From base pair to bedside: Molecular simulation and the translation of genomics to personalized medicine

Wiley Interdisciplinary Reviews: Systems Biology and Medicine

Volumn 4, Issue 6, 2012, Pages 585-598

  Wright, David W ^a   Wan, Shunzhou ^a   Shublaq, Nour ^a   Zasada, Stefan J ^a   Coveney, Peter V ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ERLOTINIB; GEFITINIB; IMATINIB; INTEGRASE; NELFINAVIR; PROTEINASE; RNA DIRECTED DNA POLYMERASE;

CANCER THERAPY; COARSE GRAINING; DRUG MECHANISM; DRUG STRUCTURE; ENZYME STRUCTURE; GENOMICS; HUMAN; MOLECULAR DYNAMICS; MONTE CARLO METHOD; PERSONALIZED MEDICINE; QUANTUM MECHANICS; REVIEW;

ANTI-HIV AGENTS; BASE PAIRING; GENOMICS; HIV; HIV INFECTIONS; HUMANS; INDIVIDUALIZED MEDICINE; MODELS, MOLECULAR; RECEPTOR, EPIDERMAL GROWTH FACTOR; SIGNAL TRANSDUCTION;

EID: 84867585198     PISSN: 19395094     EISSN: 1939005X     Source Type: Journal    
DOI: 10.1002/wsbm.1186     Document Type: Review

References (112)

1. Shublaq N. Strategic report of genotype-phenotype resources in the European Union; 2012, 1-28.
2. Pollack A. Awaiting the genome payoff. The New York Times, 2010.
3. Wade N. A decade later, genetic map yields few new cures. The New York Times, 2010.
4. Dudley JT, Schadt E, Sirota M, Butte AJ, Ashley E. Drug discovery in a multidimensional world: systems, patterns, and networks. J Cardiovasc Trans Res 2010, 3:438-447.
5. Schadt E, Bjorkegren JLM. NEW: network-enabled wisdom in biology, medicine and health care. Sci Transl Med 2012, 4:28-35.
6. Sorger PK, Allerheiligen SRB, Abernethy DR, Altman RB, Brouwer KLR, Califano A, D'Argenio DZ, Iyengar R, Jusko WJ, Lalonde R, et al. Quantitative and systems pharmacology in the post-genomic era: new approaches to discovering drugs and understanding therapeutic mechanisms An NIH white paper by the QSP Workshop group, October 2011. Available at: www.nigms.nih.gov/NR/rdonlyres/8ECB1F7C-BE3B-431F-89E6-A43411811AB1/0/SystemsPharmaWPSorger2011.pdf
7. Zasada SJ, Wang T, Haidar A, Liu E, Graf N, Clapworthy G, Manos S, Coveney PV. IMENSE: an e-infrastructure environment for patient specific multiscale data integration, modelling and clinical treatment. J Comput Sci 2011. Available at: http://dx.doi.org/10.1016/j.jocs.2011.07.001
8. Manos S, Zasada SJ, Coveney PV. Life or death decision-making: the medical case for large-scale, on-demand grid computing. CTWatch Quarterly, 2008, vol 4.
9. Marias K, Dionysiou D, Sakkalis V, Graf N, Bohle RM, Coveney PV, Wan S, Folarin A, Buchler P, Reyes M, et al. Clinically driven design of multi-scale cancer models: the ContraCancrum project paradigm. Interface Focus 2011, 1:450-461.
10. British Prime Minister's Office: PM speech on life sciences and opening up the NHS; 2011.
11. Tang MW, Liu TF, Shafer RW. The HIVdb system for HIV-1 genotypic resistance interpretation. Intervirology 2012, 55:98-101.
12. Frenkel D, Smit B. Understanding molecular simulation: from algorithms to applications. San Diego, CA: Academic Press Inc.; 2001.
13. Leach AR. Molecular modelling: principles and applications. Harlow, UK: Prentice Hall; 2001.
14. Shan YB, Kim ET, Eastwood MP, Dror RO, Seeliger MA, Shaw DE. How does a drug molecule find its target binding site? J Am Chem Soc 2011, 133: 9181-9183.
15. Christ CD, Mark AE, van Gunsteren WF. Basic ingredients of free energy calculations: a review. J Comput Chem 2010, 31:1569-1582.
