臺灣博碩士論文加值系統

肺癌的死亡率占所有癌症死亡率中的第一位，以非小細胞肺癌為主，
因為發現不易，且發現時已接近晚期，治療不易，傳統上是以不具選擇性
的癌症化療藥物來治療，但副作用大，治療的成效也不理想。表皮生長因
子受體在很多固癌裡有過度表現的情形，而在非小細胞肺癌的病人中也有
40～80﹪會過度表現，因此被視為一藥物設計的重要標的。在2003 年，
美國的FDA 通過了第一個以表皮細胞生長因子受體蛋白酪氨酸激酶抑制
劑，可對體細胞的突變（取代白氨酸為精氨酸）L858R，在亞洲地區病人，
女性，不吸癌者有顯著良好的治療結果，然而此類藥物在施予幾個月後病
人皆會發展出抗藥性。鑒於新型藥物開發的迫切性，我們的目標是運用高
計算效率的藥效基團比對，對六百萬化合物資料庫進行篩選，以尋找不同
化學骨架的L858R 抑制劑。

Lung cancer is the first mortality rate of all carcinoma. The major part is Non--‐Small--‐Cell Lung Cancer(NSC--‐LC). The curative effect is not ideal for not easily diagnosis and almost in the late state when the tumor
was discovered. Pharmacotherapy of lung cancer traditionally is by chemotherapy, but the adverse effect is serious and the curative effect is
not ideal. Epidermal growth factor receptor (EGFR), is over--‐expresed in
many solid tumor including the non--‐small--‐cell lung cancer (NSC--‐LC) (40~80%). In 2003, the first epidermal growth factor receptor
tyrosine kinase inhibitor (gefitinib) was approved by food and drug administration (FDA) in America. Dramatic therapeutic effect was discovered in Asia patient, non--‐smoker and women and is highly related to EGFR L858R (substitution of leucine 858 to arginine). However,
drug resistance is occurred in several months after administration of this
kind ofdrug.

For the imperious demand of the new drug development, our goal is to
discovery different scaffold EGFR L858R inhibitor (other than 4-anilinoquinazoline of gefitinib) by using the high performance pharmacophore based virtual screening to six million chemical database.

Table of Contents
口試委員會審定書………………………………………….....……………………….v
誌謝……………………………………………………………..……………………....vi
Figure list……………………………………………………………………………..vii
Table list…………………………………………………………………………….…ix
中文摘要…………………………………………………………...………………...…xi
Abstract…………………………………………………………….…………………xii

Chapter 1:
Introduction
1.1 The Role of EGFR in Non--‐Small Cell Lung Cancer……..1
1.2 Pharmacotherapy of Non--‐Small Cell Lung Cancer……….3
1.3 Pharmacophore Method in Drug Discovery……………..……5
1.4 Docking Theory and Virtual Screening..................7
1.5 Autodock with Robust Scoring Function……………………11
1.6 Solubility Prediction…………………………………………13
1.7 Ligand Efficiency………………………………………………14
1.8 Ligand Binding Assay……………………………………………15

Chapter 2:
Material and Methods

2.1
Pharmacophore Method …………………………………………………………..19
2.1.1 Receptor--‐based pharmacophore……………………………………..19
2.1.2
Generation of three--‐dimensional chemical database………………...…22
2.2
AutoDock with Robust Scoring Function
2.2.1
Preparation of ligand files for docking……………………………..……....…23
2.2.2
Preparation of receptor files for docking…………………………………......23
2.2.3
Preparation of grid parameter files………………………………………….....24
2.2.4
Preparation of docking parameter files………………………………….....….25
2.3
Prediction of Solubility of Candidates from Virtual Screening………...….26
2.4
Molecular Dynamic Simulations
2.4.1
Preparation of coordinate files and topology files…………………......…..27
2.4.2
Minimization…………………………………………………………………….…..28
2.4.3
Equilibration…………………………………………………………………….…...29
