<i>In Silico</i> Investigation of Potential PARP-1 Inhibitors from Traditional Chinese Medicine

Chen, Kuan-Chung; Sun, Mao-Feng; Chen, Calvin Yu-Chian

doi:https://doi.org/10.1155/2014/917605

Evidence-Based Complementary and Alternative Medicine

On this page

Abstract Introduction Materials and Methods Results and Discussion Conclusion Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2014 | Article ID 917605 | https://doi.org/10.1155/2014/917605

In Silico Investigation of Potential PARP-1 Inhibitors from Traditional Chinese Medicine

Kuan-Chung Chen,¹Mao-Feng Sun,²and Calvin Yu-Chian Chen^3,4

Academic Editor: Fuu-Jen Tsai

Received22 Nov 2013

Revised03 Jan 2014

Accepted03 Jan 2014

Published30 Apr 2014

Abstract

Poly(ADP-ribose) polymerases (PARPs) are nuclear enzymes which catalyze the poly-ADP-ribosylation involved in gene transcription, DNA damage repair, and cell-death signaling. As PARP-1 protein contains a DNA-binding domain, which can bind to DNA strand breaks and repair the damaged DNA over a low basal level, the inhibitors of poly(ADP-ribose) polymerase 1 (PARP-1) have been indicated as the agents treated for cancer. This study employed the compounds from TCM Database@Taiwan to identify the potential PARP-1 inhibitors from the vast repertoire of TCM compounds. The binding affinities of the potential TCM compounds were also predicted utilized several distinct scoring functions. Molecular dynamics simulations were performed to optimize the result of docking simulation and analyze the stability of interactions between protein and ligand. The top TCM candidates, isopraeroside IV, picrasidine M, and aurantiamide acetate, had higher potent binding affinities than control, A927929. They have stable H-bonds with residues Gly202 and, Ser243 as A927929 and stable H-bonds with residues Asp105, Tyr228, and His248 in the other side of the binding domain, which may strengthen and stabilize ligand inside the binding domain of PARP-1 protein. Hence, we propose isopraeroside IV and aurantiamide acetate as potential lead compounds for further study in drug development process with the PARP-1 protein.

1. Introduction

Poly(ADP-ribose) polymerases (PARPs) are nuclear enzymes which catalyze the poly-ADP-ribosylation to combine one or more ADP-ribose moieties from intracellular nicotinamide adenine dinucleotide (NAD⁺) covalently with target proteins [1–3]. The poly-ADP-ribosylation is commonly involved in gene transcription, DNA damage repair, and cell-death signaling [4–6].

There are six domains in the structure of poly(ADP-ribose) polymerase 1 (PARP-1) protein elucidated by recent structural studies. Two of three zinc-binding domains have the function to detect and bind to DNA breaks and the third zinc-binding domain coordinates DNA-dependent enzyme activation [7]. The automodification domain serves as acceptors of ADP-ribose moieties, which allow PARP-1 protein mediated poly-ADP-ribosylation to itself, and contains a BRCA1 C-terminus repeat motif [8–10]. The C-terminal catalytic domain catalyzes the poly-ADP-ribosylation to combine one or more ADP-ribose moieties from intracellular nicotinamide adenine dinucleotide (NAD⁺) covalently with target proteins [11–13]. As PARP-1 protein contains a DNA-binding domain, which can bind to DNA strand breaks and repair the damaged DNA over a low basal level, the inhibitors of poly(ADR-ribose) polymerase 1 (PARP-1) have been indicated as the agents treated for cancer [14–17].

Nowadays, the researchers devote to determining the mechanism of diseases and detecting the useful target protein against the diseases [18–24]. In previous researches, it was proven that many compounds extracted from traditional Chinese medicine (TCM) can be recognized as potential lead compounds treated for viral infection [25–28], stroke prevention [29–31], cancers [32–35], and metabolic syndrome [36–38]. To improve drug development from TCM compounds, this study employed the compounds from TCM Database@Taiwan for virtual screening to identify the potential PARP-1 inhibitors from the vast repertoire of TCM compounds. As the structural disorders of protein may cause the side-effect or affect the ligand binding [39, 40], the prediction of disordered amino acids of PARP-1 protein was performed before docking simulation. In docking simulation, distinct scoring functions had been created to predict the binding affinities in different measure methods, such as LigScore considering the Van der Waals interaction and buried polar surface area, piecewise linear potential (PLP), and potential of mean force (PMF) measuring the pairwise interactions of hydrogen bond (H-bond) and steric interaction. We identify the potential TCM compounds in docking simulation utilizing those scoring functions and dock score, which evaluated the docking poses by interaction energy. Moreover, the molecular dynamics (MD) simulations were performed to optimize the result of docking simulation and analyze the stability of interactions between protein and ligand under dynamic conditions.

