simulation. The smallest distance between residue pairs for Smo protein in each complex illustrated in Figure 10 also has similar distance matrices. They indicate that the top three TCM compounds may cause similar influence for Smo protein as LY2940680.

fig5
Figure 5: Root-mean-square deviations in units of nm for protein (a) and ligand (b) over 5000 ps of MD simulation in Smo protein complexes with LY2940680, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid).
873010.fig.006
Figure 6: Total energy for complex over 5000 ps of MD simulation in Smo protein complexes with LY2940680, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid).
873010.fig.007
Figure 7: Radii of gyration for protein over 5000 ps of MD simulation in Smo protein complexes with LY2940680, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid).
873010.fig.008
Figure 8: Mean square displacement (MSD) for protein over 5000 ps of MD simulation in Smo protein complexes with LY2940680, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid).
fig9
Figure 9: Variation of (a) total solvent accessible surface area, (b) hydrophobic surface area, and (c) hydrophilic surface area over 5000 ps of MD simulation for Smo protein complexes with LY2940680, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid).
873010.fig.0010
Figure 10: Smallest distance between residue pairs for protein over 5000 ps of MD simulation in Smo protein complexes with LY2940680, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid).

For the MD simulation, the representative structures of each complex under dynamic conditions were identified by the cluster analysis with a RMSD cutoff of 0.105 nm. According to the RMSD values and graphical depiction of the clusters for Smo protein complexes with LY2940680, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid) illustrated in Figure 11, the docking poses of the representative structures for Smo protein complex with LY2940680 and the top three TCM compounds were illustrated in Figure 12. For LY2940680, there exist the stable H-bonds with residues Asn219 and Arg400 under dynamic conditions. In addition, it forms an H-bond with Tyr394 after MD simulation. Precatorine has stable π interactions with residue Phe484 and H-bonds with Lys395. After MD simulation, it forms an H-bond with residue Asn219. Labiatic acid has stable H-bonds with residues Tyr207, Lys395 and forms the H-bonds with residues Asp384, Gln477, and Glu518. The top 3 TCM compounds have stable interactions with residue Phe484 and H-bonds with residue Lys395. Moreover, the interaction with residue Arg400 was changed from π interaction to H-bond and forms the H-bonds with residues Tyr207 and Arg285 after MD simulation.

873010.fig.0011
Figure 11: Root-mean-square deviation value (upper left half) and graphical depiction of the clusters with cutoff 0.105 nm (lower right half) for Smo protein complexes with LY2940680, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid).
fig12
Figure 12: Docking poses of middle RMSD structure in the major cluster for Smo protein complexes with LY2940680, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid).

To analyze the variation of H-bonds for key residues in each protein-ligand complex, the H-bond occupancy for key residues of Smo protein with top three candidates and LY2940680 overall 5000 ps of MD simulation was listed in Table 1, and the distance variations of each H-bond were displayed in Figure 13. They indicate that the H-bonds between LY2940680 and residues Asn219, Tyr394, Arg400 were stabilized over 5000 ps of MD simulation. In addition, the H-bonds between top three TCM compounds and residues mentioned above were also stabilized. Comparing to docking poses between docking simulation (Figure 4) and MD simulation (Figure 12), LY2940680 and the top three TCM compounds maintain most of interactions with Smo protein, which keep the ligand binding stable in the binding domain.

tab1
Table 1: H-bond occupancy for key residues of Smo protein with top three candidates and LY2940680 overall 5000 ps of molecular dynamics simulation.
fig13
Figure 13: Distances of hydrogen bonds with common residues during 5000 ps of MD simulation.

4. Conclusion

This study aims to investigate the potent TCM candidates for Smo protein. The top three TCM compounds, precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid), have displayed higher potent binding affinities than the positive control, LY2940680, in the docking simulation. The docking poses of top three TCM compounds have similar π interaction with residue Phe484 and interaction with common residues Lys395 and Arg400. The MD simulations are employed to optimize the result of docking simulation and validate the stability of H-bonds between each ligand and Smo protein under dynamic conditions. For the MD simulation, the top three TCM compounds maintain most of interactions with Smo protein, which keep the ligand binding stable in the binding domain. Hence, we propose precatorine, labiatic acid, and 2,2′-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid) as potential lead compounds for further study in drug development process with the Smo protein.

Conflict of Interests

The authors declared that there is no conflict of interests.