2.4.4
Productionrun………………………………………………………………………..30
2.5
Fluorescence Assay………........................…………………………………….31

Chapter 3: Results and Discussion
3.1
Receptor--‐Based Pharmacophore……………………….…………………….32
3.1.1
Pharmacophore from 2ITZ (EGFR L858R/Gefitinib)…….........……....…….32
3.1.2
Pharmacophore from 2ITT (EGFR L858R/AEE‐788)……....…….......…..…34
3.1.3
Pharmacophore from 2ITU (EGFR L858R/AFN-741)…….........……..…….35
3.1.4
Pharmacophore from 2ITV (EGFR L858R/AMP‐PNP)……....……..….........37

3.2
Common Candidate Set from Pharmacophore-Based Screening….....….43
3.2.1
The result of screened candidate and pharmacophore modification…………………………………………………………………….......38
3.2.2
The result of candidate intersection………………………………….......……47
3.3
Rescore and Re--‐Dock with AutoDock4 Robust Scoring Function……..48
3.4
Validation of AutoDock Results by Molecular Dynamic Simulation.........66
3.5
Results of Fluorescence Assay……………………………………….....……….74

Chapter 4: Discussion and Conclusion…………………………………………77

Reference
1. Herbst, R. S.; Heymach, J. V.; Lippman, S. M., Lung cancer. The New
England journal of medicine 2008, 359 (13), 1367-80.
2. Ciardiello, F.; Tortora, G., EGFR antagonists in cancer treatment. The
New England journal of medicine 2008, 358 (11), 1160-74.
3. Hirsch, F. R.; Varella-Garcia, M.; Bunn, P. A., Jr.; Di Maria, M. V.; Veve,
R.; Bremmes, R. M.; Baron, A. E.; Zeng, C.; Franklin, W. A., Epidermal
growth factor receptor in non-small-cell lung carcinomas: correlation between
gene copy number and protein expression and impact on prognosis. Journal
of clinical oncology : official journal of the American Society of Clinical
Oncology 2003, 21 (20), 3798-807.
4. Muhsin, M.; Graham, J.; Kirkpatrick, P., Gefitinib. Nature reviews. Drug
discovery 2003, 2 (7), 515-6.
5. (a) Lynch, T. J.; Bell, D. W.; Sordella, R.; Gurubhagavatula, S.; Okimoto,
R. A.; Brannigan, B. W.; Harris, P. L.; Haserlat, S. M.; Supko, J. G.; Haluska,
F. G.; Louis, D. N.; Christiani, D. C.; Settleman, J.; Haber, D. A., Activating
mutations in the epidermal growth factor receptor underlying responsiveness
of non-small-cell lung cancer to gefitinib. The New England journal of
medicine 2004, 350 (21), 2129-39; (b) Paez, J. G.; Janne, P. A.; Lee, J. C.;
Tracy, S.; Greulich, H.; Gabriel, S.; Herman, P.; Kaye, F. J.; Lindeman, N.;
Boggon, T. J.; Naoki, K.; Sasaki, H.; Fujii, Y.; Eck, M. J.; Sellers, W. R.;
Johnson, B. E.; Meyerson, M., EGFR mutations in lung cancer: correlation
with clinical response to gefitinib therapy. Science 2004, 304 (5676),
1497-500; (c) Sasaki, H.; Endo, K.; Takada, M.; Kawahara, M.; Kitahara, N.;
Tanaka, H.; Okumura, M.; Matsumura, A.; Iuchi, K.; Kawaguchi, T.; Yukiue,
H.; Kobayashi, Y.; Yano, M.; Fujii, Y., L858R EGFR mutation status
correlated with clinico-pathological features of Japanese lung cancer. Lung
Cancer 2006, 54 (1), 103-8.
6. Wermuth, G.; Ganellin, C. R.; Lindberg, P.; Mitscher, L. A., Glossary of
terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure
Appl Chem 1998, 70 (5), 1129-1143.