2. Materials and Methods

2.1. Data Collection

The X-ray crystallography structure of human poly(ADP-ribose) polymerase 1 (PARP-1) with A927929 was obtained from RCSB protein data bank with PDB ID: 3L3 M [41]. The crystal structure of PPAR protein was prepared by prepare protein module in Discovery Studio 2.5 (DS2.5) to remove crystal water, protonate the structure of protein, and employ chemistry at HARvard macromolecular mechanics (CHARMM) force field [42]. The binding site of PARP-1 protein was defined by the volume and location of the cocrystallized compound, A927929. A total of 9,029 nonduplicate TCM compounds from TCM Database@Taiwan [43] were filtered by Lipinski’s rule of five [44] and protonate the structure by prepare ligand module in DS2.5. The prediction of disordered amino acids of PARP-1 protein was performed by PONDR-Fit [45].

2.2. Docking Simulation

The TCM compounds were virtually screened by LigandFit protocol [46] in DS 2.5 to dock compounds into binding site using Monte-Carlo ligand conformation generation and a shape-based initial docking. The suitable docking poses were then optionally minimized with CHARMM force field [42], and a set of scoring functions were evaluated by LigandFit protocol [46] in DS 2.5.

2.3. Molecular Dynamics Simulation

The molecular dynamics (MD) simulations are performed by Gromacs [47]. The PARP-1 protein was reprepared with charmm27 force field by Gromacs. The topology and parameters of each ligand for use with Gromacs were provided by SwissParam program [48]. The whole system involves a cubic box with a minimum distance of 1.2 Å from the protein-ligand complex was solvated by a water model of TIP3P. At the beginning of MD simulation, an energy minimization was performed using steepest descent algorithm [49] with a maximum of 5,000 steps and followed by a single 10 ps constant temperature (NVT ensemble) equilibration performed using Berendsen weak thermal coupling method. The total of 40 ns production simulation was performed under the particle mesh Ewald (PME) option with a time step of 2 fs. The 40 ns MD trajectories were analyzed by the protocols in Gromacs.

3. Results and Discussion

3.1. Disordered Protein Prediction

The disordered amino acids of PARP-1 protein were predicted by PONDR-Fit with the protein sequence from Swiss-Prot (UniProtKB: P09874). Figure 1 displays the result of disordered amino acids prediction and the sequence alignment. It indicates that the residues in the binding domain do not deposit in the disordered region. The binding domain of PARP-1 protein may have a stable structure in protein folding. Most residues in the binding domain were close to the local lowest regions of disordered disposition.

3.2. Docking Simulation

After virtual screening, the top TCM compounds ranked by dock score [46] and control, A927929, are listed in Table 1 with the results of three scoring functions, LigScore2 Dreiding [50], -PLP1 [51], -PLP2 [52], and -PMF [53].

LigScore2 Dreiding is a scoring function calculated by three descriptors as equation as follows: where is a softened Lennard-Jones 6–9 potential in units of kcal/mol. C+_pol shows the buried polar surface area between protein and ligand in units of Å². BuryPol² is the squared sum of the buried polar surface area between protein and ligand in units of Å².

-PLP1, -PLP2, and -PMF are calculated by summing pairwise interaction, which are hydrogen bond (H-bond) and steric interaction, between protein and ligand. Higher scores indicate stronger protein-ligand binding affinities.

The scoring functions indicate that the top TCM compounds have higher binding affinities than A927929. The resources of three TCM compounds are also listed in Table 1. Isopraeroside IV is extracted from root of Angelica dahurica. Picrasidine M is extracted from bark of Picrasma quassioides (D.Don) Benn. Aurantiamide acetate is extracted from plant of Artemisia annua L. The chemical scaffolds of A927929 and top three TCM compounds are shown in Figure 2. The docking poses of A927929 and top TCM compounds in PARP-1 protein are illustrated in Figure 3. A927929 has H-bonds with two key residues Gly202 and Ser243, which restricted ligand in the binding domain. The TCM compounds, isopraeroside IV and aurantiamide acetate, have H-bonds with two key residues Gly202 and Ser243 as A927929. Moreover, aurantiamide acetate also has an H-bond with residue Gly227. Picrasidine M has H-bonds with another three residues Asp105, Tyr228, and Tyr246 to restricted ligand in the binding domain of PARP-1 protein.