16. Shirts M, Mobley D, Chodera J. Alchemical free energy calculations: ready for prime time? Ann Rep Comput Chem 2007, 3:41-59.
17. Chodera JD, Mobley DL, Shirts MR, Dixon RW, Branson K, Pande VS. Alchemical free energy methods for drug discovery: progress and challenges. Curr Opin Struct Biol 2011, 21:150-160.
18. Mobley DL, Dill KA. Binding of small-molecule ligands to proteins: "what you see" is not always "what you get". Structure 2009, 17:489-498.
19. Steinbrecher T, Labahn A. Towards accurate free energy calculations in ligand protein-binding studies. Curr Med Chem 2010, 2010:767-785.
20. Gilson M, Zhou H-X. Calculation of protein-ligand binding affinities. Annu Rev Biophys Biomol Struct 2007, 36:21-42.
21. Kuehn BM. UNAIDS report: AIDS epidemic slowing, but huge challenges remain. J Am Med Assoc 2006, 296:29-30.
22. Fang CT, Chang YY, Hsu HM, Twu SJ, Chen KT, Lin CC, Huang LY, Chen MY, Hwang JS, Wang JD, Chuang CY. Life expectancy of patients with newly-diagnosed HIV infection in the era of highly active antiretroviral therapy. QJM 2007, 100:97-105.
23. Baxter JD, Mayers DL, Wentworth DN, Neaton JD, Hoover ML, Winters MA, Mannheimer SB, Thompson MA, Abrams DI, Brizz BJ, et al. A randomized study of antiretroviral management based on plasma genotypic antiretroviral resistance testing in patients failing therapy. CPCRA 046 Study Team for the Terry Beirn Community Programs for Clinical Research on AIDS. AIDS 2000, 14:F83-F93.
24. Tural C, Ruiz L, Holtzer C, Schapiro J, Viciana P, Gonzalez J, Domingo P, Boucher C, Rey-Joly C, Clotet B. Clinical utility of HIV-1 genotyping and expert advice: the Havana trial. AIDS 2002, 16: 209-218.
25. Durant J, Clevenbergh P, Halfon P, Delgiudice P, Porsin S, Simonet P, Montagne N, Boucher CA, Schapiro JM, Dellamonica P. Drug-resistance genotyping in HIV-1 therapy: the VIRADAPT randomised controlled trial. Lancet 1999, 353:2195-2199.
26. Meynard JL, Vray M, Morand-Joubert L, Race E, Descamps D, Peytavin G, Matheron S, Lamotte C, Guiramand S, Costagliola D, et al. Phenotypic or genotypic resistance testing for choosing antiretroviral therapy after treatment failure: a randomized trial. AIDS 2002, 16:727-736.
27. Van Laethem K, De Luca A, Antinori A, Cingolani A, Perna CF, Vandamme AM. A genotypic drug resistance interpretation algorithm that significantly predicts therapy response in HIV-1-infected patients. Antivir Ther 2002, 7:123-129.
28. De Luca A, Cingolani A, Di Giambenedetto S, Trotta MP, Baldini F, Rizzo MG, Bertoli A, Liuzzi G, Narciso P, Murri R, et al. Variable prediction of antiretroviral treatment outcome by different systems for interpreting genotypic human immunodeficiency virus type 1 drug resistance. J Infect Dis 2003, 187: 1934-1943.
29. Kubinyi H. Structure-based design of enzyme inhibitors and receptor ligands. Curr Opin Drug Discov Devel 1998, 1:4-15.
30. Wlodawer A, Vondrasek J. Inhibitors of HIV-1 protease: a major success of structure-assisted drug design. Annu Rev Biophys Biomol Struct 1998, 27:249-284.
31. De Clercq E. The design of drugs for HIV and HCV. Nat Rev Drug Discov 2007, 6:1001-1018.
32. Schames JR, Henchman RH, Siegel JS, Sotriffer CA, Ni HH, McCammon JA. Discovery of a novel binding trench in HIV integrase. J Med Chem 2004, 47: 1879-1881.
33. Ingale KB, Bhatia MS. HIV-1 integrase inhibitors: a review of their chemical development. Antivir Chem Chemother 2011, 22:95-105.