7. Wolber, G.; Langer, T., LigandScout: 3-D pharmacophores derived from
protein-bound ligands and their use as virtual screening filters. Journal of
chemical information and modeling 2005, 45 (1), 160-9.
8. Chiang, Y. K.; Kuo, C. C.; Wu, Y. S.; Chen, C. T.; Coumar, M. S.; Wu, J.
S.; Hsieh, H. P.; Chang, C. Y.; Jseng, H. Y.; Wu, M. H.; Leou, J. S.; Song, J.
S.; Chang, J. Y.; Lyu, P. C.; Chao, Y. S.; Wu, S. Y., Generation of
Ligand-Based Pharmacophore Model and Virtual Screening for Identification
of Novel Tubulin Inhibitors with Potent Anticancer Activity. J Med Chem
2009, 52 (14), 4221-4233.
9. Dong, X. L.; Ebalunode, J. O.; Yang, S. Y.; Zheng, W. F.,
Receptor-Based Pharmacophore and Pharmacophore Key Descriptors forVirtual Screening and QSAR Modeling. Curr Comput-Aid Drug 2011, 7 (3),
181-189.
10. Datta, S.; Grant, D. J., Crystal structures of drugs: advances in
determination, prediction and engineering. Nature reviews. Drug discovery
2004, 3 (1), 42-57.
11. (a) Dixon, S. L.; Smondyrev, A. M.; Knoll, E. H.; Rao, S. N.; Shaw, D.
E.; Friesner, R. A., PHASE: a new engine for pharmacophore perception, 3D
QSAR model development, and 3D database screening: 1. Methodology and
preliminary results. J Comput Aid Mol Des 2006, 20 (10-11), 647-671; (b)
Dixon, S. L.; Smondyrev, A. M.; Rao, S. N., PHASE: A novel approach to
pharmacophore modeling and 3D database searching. Chem Biol Drug Des
2006, 67 (5), 370-372.
12. Chen, I. J.; Foloppe, N., Conformational sampling of druglike molecules
with MOE and catalyst: Implications for pharmacophore modeling and virtual
screening. Journal of chemical information and modeling 2008, 48 (9),
1773-1791.
13. Guner, O.; Clement, O.; Kurogi, Y., Pharmacophore modeling and three
dimensional database searching for drug design using catalyst: Recent
advances. Curr Med Chem 2004, 11 (22), 2991-3005.
14. (a) Wolber, G.; Langer, T., LigandScout: Interactive automated
pharmacophore model generation from ligand-target complexes. Abstr Pap
Am Chem S 2005, 229, U611-U611; (b) Wolber, G.; Langer, T., LigandScout:
3-d pharmacophores derived from protein-bound Ligands and their use as
virtual screening filters. Journal of chemical information and modeling 2005,
45 (1), 160-169.
15. Wolber, G.; Seidel, T.; Bendix, F.; Langer, T., Molecule-pharmacophore
superpositioning and pattern matching in computational drug design. Drug
Discov Today 2008, 13 (1-2), 23-29.
16. Ewing, T. J.; Makino, S.; Skillman, A. G.; Kuntz, I. D., DOCK 4.0:
search strategies for automated molecular docking of flexible molecule
databases. J Comput Aided Mol Des 2001, 15 (5), 411-28.
17. Subramanian, G.; Paterlini, M. G.; Larson, D. L.; Portoghese, P. S.;
Ferguson, D. M., Conformational analysis and automated receptor docking of
selective arylacetamide-based kappa-opioid agonists. J Med Chem 1998, 41
(24), 4777-89.
18. McMartin, C.; Bohacek, R. S., QXP: Powerful, rapid computer
algorithms for structure-based drug design. J Comput Aid Mol Des 1997, 11
(4), 333-344.
19. Abagyan, R.; Totrov, M.; Kuznetsov, D., Icm - a New Method for Protein
Modeling and Design - Applications to Docking and Structure Prediction
from the Distorted Native Conformation. J Comput Chem 1994, 15 (5),
488-506.