3.3. Molecular Dynamics Simulation

The molecular dynamics (MD) simulations were performed to analyze the stability of interactions between protein and ligand under dynamic conditions. Figure 4 illustrates the root-mean-square deviations (RMSDs) and total energies for PARP-1 protein complexes with A927929, isopraeroside IV, picrasidine M, and aurantiamide acetate over 40 ns MD simulation. RMSDs were calculated to study atomic fluctuations for each protein and ligand during MD simulation. The Cα RMSDs and ligand RMSDs indicate that each complex tends to stabilize after 31 ns of MD simulation. Moreover, Figure 4 also indicates that the PARP-1 complexes with the top three TCM compounds have similar total energies as the PARP-1 complex with A927929 under dynamic conditions. Distance matrices consisting of the smallest distance between residue pairs for each protein-ligand complex are shown in Figure 5. Those matrices display that the influence of the top three TCM compounds on the structure of PARP-1 protein is similar to A927929. Figure 6 shows the secondary structure changes for each complex during MD simulation, respectively. The secondary structure changes indicate that the top three TCM compounds did not cause significant differences from the control. The secondary structural feature ratio variations indicate that each protein-ligand complex has approximately 33% of α-helix and 21% of β-sheet during MD simulation. In Figure 7, it illustrates the RMSD values and graphical depiction of the clusters with cutoff of 0.105 nm over 40 ns MD simulation. The RMSD values between MD trajectories indicate that the PARP-1 protein complexes tend to stabilize after MD simulation. After the complexes tend to stabilize under dynamic conditions, the representative structures of each protein-ligand complex after MD simulation were identified by middle RMSD structure in the major cluster. Docking poses of middle RMSD structure in the major cluster for PARP-1 protein complexes with A927929 (39.32 ns), isopraeroside IV (38.42 ns), picrasidine M (31.22 ns), and aurantiamide acetate (38.44 ns) are illustrated in Figure 8. It indicates that A927929 has a similar docking pose as docking simulation and maintains the H-bonds with two key residues Gly202 and Ser243 after MD simulation. For three TCM compounds, isopraeroside IV keeps the H-bonds with two key residues Gly202 and Ser243 under dynamic conditions. Moreover, isopraeroside IV has H-bonds with the other two residues Asp105 and His248 after MD simulation. Picrasidine M maintains the H-bond with residue Tyr228 under dynamic conditions and shifts an H-bond from residue Tyr246 to residue Lys242. In addition, picrasidine M loses the H-bond with residue Asp105 after MD simulation. Aurantiamide acetate maintains the H-bonds with two key residues Gly202 and Ser243 under dynamic conditions and has an H-bond with residue Tyr228 after MD simulation.

Docking poses of middle RMSD structure in the major cluster for PARP-1 protein complexes indicate that all compounds except picrasidine M have stable H-bonds with two key residues Gly202 and Ser243. Picrasidine M and aurantiamide acetate have an H-bond with residue Tyr228. Isopraeroside IV has H-bonds with the other two residues Asp105 and His248 after MD simulation.

The occupancies of H-bonds for key residues of PARP-1 protein are listed in Table 2, and the fluctuation of distances for H-bonds with common residues of PARP-1 protein is shown in Figure 9. The H-bonds occupancies and distances fluctuation over MD simulation displays the stable H-bonds between ligands, A927929, isopraeroside IV, aurantiamide acetate, and residues Gly202 and Ser243. In addition, picrasidine M has stable H-bonds with residue Tyr228. For A927929, although the H-bond occupancy with residue His201 over 40 ns of MD simulation is 58%, the distance variation of H-bond shown in Figure 9 indicates that this H-bond was lost at the end of the MD simulation. For isopraeroside IV, the H-bonds with residues Asp105 and His248 are tended to stabilize after MD simulation. Aurantiamide acetate also has a stable H-bond with residue Tyr228 after 25 ns of MD simulation. For picrasidine M, the H-bond with residue Tyr246 in the docking simulation has shifted to binding with residue Lys242 after MD simulation, and it has another H-bond with residue Tyr246 under dynamic conditions.