Authors’ Contribution

Kuan-Chung Chen, Mao-Feng Sun, and Hsin-Yi Chen contributed equally to this work.

Acknowledgments

The research was supported by grants from the National Science Council of Taiwan (NSC102-2325-B039-001 and NSC102-2221-E-468-027-), Asia University (ASIA100-CMU-2, ASIA101-CMU-2, and 102-ASIA-07), China Medical University (CMU99-TC-24, and CMU102-BC-3), and China Medical University Hospital (DMR-102-001, DMR-102-051, DMR-103-058, DMR-103-001, and DMR-103-096). This study is also supported in part by Taiwan Department of Health Clinical Trial and Research Center of Excellence (DOH102-TD-B-111-004), Taiwan Department of Health Cancer Research Center of Excellence (MOHW103-TD-B-111-03), and CMU under the Aim for Top University Plan of the Ministry of Education, Taiwan.

References

  1. K. Jauch-Chara and K. M. Oltmanns, “Obesity—a neuropsychological disease? Systematic review and neuropsychological model,” Progress in Neurobiology, vol. 114, pp. 84–101, 2014. View at Publisher · View at Google Scholar
  2. A. J. Guri, R. Hontecillas, and J. Bassaganya-Riera, “Peroxisome proliferator-activated receptors: bridging metabolic syndrome with molecular nutrition,” Clinical Nutrition, vol. 25, no. 6, pp. 871–885, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Teperino, S. Amann, M. Bayer et al., “Hedgehog partial agonism drives warburg-like metabolism in muscle and brown fat,” Cell, vol. 151, no. 2, pp. 414–426, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Jiang, X. Li, W. Yang et al., “PKM2 regulates chromosome segregation and mitosis progression of tumor cells,” Molecular Cell, vol. 53, no. 1, pp. 75–87, 2014. View at Publisher · View at Google Scholar
  5. Y.-M. Chang, B. K. Velmurugan, W.-W. Kuo et al., “Inhibitory effect of alpinate Oxyphyllae fructus extracts on Ang II-induced cardiac pathological remodeling-related pathways in H9c2 cardiomyoblast cells,” BioMedicine, vol. 3, no. 4, pp. 148–152, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. M. Leung, K. L. Wong, S. W. Chen et al., “Down-regulation of voltage-gated Ca2+ channels in Ca2+ store-depleted rat insulinoma RINm5F cells,” BioMedicine, vol. 3, no. 3, pp. 130–139, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. M. A. Leissring, E. Malito, S. Hedouin et al., “Designed inhibitors of insulin-degrading enzyme regulate the catabolism and activity of insulin,” PLoS ONE, vol. 5, no. 5, Article ID e10504, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. C.-L. Jao, S.-L. Huang, and K.-C. Hsu, “Angiotensin I-converting enzyme inhibitory peptides: inhibition mode, bioavailability, and antihypertensive effects,” BioMedicine, vol. 2, no. 4, pp. 130–136, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. A. P. McMahon, P. W. Ingham, and C. J. Tabin, “Developmental roles and clinical significance of Hedgehog signaling,” Current Topics in Developmental Biology, vol. 53, pp. 1–114, 2003. View at Google Scholar · View at Scopus
  10. S. J. Scales and F. J. de Sauvage, “Mechanisms of Hedgehog pathway activation in cancer and implications for therapy,” Trends in Pharmacological Sciences, vol. 30, no. 6, pp. 303–312, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Sekulic, A. R. Mangold, D. W. Northfelt, and P. M. Lorusso, “Advanced basal cell carcinoma of the skin: targeting the Hedgehog pathway,” Current Opinion in Oncology, vol. 25, no. 3, pp. 218–223, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. R. McMillan and W. Matsui, “Molecular pathways: the hedgehog signaling pathway in cancer,” Clinical Cancer Research, vol. 18, no. 18, pp. 4883–4888, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. V. Chenna, C. Hu, and S. R. Khan, “Synthesis and cytotoxicity studies of Hedgehog enzyme inhibitors SANT-1 and GANT-61 as anticancer agents,” Journal of Environmental Science and Health A: Toxic/Hazardous Substances and Environmental Engineering, vol. 49, no. 6, pp. 641–647, 2014. View at Publisher · View at Google Scholar