20. Claussen, H.; Buning, C.; Rarey, M.; Lengauer, T., FlexE: efficient
molecular docking considering protein structure variations. J Mol Biol 2001,308 (2), 377-95.
21. McGann, M., FRED pose prediction and virtual screening accuracy.
Journal of chemical information and modeling 2011, 51 (3), 578-96.
22. Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R., Development
and validation of a genetic algorithm for flexible docking. J Mol Biol 1997,
267 (3), 727-48.
23. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.;
Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw,
D. E.; Francis, P.; Shenkin, P. S., Glide: a new approach for rapid, accurate
docking and scoring. 1. Method and assessment of docking accuracy. J Med
Chem 2004, 47 (7), 1739-49.
24. Venkatachalam, C. M.; Jiang, X.; Oldfield, T.; Waldman, M., LigandFit:
a novel method for the shape-directed rapid docking of ligands to protein
active sites. J Mol Graph Model 2003, 21 (4), 289-307.
25. Lyne, P. D., Structure-based virtual screening: an overview. Drug Discov
Today 2002, 7 (20), 1047-1055.
26. (a) Kuntz, I. D., Structure-based strategies for drug design and discovery.
Science 1992, 257 (5073), 1078-82; (b) Lyne, P. D., Structure-based virtual
screening: an overview. Drug Discov Today 2002, 7 (20), 1047-55.
27. (a) Schevitz, R. W.; Bach, N. J.; Carlson, D. G.; Chirgadze, N. Y.;
Clawson, D. K.; Dillard, R. D.; Draheim, S. E.; Hartley, L. W.; Jones, N. D.;
Mihelich, E. D.; et al., Structure-based design of the first potent and selective
inhibitor of human non-pancreatic secretory phospholipase A2. Nature
structural biology 1995, 2 (6), 458-65; (b) Shoichet, B. K.; Stroud, R. M.;
Santi, D. V.; Kuntz, I. D.; Perry, K. M., Structure-Based Discovery of
Inhibitors of Thymidylate Synthase. Science 1993, 259 (5100), 1445-1450; (c)
Schevitz, R. W.; Bach, N. J.; Carlson, D. G.; Chirgadze, N. Y.; Clawson, D.
K.; Dillard, R. D.; Draheim, S. E.; Hartley, L. W.; Jones, N. D.; Mihelich, E.
D.; Olkowski, J. L.; Snyder, D. W.; Sommers, C.; Wery, J. P.,
Structure-Based Design of the First Potent and Selective Inhibitor of Human
Nonpancreatic Secretory Phospholipase-a(2). Nature structural biology 1995,
2 (6), 458-465.
28. Wang, J.; Kang, X.; Kuntz, I. D.; Kollman, P. A., Hierarchical database
screenings for HIV-1 reverse transcriptase using a pharmacophore model,
rigid docking, solvation docking, and MM-PB/SA. J Med Chem 2005, 48 (7),
2432-44.
29. (a) Osterberg, F.; Morris, G. M.; Sanner, M. F.; Olson, A. J.; Goodsell, D.
S., Automated docking to multiple target structures: Incorporation of protein
mobility and structural water heterogeneity in AutoDock. Proteins-Structure
Function and Genetics 2002, 46 (1), 34-40; (b) Park, H.; Lee, J.; Lee, S.,
Critical assessment of the automated AutoDock as a new docking tool for
virtual screening. Proteins 2006, 65 (3), 549-554; (c) Morris, G. M.; Huey, R.;Lindstrom, W.; Li, C. L.; Zhao, Y.; Hart, W. E.; Belew, R.; Sanner, M. F.;
Goodsell, D. S.; Olson, W. J., Recent advances in autodock: Search,
representation and scoring. Abstr Pap Am Chem S 2004, 228, U508-U508.