(a)

(b)

(c)

(d)

The top TCM compounds, isopraeroside IV and aurantiamide acetate, have stable H-bonds with residues Gly202 and Ser243 as A927929. In addition, isopraeroside IV also has stable H-bonds with residues Asp105 and His248, which stabilized the docking pose of ligand in the binding domain. Aurantiamide acetate has another stable H-bond with residue Tyr228 similar to picrasidine M. For picrasidine M, it forms the stable H-bond with residue Lys242 instead of residues Gly202 and Ser243.

4. Conclusion

In this study, we aim to investigate the potent TCM candidates for PARP-1 protein. The top TCM candidates, isopraeroside IV, picrasidine M, and aurantiamide acetate had higher potent binding affinities than control, A927929, in the docking simulation. Both isopraeroside IV and aurantiamide acetate had H-bond with residues Gly202 and Ser243 as A927929. The MD simulations were performed to optimize the result of docking simulation and validate the stability of H-bonds between each ligand and PARP-1 protein under dynamic conditions. Isopraeroside IV and aurantiamide acetate have stable H-bonds with residues Gly202 and Ser243 as A927929 under dynamic conditions. Moreover, they had stable H-bonds with residues Asp105, Tyr228, and His248 in the other side of the binding domain, which may strengthen and stabilize ligand inside the binding domain of PARP-1 protein. Hence, we propose isopraeroside IV and aurantiamide acetate as potential lead compounds for further study in drug development process with the PARP-1 protein.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors’ Contribution

Kuan-Chung Chen and Mao-Feng Sun are equally contributed.

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 and ASIA101-CMU-2, 102-ASIA-07), and China Medical University Hospital (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) and Taiwan Department of Health Cancer Research Center of Excellence (MOHW103-TD-B-111-03).