34. Hare S, Gupta SS, Valkov E, Engelman A, Cherepanov P. Retroviral intasome assembly and inhibition of DNA strand transfer. Nature 2010, 464:232-236.
35. Krishnan L, Li X, Naraharisetty HL, Hare S, Cherepanov P, Engelman A. Structure-based modeling of the functional HIV-1 intasome and its inhibition. Proc Nat Acad Sci U S A 2010, 107: 15910-15915
36. Johnson BC, Métifiot M, Pommier Y, Hughes SH. Molecular dynamics approaches estimate the binding energy of HIV-1 integrase inhibitors and correlate with in vitro activity. Antimicrob Agents Chemother 2012, 56:411-419.
37. Sgobba M, Olubiyi O, Ke S, Haider S. Molecular dynamics of HIV1-integrase in complex with 93del-a structural perspective on the mechanism of inhibition. J Biomol Struct Dyn 2012, 29:863-877.
38. Wang J, Morin P, Wang W, Kollman PA. Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. J Am Chem Soc 2001, 123:5221-5230.
39. Bakan A, Bahar I. The intrinsic dynamics of enzymes plays a dominant role in determining the structural changes induced upon inhibitor binding. Proc Natl Acad Sci U S A 2009, 106:14349-14354.
40. Ivetac A, McCammon JA. Elucidating the inhibition mechanism of HIV-1 non-nucleoside reverse transcriptase inhibitors through multicopy molecular dynamics simulations. J Mol Biol 2009, 388:644-658.
41. Das K, Clark AD Jr., Lewi PJ, Heeres J, De Jonge MR, Koymans LM, Vinkers HM, Daeyaert F, Ludovici DW, Kukla MJ, et al. Roles of conformational and positional adaptability in structure-based design of TMC125-R165335 (etravirine) and related non-nucleoside reverse transcriptase inhibitors that are highly potent and effective against wild-type and drug-resistant HIV-1 variants. J Med Chem 2004, 47:2550-2560.
42. Sluis-Cremer N, Temiz NA, Bahar I. Conformational changes in HIV-1 reverse transcriptase induced by nonnucleoside reverse transcriptase inhibitor binding. Curr HIV Res 2004, 2:323-332.
43. Udier-Blagović M, Tirado-Rives J, Jorgensen WL. Validation of a model for the complex of HIV-1 reverse transcriptase with nonnucleoside inhibitor TMC125. J Am Chem Soc 2003, 125:6016-6017.
44. Alcaro S, Artese A, Ceccherini-Silberstein F, Ortuso F, Perno CF, Sing T, Svicher V. Molecular dynamics and free energy studies on the wild-type and mutated HIV-1 protease complexed with four approved drugs: mechanism of binding and drug resistance. J Chem Inf Model 2009, 49:1751-1761.
45. Tzoupis H, Leonis G, Durdagi S, Mouchlis V, Mavromoustakos T, Papadopoulos MG. Binding of novel fullerene inhibitors to HIV-1 protease: insight through molecular dynamics and molecular mechanics Poisson-Boltzmann surface area calculations. J Comput Aided Mol Des 2011, 25:959-976.
46. Soares RO, Batista PR, Costa MG, Dardenne LE, Pascutti PG, Soares MA. Understanding the HIV-1 protease nelfinavir resistance mutation D30N in subtypes B and C through molecular dynamics simulations. J Mol Graph Model 2010, 29:137-147.
47. Hao GF, Yang GF, Zhan CG. Computational mutation scanning and drug resistance mechanisms of HIV-1 protease inhibitors. J Phys Chem B 2010, 114: 9663-9676.
48. Sousa SF, Fernandes PA, Ramos MJ. Protein-ligand docking: current status and future challenges. Proteins 2006, 65:15-26.
49. Meagher KL, Carlson HA. Incorporating protein flexibility in structure-based drug discovery: using HIV-1 protease as a test case. J Am Chem Soc 2004, 126: 13276-13281.
50. Osterberg F, Morris GM, Sanner MF, Olson AJ, Goodsell DS. Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins 2002, 46:34-40.
51. Foulkes-Murzycki JE, Scott WR, Schiffer CA. Hydrophobic sliding: a possible mechanism for drug resistance in human immunodeficiency virus type 1 protease. Structure 2007, 15:225-233.