30. Huey, R.; Morris, G. M.; Olson, A. J.; Goodsell, D. S., A semiempirical
free energy force field with charge-based desolvation. J Comput Chem 2007,
28 (6), 1145-1152.
31. Gasteiger, J.; Marsili, M., Iterative Partial Equalization of Orbital
Electronegativity - a Rapid Access to Atomic Charges. Tetrahedron 1980, 36
(22), 3219-3228.
32. Wang, J. C.; Lin, J. H.; Chen, C. M.; Perryman, A. L.; Olson, A. J.,
Robust Scoring Functions for Protein-Ligand Interactions with Quantum
Chemical Charge Models. Journal of chemical information and modeling
2011, 51 (10), 2528-2537.
33. Roche, O.; Kiyama, R.; Brooks, C. L., 3rd, Ligand-protein database:
linking protein-ligand complex structures to binding data. J Med Chem 2001,
44 (22), 3592-8.
34. Weiner, S. J.; Kollman, P. A.; Case, D. A.; Singh, U. C.; Ghio, C.;
Alagona, G.; Profeta, S.; Weiner, P., A New Force-Field for Molecular
Mechanical Simulation of Nucleic-Acids and Proteins. Journal of the
American Chemical Society 1984, 106 (3), 765-784.
35. (a) Tetko, I. V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.;
Ertl, P.; Palyulin, V. A.; Radchenko, E. V.; Zefirov, N. S.; Makarenko, A. S.;
Tanchuk, V. Y.; Prokopenko, V. V., Virtual computational chemistry
laboratory--design and description. J Comput Aided Mol Des 2005, 19 (6),
453-63; (b) Tetko, I. V., Computing chemistry on the web. Drug Discov
Today 2005, 10 (22), 1497-500; (c) Tetko, I. V.; Tanchuk, V. Y., Application
of associative neural networks for prediction of lipophilicity in ALOGPS 2.1
program. J Chem Inf Comput Sci 2002, 42 (5), 1136-45; (d) Tetko, I. V.;
Tanchuk, V. Y.; Villa, A. E. P., Prediction of n-octanol/water partition
coefficients from PHYSPROP database using artificial neural networks and
E-state indices. J Chem Inf Comp Sci 2001, 41 (5), 1407-1421.
36. AQUASOL database
http://www.pharmacy.arizona.edu/outreach/aquasol/index.html.
37. PHYSPROP database.
http://www.srcinc.com/what--‐we--‐do/product.aspx?id=133.
38. (a) Abad-Zapatero, C.; Metz, J. T., Ligand efficiency indices as
guideposts for drug discovery. Drug Discov Today 2005, 10 (7), 464-469; (b)
Hopkins, A. L.; Groom, C. R.; Alex, A., Ligand efficiency: a useful metric for
lead selection. Drug Discov Today 2004, 9 (10), 430-431.
39. Garcia-Sosa, A. T.; Hetenyi, C.; Maran, U., Drug Efficiency Indices for
Improvement of Molecular Docking Scoring Functions. J Comput Chem 2010,
31 (1), 174-184.
40. Lakowicz.J.R., Principes of Flurescence Spectroscopy. 1999.41. Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.;
Weissig, H.; Shindyalov, I. N.; Bourne, P. E., The Protein Data Bank. Nucleic
Acids Res 2000, 28 (1), 235-42.
42. Stamos, J.; Sliwkowski, M. X.; Eigenbrot, C., Structure of the epidermal
growth factor receptor kinase domain alone and in complex with a
4-anilinoquinazoline inhibitor. The Journal of biological chemistry 2002, 277
(48), 46265-72.
43. Wood, E. R.; Truesdale, A. T.; McDonald, O. B.; Yuan, D.; Hassell, A.;
Dickerson, S. H.; Ellis, B.; Pennisi, C.; Horne, E.; Lackey, K.; Alligood, K. J.;
Rusnak, D. W.; Gilmer, T. M.; Shewchuk, L., A unique structure for
epidermal growth factor receptor bound to GW572016 (Lapatinib):
relationships among protein conformation, inhibitor off-rate, and receptor
activity in tumor cells. Cancer research 2004, 64 (18), 6652-9.