References

D. D'Amours, S. Desnoyers, I. D'Silva, and G. G. Poirier, “Poly(ADP-ribosyl)ation reactions in the regulation of nuclear functions,” The Biochemical Journal, vol. 342, part 2, pp. 249–268, 1999.
View at: Publisher Site | Google Scholar
P. Bai and C. Canto, “The role of PARP-1 and PARP-2 enzymes in metabolic regulation and disease,” Cell Metabolism, vol. 16, no. 3, pp. 290–295, 2012.
View at: Google Scholar
P. O. Hassa and M. O. Hottiger, “The diverse biological roles of mammalian PARPs, a small but powerful family of poly-ADP-ribose polymerases,” Frontiers in Bioscience, vol. 13, no. 8, pp. 3046–3082, 2008.
View at: Publisher Site | Google Scholar
R. Krishnakumar and W. L. Kraus, “The PARP side of the nucleus: molecular actions, physiological outcomes, and clinical targets,” Molecular Cell, vol. 39, no. 1, pp. 8–24, 2010.
View at: Publisher Site | Google Scholar
M. F. Langelier and J. M. Pascal, “PARP-1 mechanism for coupling DNA damage detection to poly(ADP-ribose) synthesis,” Current Opinion in Structural Biology, vol. 23, no. 1, pp. 134–143, 2013.
View at: Google Scholar
N. McCabe, N. C. Turner, C. J. Lord et al., “Deficiency in the repair of DNA damage by homologous recombination and sensitivity to poly(ADP-ribose) polymerase inhibition,” Cancer Research, vol. 66, no. 16, pp. 8109–8115, 2006.
View at: Publisher Site | Google Scholar
M.-F. Langelier, K. M. Servent, E. E. Rogers, and J. M. Pascal, “A third zinc-binding domain of human poly(ADP-ribose) polymerase-1 coordinates DNA-dependent enzyme activation (Journal of Biological Chemistry (2008) 283, (4105–4114)),” The Journal of Biological Chemistry, vol. 283, no. 33, p. 22884, 2008.
View at: Google Scholar
M. Altmeyer, S. Messner, P. O. Hassa, M. Fey, and M. O. Hottiger, “Molecular mechanism of poly(ADP-ribosyl)ation by PARP1 and identification of lysine residues as ADP-ribose acceptor sites,” Nucleic Acids Research, vol. 37, no. 11, pp. 3723–3738, 2009.
View at: Publisher Site | Google Scholar
Z. H. Tao, P. Gao, and H.-W. Liu, “Identification of the ADP-ribosylation sites in the PARP-1 automodification domain: analysis and implications,” Journal of the American Chemical Society, vol. 131, no. 40, pp. 14258–14260, 2009.
View at: Publisher Site | Google Scholar
M. F. Langelier, J. L. Planck, S. Roy, and J. M. Pascal, “Structural basis for DNA damage-dependent poly(ADP-ribosyl)ation by human PARP-1,” Science, vol. 336, no. 6082, pp. 728–732, 2012.
View at: Google Scholar
A. Ruf, G. de Murcia, and G. E. Schulz, “Inhibitor and NAD+ binding to poly(ADP-ribose) polymerase as derived from crystal structures and homology modeling,” Biochemistry, vol. 37, no. 11, pp. 3893–3900, 1998.
View at: Publisher Site | Google Scholar
A. Ruf, J. M. de Murcia, G. M. de Murcia, and G. E. Schulz, “Structure of the catalytic fragment of poly(ADP-ribose) polymerase from chicken,” Proceedings of the National Academy of Sciences of the United States of America, vol. 93, no. 15, pp. 7481–7485, 1996.
View at: Publisher Site | Google Scholar
I. Kameshita, Z. Matsuda, T. Taniguchi, and Y. Shizuta, “Poly(ADP-ribose) synthetase. Separation and identification of three proteolytic fragments as the substrate-binding domain, the DNA-binding domain, and the automodification domain,” The Journal of Biological Chemistry, vol. 259, no. 8, pp. 4770–4776, 1984.
View at: Google Scholar
D. R. Spigel, “PARP inhibitors in lung cancer,” Journal of Thoracic Oncology, vol. 7, no. 16, supplement 5, pp. S392–S393, 2012.
View at: Google Scholar
R. E. Reinbolt and J. L. Hays, “The role of PARP inhibitors in the treatment of gynecologic malignancies,” Frontiers in Oncology, vol. 3, p. 237, 2013.
View at: Google Scholar
H. Farmer, H. McCabe, C. J. Lord et al., “Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy,” Nature, vol. 434, no. 7035, pp. 917–921, 2005.
View at: Publisher Site | Google Scholar
P. C. Fong, D. S. Boss, T. A. Yap et al., “Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers,” The New England Journal of Medicine, vol. 361, no. 2, pp. 123–134, 2009.
View at: Publisher Site | Google Scholar
D.-Y. Lin, F.-J. Tsai, C.-H. Tsai, and C.-Y. Huang, “Mechanisms governing the protective effect of 17β-estradiol and estrogen receptors against cardiomyocyte injury,” Biomedicine, vol. 1, no. 1, pp. 21–28, 2011.
View at: Publisher Site | Google Scholar
W.-L. Liao and F.-J. Tsai, “Personalized medicine: a paradigm shift in healthcare,” BioMedicine, vol. 3, no. 2, pp. 66–72, 2013.
View at: Google Scholar
C.-C. Lee, C.-H. Tsai, L. Wan et al., “Increased incidence of Parkinsonism among Chinese with β-glucosidase mutation in central Taiwan,” BioMedicine, vol. 3, no. 2, pp. 92–94, 2013.
View at: Google Scholar
C.-H. Wang, W.-D. Lin, D.-T. Bau et al., “Appearance of acanthosis nigricans may precede obesity: an involvement of the insulin/IGF receptor signaling pathway,” BioMedicine, vol. 3, no. 2, pp. 82–87, 2013.
View at: Google Scholar
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: Google Scholar