52. Liu Z, Wang Y, Brunzelle J, Kovari IA, Kovari LC. Nine crystal structures determine the substrate envelope of the MDR HIV-1 protease. Protein J 2011, 30: 173-183.
53. Chellappan S, Kairys V, Fernandes MX, Schiffer C, Gilson MK. Evaluation of the substrate envelope hypothesis for inhibitors of HIV-1 protease. Proteins 2007, 68:561-567.
54. Freedberg DI, Ishima R, Jacob J, Wang YX, Kustanovich I, Louis JM, Torchia DA. Rapid structural fluctuations of the free HIV protease flaps in solution: relationship to crystal structures and comparison with predictions of dynamics calculations. Protein Sci 2002, 11:221-232.
55. Scott WR, Schiffer CA. Curling of flap tips in HIV-1 protease as a mechanism for substrate entry and tolerance of drug resistance. Structure 2000, 8:1259-1265.
56. Lauria A, Ippolito M, Almerico AM. Molecular dynamics studies on HIV-1 protease: a comparison of the flap motions between wild type protease and the M46I/G51D double mutant. J Mol Model 2007, 13: 1151-1156.
57. Zimmer J, Roman F, Lambert C, Jonckheer A, Vazquez A, Plesséria J, Servais J, Covens K, Weber J, Van Laethem K, et al. Impact on replicative fitness of the G48E substitution in the protease of HIV-1: an in vitro and in silico evaluation. J Acquir Immune Defic Syndr 2008, 48:255-262.
58. Kagan RM, Shenderovich MD, Heseltine PNR, Ramnarayan K. Structural analysis of an HIV-1 protease I47A mutant resistant to the protease inhibitor lopinavir. Protein Sci 2005, 14:1870-1878.
59. Wright DW, Coveney PV. Resolution of discordant HIV-1 protease resistance rankings using molecular dynamics simulations. J Chem Inf Model 2011, 51: 2636-2649.
60. Sloot PM, Coveney PV, Ertaylan G, Muller V, Boucher CA, Bubak M. HIV decision support: from molecule to man. Philos Transact A Math Phys Eng Sci 2009, 367:2691-2703.
61. Stoica I, Sadiq SK, Coveney PV. Rapid and accurate prediction of binding free energies for saquinavir-bound HIV-1 proteases. J Am Chem Soc 2008, 130: 2639-2648.
62. Sadiq SK, Wright DW, Kenway OA, Coveney PV. Accurate ensemble molecular dynamics binding free energy ranking of multidrug-resistant HIV-1 proteases. J Chem Inf Model 2010, 50:890-905.
63. Yuriev E, Agostino M, Ramsland PA. Challenges and advances in computational docking: 2009 in review. J Mol Recogn 2011, 24:149-164.
64. Cullen BR. Regulation of HIV-1 gene expression. FASEB J 1991, 5:2361-2368.
65. Fulle S, Christ NA, Kestner E, Gohlke H. HIV-1 TAR RNA spontaneously undergoes relevant apo-to-holo conformational transitions in molecular dynamics and constrained geometrical simulations. J Chem Inf Model 2010, 50:1489-1501.
66. Wells S, Menor S, Hespenheide B, Thorpe MF. Constrained geometric simulation of diffusive motion in proteins. Phys Biol 2005, 2:S127-S136.
67. Wang T, Duan Y. Binding modes of CCR5-targetting HIV entry inhibitors: partial and full antagonists. J Mol Graph Model 2008, 26:1287-1295.
68. Lopalco L. CCR5: from natural resistance to a new anti-HIV strategy. Viruses 2010, 2:574-600.
69. Autore F, Bergeron JR, Malim MH, Fraternali F, Huthoff H. Rationalisation of the differences between APOBEC3G structures from crystallography and NMR studies by molecular dynamics simulations. PLoS One 2010, 5:e11515.
70. Zazzi M, Incardona F, Rosen-Zvi M, Prosperi M, Lengauer T, Altmann A, Sonnerborg A, Lavee T, Schulter E, Kaiser R. Predicting response to antiretroviral treatment by machine learning: the EuResist project. Intervirology 2012, 55:123-127.