44. Zhang, X.; Gureasko, J.; Shen, K.; Cole, P. A.; Kuriyan, J., An allosteric
mechanism for activation of the kinase domain of epidermal growth factor
receptor. Cell 2006, 125 (6), 1137-49.
45. Blair, J. A.; Rauh, D.; Kung, C.; Yun, C. H.; Fan, Q. W.; Rode, H.;
Zhang, C.; Eck, M. J.; Weiss, W. A.; Shokat, K. M., Structure-guided
development of affinity probes for tyrosine kinases using chemical genetics.
Nat Chem Biol 2007, 3 (4), 229-238.
46. Yun, C. H.; Boggon, T. J.; Li, Y. Q.; Woo, M. S.; Greulich, H.; Meyerson,
M.; Eck, M. J., Structures of lung cancer-derived EGFR mutants and inhibitor
complexes: Mechanism of activation and insights into differential inhibitor
sensitivity. Cancer cell 2007, 11 (3), 217-227.
47. Yun, C. H.; Mengwasser, K. E.; Toms, A. V.; Woo, M. S.; Greulich, H.;
Wong, K. K.; Meyerson, M.; Eck, M. J., The T790M mutation in EGFR
kinase causes drug resistance by increasing the affinity for ATP. Proceedings
of the National Academy of Sciences of the United States of America 2008,
105 (6), 2070-2075.
48. Zhou, W. J.; Ercan, D.; Chen, L.; Yun, C. H.; Li, D. N.; Capelletti, M.;
Cortot, A. B.; Chirieac, L.; Iacob, R. E.; Padera, R.; Engen, J. R.; Wong, K.
K.; Eck, M. J.; Gray, N. S.; Janne, P. A., Novel mutant-selective EGFR
kinase inhibitors against EGFR T790M. Nature 2009, 462 (7276), 1070-1074.
49. Brewer, M. R.; Choi, S. H.; Alvarado, D.; Moravcevic, K.; Pozzi, A.;
Lemmon, M. A.; Carpenter, G., The Juxtamembrane Region of the EGF
Receptor Functions as an Activation Domain. Mol Cell 2009, 34 (6), 641-651.
50. Jura, N.; Endres, N. F.; Engel, K.; Deindl, S.; Das, R.; Lamers, M. H.;
Wemmer, D. E.; Zhang, X. W.; Kuriyan, J., Mechanism for Activation of the
EGF Receptor Catalytic Domain by the Juxtamembrane Segment. Cell 2009,
137 (7), 1293-1307.
51. Klein, D. E.; Stayrook, S. E.; Shi, F. M.; Narayan, K.; Lemmon, M. A.,
Structural basis for EGFR ligand sequestration by Argos. Nature 2008, 453
(7199), 1271-U79.52. Mineev, K. S.; Bocharov, E. V.; Pustovalova, Y. E.; Bocharova, O. V.;
Chupin, V. V.; Arseniev, A. S., Spatial Structure of the Transmembrane
Domain Heterodimer of ErbB1 and ErbB2 Receptor Tyrosine Kinases. J Mol
Biol 2010, 400 (2), 231-243.
53. Alvarado, D.; Klein, D. E.; Lemmon, M. A., Structural Basis for
Negative Cooperativity in Growth Factor Binding to an EGF Receptor. Cell
2010, 142 (4), 568-579.
54. Yoshikawa, S.; Kukimoto-Niino, M.; Parker, L.; Handa, N.; Terada, T.;
Fujimoto, T.; Terazawa, Y.; Wakiyama, M.; Sato, M.; Sano, S.; Kobayashi, T.;
Tanaka, T.; Chen, L.; Liu, Z. J.; Wang, B. C.; Shirouzu, M.; Kawa, S.; Semba,
K.; Yamamoto, T.; Yokoyama, S., Structural basis for the altered drug
sensitivities of non-small cell lung cancer-associated mutants of human
epidermal growth factor receptor. Oncogene 2012.