  14. D. Amakye, Z. Jagani, and M. Dorsch, “Unraveling the therapeutic potential of the Hedgehog pathway in cancer,” Nature Medicine, vol. 19, no. 11, pp. 1410–1422, 2013. View at Publisher · View at Google Scholar
  15. D. D. Von Hoff, P. M. LoRusso, C. M. Rudin et al., “Inhibition of the hedgehog pathway in advanced basal-cell carcinoma,” The New England Journal of Medicine, vol. 361, no. 12, pp. 1164–1172, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. A. E. Proctor, L. A. Thompson, and C. L. O'Bryant, “Vismodegib: an inhibitor of the hedgehog signaling pathway in the treatment of basal cell carcinoma,” The Annals of Pharmacotherapy, vol. 48, no. 1, pp. 99–106, 2014. View at Publisher · View at Google Scholar
  17. M. Xin, L. Zhang, F. Tang et al., “Design, synthesis, and evaluation of pyrrolo[2,1-f][1,2,4]triazine derivatives as novel hedgehog signaling pathway inhibitors,” Bioorganic & Medicinal Chemistry, vol. 22, no. 4, pp. 1429–1440, 2014. View at Publisher · View at Google Scholar
  18. R. Kunstfeld, “Smoothened inhibitors in the treatment of advanced basal cell carcinomas,” Current Opinion in Oncology, vol. 26, no. 2, pp. 184–195, 2014. View at Publisher · View at Google Scholar
  19. C. Y. Chen, “A novel integrated framework and improved methodology of computer-aided drug design,” Current Topics in Medicinal Chemistry, vol. 13, no. 9, pp. 965–988, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Y. Chen and C. Y. C. Chen, “Insights into designing the dual-targeted HER2/HSP90 inhibitors,” Journal of Molecular Graphics and Modelling, vol. 29, no. 1, pp. 21–31, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. S. C. Yang, S. S. Chang, H. Y. Chen, and C. Y. C. Chen, “Identification of potent EGFR inhibitors from TCM Database@Taiwan,” PLoS Computational Biology, vol. 7, no. 10, Article ID e1002189, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. A. Tsou, K. C. Chen, H. C. Lin, S. S. Chang, and C. Y. C. Chen, “Uroporphyrinogen decarboxylase as a potential target for specific components of Traditional Chinese Medicine: a virtual screening and molecular dynamics study,” PLoS ONE, vol. 7, no. 11, Article ID e50087, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. A. Tsou, K. C. Chen, S. S. Chang, Y. R. Wen, and C. Y. Chen, “A possible strategy against head and neck cancer: in silico investigation of three-in-one inhibitors,” Journal of Biomolecular Structure and Dynamics, vol. 31, no. 12, pp. 1358–1369, 2013. View at Publisher · View at Google Scholar
  24. K. C. Chen, S. S. Chang, F. J. Tsai, and C. Y. Chen, “Han ethnicity-specific type 2 diabetic treatment from Traditional Chinese Medicine?” Journal of Biomolecular Structure and Dynamics, vol. 31, no. 11, pp. 1219–1235, 2013. View at Publisher · View at Google Scholar
  25. K. C. Chen, M. F. Sun, S. C. Yang et al., “Investigation into potent inflammation inhibitors from Traditional Chinese Medicine,” Chemical Biology and Drug Design, vol. 78, no. 4, pp. 679–688, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. S. S. Chang, H. J. Huang, and C. Y. Chen, “Two birds with one stone? Possible dual-targeting H1N1 inhibitors from Traditional Chinese Medicine,” PLoS Computational Biology, vol. 7, no. 12, Article ID e1002315, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. H. J. Huang, K. J. Lee, H. W. Yu, H. Chen, F. Tsai, and C. Y. Chen, “A novel strategy for designing the selective PPAR agonist by the “sum of activity” model,” Journal of Biomolecular Structure and Dynamics, vol. 28, no. 2, pp. 187–200, 2010. View at Google Scholar · View at Scopus