71. Lengauer T. Bioinformatical assistance of selecting anti-HIV therapies: where do we stand? Intervirology 2012, 55:108-112.
72. Prosperi MC, Altmann A, Rosen-Zvi M, Aharoni E, Borgulya G, Bazso F, Sonnerborg A, Schulter E, Struck D, Ulivi G, et al. Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment. Antivir Ther 2009, 14: 433-442.
73. Flexner C. HIV drug development: the next 25 years. Nat Rev Drug Discov 2007, 6:959-966.
74. Murali TM, Dyer MD, Badger D, Tyler BM, Katze MG. Network-based prediction and analysis of HIV dependency factors. PLoS Comput Biol 2011, 7: e1002164.
75. Huang T, Xu Z, Chen L, Cai Y-D, Kong X. Computational analysis of HIV-1 resistance based on gene expression profiles and the virus-host interaction network. PLoS One 2011, 6:e17291.
76. Jäger S, Cimermancic P, Gulbahce N, Johnson JR, McGovern KE, Clarke SC, Shales M, Mercenne G, Pache L, Li K, et al. Global landscape of HIV-human protein complexes. Nature 2011, 481:365-370.
77. Ceccherini-Silberstein F, Malet I, Fabeni L, Dimonte S, Svicher V, D'Arrigo R, Artese A, Costa G, Bono S, Alcaro S, et al. Specific HIV-1 integrase polymorphisms change their prevalence in untreated versus antiretroviral-treated HIV-1-infected patients, all naive to integrase inhibitors. J Antimicrob Chemother 2010, 65:2305-2318.
78. Metzner KJ, Giulieri SG, Knoepfel SA, Rauch P, Burgisser P, Yerly S, Gunthard HF, Cavassini M. Minority quasispecies of drug-resistant HIV-1 that lead to early therapy failure in treatment-naive and -adherent patients. Clin Infect Dis 2009, 48:239-247.
79. Roquebert B, Malet I, Wirden M, Tubiana R, Valantin M, Simon A, Katlama C, Peytavin G, Calvez V, Marcelin A. Role of HIV-1 minority populations on resistance mutational pattern evolution and susceptibility to protease inhibitors. AIDS 2006, 20: 287-289.
80. Thielen A, Lengauer T. Geno2pheno[454]: a web server for the prediction of HIV-1 coreceptor usage from next-generation sequencing data. Intervirology 2012, 55:113-117.
81. Parry CM, Kohli A, Boinett CJ, Towers GJ, McCormick AL, Pillay D. Gag determinants of fitness and drug susceptibility in protease inhibitor-resistant human immunodeficiency virus type 1. J Virol 2009, 83:9094-9101.
82. Sadiq SK, Konnyu B, Muller V, Coveney PV. Reaction kinetics of catalyzed competitive heteropolymer cleavage. J Phys Chem B 2011, 115:11017-11027.
83. Patterson SD, Aebersold RH. Proteomics: the first decade and beyond. Nat Genet 2003, 33:311-323.
84. Wold B, Myers RM. Sequence census methods for functional genomics. Nat Methods 2008, 5:19-21.
85. Cusick ME, Klitgord N, Vidal M, Hill DE. Interactome: gateway into systems biology. Hum Mol Genet 2005, 14(suppl 2):R171-R181.
86. Deisboeck TS, Wang ZH, Macklin P, Cristini V. Multiscale cancer modeling. Annu Rev Biomed Eng 2011, 13:127-155.
87. Rejniak KA, Anderson ARA Hybrid models of tumor growth. WIREs Syst Biol Med 2010, 3:115-125.
88. Deisboeck TS, Zhang L, Yoon J, Costa J. In silico cancer modeling: is it ready for prime time? Nat Clin Pract Oncol 2009, 6:34-42.
89. Materi W, Wishart DS. Computational systems biology in drug discovery and development: methods and applications. Drug Discov Today 2007, 12:295-303.
90. Khain E, Sander LM. Dynamics and pattern formation in invasive tumor growth. Phys Rev Lett 2006, 96:188103.
91. Woodward DE, Cook J, Tracqui P, Cruywagen GC, Murray JD, Alvord EC, Jr. A mathematical model of glioma growth: the effect of extent of surgical resection. Cell Prolif 1996, 29:269-288.