55. LigandScout 3.0. http://www.inteligand.com/ligandscout/.
56. Catalyst. Accelrys, Accelrys Inc. http://www.accelrys.com.
57. DiscoveryStudio 2.5, Accelrys, Accelrys Inc.
http://accelrys.com/events/webinars/discovery--‐studio--‐25/index.html.
58. Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.; Coleman, R. G.,
ZINC: A Free Tool to Discover Chemistry for Biology. Journal of chemical
information and modeling 2012.
59. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J.,
Experimental and computational approaches to estimate solubility and
permeability in drug discovery and development settings. Advanced drug
delivery reviews 2001, 46 (1-3), 3-26.
60. Li, J.; Ehlers, T.; Sutter, J.; Varma-O''brien, S.; Kirchmair, J., CAESAR: a
new conformer generation algorithm based on recursive buildup and local
rotational symmetry consideration. Journal of chemical information and
modeling 2007, 47 (5), 1923-32.
61. Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.;
Goodsell, D. S.; Olson, A. J., AutoDock4 and AutoDockTools4: Automated
Docking with Selective Receptor Flexibility. J Comput Chem 2009, 30 (16),
2785-2791.
62. O''Boyle, N. M.; Banck, M.; James, C. A.; Morley, C.; Vandermeersch, T.;
Hutchison, G. R., Open Babel: An open chemical toolbox. J Cheminform
2011, 3, 33.
63. Gaussian 03, R. C., Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.;
Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Montgomery, Jr., J. A.;
Vreven, T.; Kudin, K. N.; Burant, J. C.; Millam, J. M.; Iyengar, S. S.; Tomasi,
J.; Barone, V.; Mennucci, B.; Cossi, M.; Scalmani, G.; Rega, N.; Petersson, G.
A.; Nakatsuji, H.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.;
Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Klene, M.; Li, X.;
Knox, J. E.; Hratchian, H. P.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo,
J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.;Pomelli, C.; Ochterski, J. W.; Ayala, P. Y.; Morokuma, K.; Voth, G. A.;
Salvador, P.; Dannenberg, J. J.; Zakrzewski, V. G.; Dapprich, S.; Daniels, A.
D.; Strain, M. C.; Farkas, O.; Malick, D. K.; Rabuck, A. D.; Raghavachari, K.;
Foresman, J. B.; Ortiz, J. V.; Cui, Q.; Baboul, A. G.; Clifford, S.; Cioslowski,
J.; Stefanov, B. B.; Liu, G.; Liashenko, A.; Piskorz, P.; Komaromi, I.; Martin,
R. L.; Fox, D. J.; Keith, T.; Al-Laham, M. A.; Peng, C. Y.; Nanayakkara, A.;
Challacombe, M.; Gill, P. M. W.; Johnson, B.; Chen, W.; Wong, M. W.;
Gonzalez, C.; and Pople, J. A, Gaussian03. 2004.
64. (a) Bayly, C. I.; Cieplak, P.; Cornell, W. D.; Kollman, P. A., A
Well-Behaved Electrostatic Potential Based Method Using Charge Restraints
for Deriving Atomic Charges - the Resp Model. J Phys Chem-Us 1993, 97
(40), 10269-10280; (b) Zhang, W.; Hou, T. J.; Qiao, X. B.; Xu, X. J.,
Parameters for the generalized born model consistent with RESP atomic
partial charge assignment protocol. J Phys Chem B 2003, 107 (34),
9071-9078.
65. David A. Case, T. A. D., T. E. Cheatham, Carlos L. Simmerling, J. Wang,
Robert E. Duke, Ray Luo, Michael Crowley, Ross C. Walker, W. Zhang, K.