I. C. Chou, W.-D. Lin, C.-H. Wang et al., “Association analysis between Tourette's syndrome and two dopamine genes (DAT1, DBH) in Taiwanese children,” BioMedicine, vol. 3, no. 2, pp. 88–91, 2013.
View at: Google Scholar
W. Y. Lin, H. P. Liu, J. S. Chang et al., “Genetic variations within the PSORS1 region affect Kawasaki disease development and coronary artery aneurysm formation,” BioMedicine, vol. 3, no. 2, pp. 73–81, 2013.
View at: Google Scholar
C.-Y. Chen, Y.-H. Chang, D.-T. Bau et al., “Ligand-based dual target drug design for H1N1: swine flu—a preliminary first study,” Journal of Biomolecular Structure and Dynamics, vol. 27, no. 2, pp. 171–178, 2009.
View at: Google Scholar
S.-S. Chang, H.-J. Huang, and C. Y.-C. 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 Site | Google Scholar
T.-T. Chang, M.-F. Sun, H.-Y. Chen et al., “Screening from the world's largest TCM database against H1N1 virus,” Journal of Biomolecular Structure and Dynamics, vol. 28, no. 5, pp. 773–786, 2011.
View at: Google Scholar
C.-H. Lin, T.-T. Chang, M.-F. Sun et al., “Potent inhibitor design against H1N1 swine influenza: structure-based and molecular dynamics analysis for M2 inhibitors from traditional Chinese medicine database,” Journal of Biomolecular Structure and Dynamics, vol. 28, no. 4, pp. 471–482, 2011.
View at: Google Scholar
K.-C. Chen and C. Yu-Chian 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 Site | Google Scholar
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 Site | Google Scholar
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 Site | Google Scholar
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: Google Scholar
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 Site | Google Scholar
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 Site | Google Scholar
H.-J. Huang, K.-J. Lee, H. W. Yu et al., “Structure-based and ligand-based drug design for HER 2 receptor,” Journal of Biomolecular Structure and Dynamics, vol. 28, no. 1, pp. 23–37, 2010.
View at: Google Scholar
H.-J. Huang, K.-J. Lee, H. W. Yu, H.-Y. Chen, F.-J. Tsai, and C. Y.-C. 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
K. C. Chen, S. S. Chang, H. J. Huang et al., “Three-in-one agonists for PPAR-alpha, PPAR-gamma, and PPAR-delta from traditional Chinese medicine,” Journal of Biomolecular Structure & Dynamics, vol. 30, no. 6, pp. 662–683, 2012.
View at: Google Scholar
P.-C. Chang, J.-D. Wang, M.-M. Lee et al., “Lose weight with traditional Chinese medicine? Potential suppression of fat mass and obesity-associated protein,” Journal of Biomolecular Structure and Dynamics, vol. 29, no. 3, pp. 471–483, 2011.
View at: Google Scholar
W. I. Tou and C. Y. Chen, “May disordered protein cause serious drug side effect?” Drug Discovery Today, 2013.
View at: Publisher Site | Google Scholar
C. Y. 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: Google Scholar
T. D. Penning, G.-D. Zhu, J. Gong et al., “Optimization of phenyl-substituted benzimidazole carboxamide poly(ADP-ribose) polymerase inhibitors: identification of (S)-2-(2-fluoro-4- (pyrrolidin-2-yl)phenyl)-1 H -benzimidazole-4-carboxamide (A-966492), a highly potent and efficacious inhibitor,” Journal of Medicinal Chemistry, vol. 53, no. 8, pp. 3142–3153, 2010.
View at: Publisher Site | Google Scholar
B. R. Brooks, R. E. Bruccoleri, B. D. Olafson et al., “CHARMM: a program for macromolecular energy minimization and dynamics calculations,” Journal of Computational Chemistry, vol. 4, pp. 187–217, 1983.
View at: Google Scholar
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 Site | Google Scholar
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 Site | Google Scholar
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 Site | Google Scholar
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 Site | Google Scholar
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 Site | Google Scholar
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 Site | Google Scholar
R. Fletcher, Optimization, Academic Press, New York, NY, USA, 1969.
S. L. Mayo, B. D. Olafson, and W. A. Goddard III, “DREIDING: a generic force field for molecular simulations,” Journal of Physical Chemistry, vol. 94, no. 26, pp. 8897–8909, 1990.
View at: Google Scholar
D. K. Gehlhaar, G. M. Verkhivker, P. A. Rejto et al., “Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming,” Chemistry and Biology, vol. 2, no. 5, pp. 317–324, 1995.
View at: Publisher Site | Google Scholar
K. Gehlhaar Daniel, D. Bouzida, and A. Rejto Paul, “Reduced dimensionality in ligand? Protein structure prediction: covalent inhibitors of serine proteases and design of site-directed combinatorial libraries,” in Rational Drug Design, pp. 292–311, American Chemical Society, 1999.
View at: Google Scholar
I. Muegge and Y. C. Martin, “A general and fast scoring function for protein-ligand interactions: a simplified potential approach,” Journal of Medicinal Chemistry, vol. 42, no. 5, pp. 791–804, 1999.
View at: Publisher Site | Google Scholar

Copyright

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.

PDF Download Citation

Download other formats

Order printed copies

Views

2063

Downloads

1541

Citations