92. Gatenby RA, Gawlinski ET. A reaction-diffusion model of cancer invasion. Cancer Res 1996, 56: 5745-5753.
93. Turner S, Sherratt JA. Intercellular adhesion and cancer invasion: a discrete simulation using the extended Potts model. J Theor Biol 2002, 216:85-100.
94. Sanga S, Frieboes HB, Zheng X, Gatenby R, Bearer EL, Cristini V. Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth. NeuroImage 2007, 37(suppl 1):S120-S134.
95. Karplus M, McCammon JA. Molecular dynamics simulations of biomolecules. Nat Struct Biol 2002, 9:646-652.
96. Meier-Schellersheim M, Fraser IDC, Klauschen F. Multiscale modeling for biologists. WIREs Syst Biol Med 2009, 1:11.
97. Hajduk PJ, Greer J. A decade of fragment-based drug design: strategic advances and lessons learned. Nat Rev Drug Discov 2007, 6:211-219.
98. Hennessy BT, Smith DL, Ram PT, Lu YL, Mills GB. Exploiting the PI3K/AKT pathway for cancer drug discovery. Nat Rev Drug Discov 2005, 4:988-1004.
99. Kühn A, Lehrach H. The "Virtual Patient" system: modeling cancer using deep sequencing technologies for personalized cancer treatment. J Verbrauch Lebensm 2012, 7:55-62.
100. Kreeger PK, Lauffenburger DA. Cancer systems biology: a network modeling perspective. Carcinogenesis 2010, 31:2-8.
101. Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S. The protein kinase complement of the human genome. Science 2002, 208:1912-1934.
102. Roberts PJ, Der CJ. Targeting the Raf-MEK-ERK mitogen-activated protein kinase cascade for the treatment of cancer. Oncogene 2007, 26:3291-3310.
103. Deininger M, Buchdunger E, Druker BJ. The development of imatinib as a therapeutic agent for chronic myeloid leukemia. Blood 2005, 105:2640-2653.
104. Fernández A, Sanguino A, Peng Z, Ozturk E, Chen J, Crespo A, Wulf S, Shavrin A, Qin C, Ma J, et al. An anticancer C-Kit kinase inhibitor is reengineered to make it more active and less cardiotoxic. J Clin Invest 2007, 117:4044-4054.
105. Zhang X, Crespo A, Fernández A. Turning promiscuous kinase inhibitors into safer drugs. Trends Biotechnol 2008, 26:295-301.
106. Gills JJ, LoPiccolo J, Tsurutani J, Shoemaker RH, Best CJM, Abu-Asab MS, Borojerdi J, Warfel NA, Gardner ER, Danish M, et al. Nelfinavir, a lead hiv protease inhibitor, is a broad-spectrum, anticancer agent that induces endoplasmic reticulum stress, autophagy, and apoptosis in vitro and in vivo. Clin Cancer Res 2007, 13:5183-5194.
107. Xie L, Evangelidis T, Xie L, Bourne PE. Drug discovery using chemical systems biology: weak inhibition of multiple kinases may contribute to the anti-cancer effect of nelfinavir. PLoS Comput Biol 2011, 7:e1002037.
108. Tobinick EL. The value of drug repositioning in the current pharmaceutical market. Drug News Perspect 2009, 22:119-125.
109. Wan SZ, Coveney PV. Molecular dynamics simulation reveals structural and thermodynamic features of kinase activation by cancer mutations within the epidermal growth factor receptor. J Comput Chem 2011, 32:2843-2852.
110. Wan SZ, Coveney PV. Rapid and accurate ranking of binding affinities of epidermal growth factor receptor sequences with selected lung cancer drugs. J R Soc Interface 2011, 8:1114-1127.
111. Shaw DE, Deneroff MM, Dror RO, Kuskin JS, Larson RH, Salmon JK, Young C, Batson B, Bowers KJ, Chao JC, et al. Anton, a special-purpose machine for molecular dynamics simulation. Commun ACM 2008, 51:91-97.
112. Wan S, Wright D, Coveney PV. Mechanism of drug efficacy within the epidermal growth factor receptor revealed by microsecond molecular dynamics simulation. Molecular Cancer Therapeutics 2012.