M. Merz, B. Wang, S. Hayik, Adrian Roitberg, Gustavo Seabra, I. Kolossvary,
K. F. Wong, F. Paesani, J. Vanicek, X. Wu, Scott R. Brozell, Tom
Steinbrecher, Holger Gohlke, L. Yang, C. Tan, J. Mongan, V. Hornak, G. Cui,
D. H. Mathews, M. G. Seetin, C. Sagui, V. Babin, Peter A. Kollman,
AMBER11. Amber 11. University of California, San Francisco.
66. Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A.,
Development and testing of a general amber force field. J Comput Chem 2004,
25 (9), 1157-74.
67. Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling,
C., Comparison of multiple Amber force fields and development of improved
protein backbone parameters. Proteins 2006, 65 (3), 712-25.
68. (a) Lucasius, C. B.; Kateman, G., Understanding and Using Genetic
Algorithms .1. Concepts, Properties and Context. Chemometr Intell Lab 1993,
19 (1), 1-33; (b) Lucasius, C. B.; Kateman, G., Understanding and Using
Genetic Algorithms .2. Representation, Configuration and Hybridization.
Chemometr Intell Lab 1994, 25 (2), 99-145.
69. Xiang, Z.; Soto, C. S.; Honig, B., Evaluating conformational free
energies: the colony energy and its application to the problem of loop
prediction. Proceedings of the National Academy of Sciences of the United
States of America 2002, 99 (11), 7432-7.
70. (a) Dolinsky, T. J.; Nielsen, J. E.; McCammon, J. A.; Baker, N. A.,
PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann
electrostatics calculations. Nucleic Acids Res 2004, 32, W665-W667; (b)
Dolinsky, T. J.; Czodrowski, P.; Li, H.; Nielsen, J. E.; Jensen, J. H.; Klebe, G.;
Baker, N. A., PDB2PQR: expanding and upgrading automated preparation of
biomolecular structures for molecular simulations. Nucleic Acids Res 2007,35, W522-W525.
71. Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein,
M. L., Comparison of Simple Potential Functions for Simulating Liquid
Water. J Chem Phys 1983, 79 (2), 926-935.
72. Ryckaert, J. P.; Ciccotti, G.; Berendsen, H. J. C., Numerical-Integration
of Cartesian Equations of Motion of a System with Constraints -
Molecular-Dynamics of N-Alkanes. J Comput Phys 1977, 23 (3), 327-341.
73. (a) Darden, T.; York, D.; Pedersen, L., Particle Mesh Ewald - an
N.Log(N) Method for Ewald Sums in Large Systems. J Chem Phys 1993, 98
(12), 10089-10092; (b) Petersen, H. G., Accuracy and Efficiency of the
Particle Mesh Ewald Method. J Chem Phys 1995, 103 (9), 3668-3679; (c)
Cheatham, T. E.; Miller, J. L.; Fox, T.; Darden, T. A.; Kollman, P. A.,
Molecular-Dynamics Simulations on Solvated Biomolecular Systems - the
Particle Mesh Ewald Method Leads to Stable Trajectories of DNA, Rna, and
Proteins. Journal of the American Chemical Society 1995, 117 (14),
4193-4194.
74. Karaman, M. W.; Herrgard, S.; Treiber, D. K.; Gallant, P.; Atteridge, C.
E.; Campbell, B. T.; Chan, K. W.; Ciceri, P.; Davis, M. I.; Edeen, P. T.;
Faraoni, R.; Floyd, M.; Hunt, J. P.; Lockhart, D. J.; Milanov, Z. V.; Morrison,
M. J.; Pallares, G.; Patel, H. K.; Pritchard, S.; Wodicka, L. M.; Zarrinkar, P.
P., A quantitative analysis of kinase inhibitor selectivity. Nature
biotechnology 2008, 26 (1), 127-32.
75. Wheeler, D. L.; Dunn, E. F.; Harari, P. M., Understanding resistance to
EGFR inhibitors-impact on future treatment strategies. Nature reviews.
Clinical oncology 2010, 7 (9), 493-507.