  28. K. C. Chen, S. S. Chang, H. J. Huang, T. Lin, Y. Wu, and C. Y. Chen, “Three-in-one agonists for PPAR-α, PPAR-γ, and PPAR-δ from Traditional Chinese Medicine,” Journal of Biomolecular Structure and Dynamics, vol. 30, no. 6, pp. 662–683, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. K.-C. Chen and C. Y. C. Chen, “Stroke prevention by Traditional Chinese Medicine? A genetic algorithm, support vector machine and molecular dynamics approach,” Soft Matter, vol. 7, no. 8, pp. 4001–4008, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. K. C. Chen, K. W. Chang, H. Y. Chen, and C. Y. C. Chen, “Traditional Chinese Medicine, a solution for reducing dual stroke risk factors at once?” Molecular BioSystems, vol. 7, no. 9, pp. 2711–2719, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. T. T. Chang, K. C. Chen, K. W. Chang et al., “In silico pharmacology suggests ginger extracts may reduce stroke risks,” Molecular BioSystems, vol. 7, no. 9, pp. 2702–2710, 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. W. Ieongtou, S. S. Chang, D. Wu et al., “Molecular level activation insights from a NR2A/NR2B agonist,” Journal of Biomolecular Structure and Dynamics, vol. 32, no. 5, pp. 683–693, 2014. View at Publisher · View at Google Scholar
  33. H. J. Huang, Y. R. Jian, and C. Y. Chen, “Traditional Chinese Medicine application in HIV: an in silico study,” Journal of Biomolecular Structure and Dynamics, vol. 32, no. 1, pp. 1–12, 2014. View at Publisher · View at Google Scholar
  34. W. I. Tou, S. S. Chang, C. C. Lee, and C. Y. Chen, “Drug design for neuropathic pain regulation from Traditional Chinese Medicine,” Scientific Reports, vol. 3, p. 844, 2013. View at Google Scholar · View at Scopus
  35. K. C. Chen, Y. R. Jian, M. F. Sun, T. T. Chang, C. C. Lee, and C. Y. Chen, “Investigation of silent information regulator 1 (Sirt1) agonists from Traditional Chinese Medicine,” Journal of Biomolecular Structure and Dynamics, vol. 31, no. 11, pp. 1207–1218, 2013. View at Publisher · View at Google Scholar
  36. C. Y. C. Chen, “TCM Database@Taiwan: the world's largest Traditional Chinese Medicine database for drug screening in silico,” PLoS ONE, vol. 6, no. 1, Article ID e15939, 2011. View at Publisher · View at Google Scholar · View at Scopus
  37. W. I. Tou and C. Y. Chen, “May disordered protein cause serious drug side effect?” Drug Discovery Today, vol. 19, no. 4, pp. 367–372, 2014. View at Publisher · View at Google Scholar
  38. C. Y. C. Chen and W. I. Tou, “How to design a drug for the disordered proteins?” Drug Discovery Today, vol. 18, no. 19-20, pp. 910–915, 2013. View at Publisher · View at Google Scholar · View at Scopus
  39. C. Wang, H. Wu, V. Katritch et al., “Structure of the human smoothened receptor bound to an antitumour agent,” Nature, vol. 497, no. 7449, pp. 338–343, 2013. View at Publisher · View at Google Scholar · View at Scopus
  40. B. Xue, R. L. Dunbrack, R. W. Williams, A. K. Dunker, and V. N. Uversky, “PONDR-FIT: a meta-predictor of intrinsically disordered amino acids,” Biochimica et Biophysica Acta—Proteins and Proteomics, vol. 1804, no. 4, pp. 996–1010, 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan, and M. Karplus, “CHARMM: a program for macromolecular energy minimization and dynamics calculations,” Journal of Computational Chemistry, vol. 4, no. 2, pp. 187–217, 1983. View at Publisher · View at Google Scholar
  42. C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, “Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings,” Advanced Drug Delivery Reviews, vol. 46, no. 1–3, pp. 3–26, 2001. View at Publisher · View at Google Scholar · View at Scopus
  43. C. M. Venkatachalam, X. Jiang, T. Oldfield, and M. Waldman, “LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites,” Journal of Molecular Graphics and Modelling, vol. 21, no. 4, pp. 289–307, 2003. View at Publisher · View at Google Scholar · View at Scopus
  44. B. Hess, C. Kutzner, D. van der Spoel, and E. Lindahl, “GRGMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation,” Journal of Chemical Theory and Computation, vol. 4, no. 3, pp. 435–447, 2008. View at Publisher · View at Google Scholar · View at Scopus
  45. V. Zoete, M. A. Cuendet, A. Grosdidier, and O. Michielin, “SwissParam: a fast force field generation tool for small organic molecules,” Journal of Computational Chemistry, vol. 32, no. 11, pp. 2359–2368, 2011. View at Publisher · View at Google Scholar · View at Scopus
  46. R. Fletcher, Optimization, Academic Press, New York, NY, USA, 1969.