Drug design for protein kinases and phosphatases: Flexible-receptor docking, binding affinity and specificity, and drug-binding kinetics

Current Pharmaceutical Design

Volumn 19, Issue 26, 2013, Pages 4739-4754

  Wong, Chung F ^a   Bairy, Sneha ^a  

^a UNIVERSITY OF MISSOURI ST LOUIS   (United States)

Author keywords

Applicability domain of models; Drug binding kinetics; Inhibitor specificity; Multipleconformational model of protein kinases; Pathway simulation; Protein kinases; Protein phosphatases; Protein ligand docking pathways; QM MM PBSA

Indexed keywords

BALANOL; CYCLIC AMP DEPENDENT PROTEIN KINASE; CYCLIN DEPENDENT KINASE 2; FK 506 BINDING PROTEIN; PHOSPHOPROTEIN PHOSPHATASE; PROTEIN KINASE; SALICYLIC ACID DERIVATIVE;

BINDING AFFINITY; CRYSTAL STRUCTURE; DRUG BINDING; DRUG DESIGN; LIGAND BINDING; MOLECULAR DOCKING; MOLECULAR DYNAMICS; MOLECULAR MECHANICS; PRIORITY JOURNAL; PROTEIN CONFORMATION; PROTEIN INTERACTION; PROTEIN STRUCTURE; REVIEW; SIGNAL TRANSDUCTION; SIMULATION; STATIC ELECTRICITY; THERMODYNAMICS;

EID: 84881343069     PISSN: 13816128     EISSN: 18734286     Source Type: Journal    
DOI: 10.2174/1381612811319260006     Document Type: Review

References (156)

1. Winer ES, Ingham RR, Castillo JJ. PCI-32765: A novel Bruton's tyrosine kinase inhibitor for the treatment of lymphoid malignancies. Expert Opin on Investigat Drugs 2012; 21: 355-61.
2. Wei L, Wu J, Li G, Shi N. Cis-enone resorcylic acid lactones (RALs) as irreversible protein kinase inhibitors. Curr Pharm Des 2012; 18: 1186-98.
3. Breccia M, Alimena G. Second-generation tyrosine kinase inhibitors as first-line treatment strategy in newly diagnosed chronic phase chronic myeloid leukemia patients. Curr Cancer Drug Targets 2012; 12: 391-401.
4. Crawford JJ, Hoeflich KP, Rudolph J. P21-Activated kinase inhibitors: A patent review. Expert Opin on Therapeut Pat 2012; 22: 293-310.
5. Dasanu CA, Padmanabhan P, Clark Iii BA, Do C. Cardiovascular toxicity associated with small molecule tyrosine kinase inhibitors currently in clinical use. Expert Opin Drug Saf 2012; 11: 445-57.
6. Grzybowska-Izydorczyk O, Smolewski P. MTOR kinase inhibitors as a treatment strategy in hematological malignancies. Future Med Chem 2012; 4: 487-504.
7. Huang CH, Powers BC. The evolving role of maintenance therapy using epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) in the management of advanced non-small-cell lung cancer. Clinical Medicine Insights: Oncology 2012; 6: 137-47.
8. Kontzias A, Laurence A, Gadina M, O'Shea JJ. Kinase inhibitors in the treatment of immune-mediated disease. F1000 Medicine Reports 2012; 4: 5.
9. Kramer T, Lo Monte F, Göring S, Okala Amombo GM, Schmidt B. Small molecule kinase inhibitors for LRRK2 and their application to Parkinson's disease models. ACS Chem Neurosci 2012; 3: 151-60.
10. Liu J, Hu Y, Waller DL, Wang J, Liu Q. Natural products as kinase inhibitors. Nat Prod Rep 2012; 29: 392-403.
11. Meulenbeld HJ, Mathijssen RHJ, Verweij J, De Wit R, De Jonge MJA. Danusertib, an aurora kinase inhibitor. Expert Opin on Investigat Drugs 2012; 21: 383-93.
12. Tian B, Kaufman PL. Comparisons of actin filament disruptors and Rho kinase inhibitors as potential antiglaucoma medications. Expert Rev Ophthalmol 2012; 7: 177-87.
13. Hunter T. 1001 protein kinases redux--towards 2000. Semin Cell Biol 1994; 5: 367-76.
14. Sims P, Wong CF, McCammon JA. A computational model OF binding thermodynamics: the design OF CDK2 inhibitors. J Med Chem 2003; 46: 3314-25.
15. Toendel K, Wong CF, McCammon JA. Computational analysis of the interactions between the angiogenesis inhibitor PD173074 and fibroblast growth factor receptor 1. J Theor Comput Chem 2003; 2: 43-56.
16. Wong CF, Hünenberger PH, Akamine P, et al. Computational analysis of PKA-balanol interactions. J Med Chem 2001; 44: 1530-9.
17. Hanks S, Quinn AM. Protein kinase catalytic domain sequence database: Identification of conserved features of primary structure and classification of family members. Methods Enzymol 1991; 200: 38-62.
18. Andersen JN, Jansen PG, Echwald SM, et al. A genomic perspective on protein tyrosine phosphatases: gene structure, pseudogenes, and genetic disease linkage. FASEB J 2004; 18: 8-30.
19. Liao JJ. Molecular recognition of protein kinase binding pockets for design of potent and selective kinase inhibitors. J Med Chem 2007; 50: 409-24.
20. Aleksandrov A, Simonson T. Molecular dynamics simulations show that conformational selection governs the binding preferences of imatinib for several tyrosine kinases. J Biol Chem 2010; 285: 13807-15.
21. Namboodiri HV, Bukhtiyarova M, Ramcharan J, Karpusas M, Lee Y, Springman EB. Analysis of imatinib and sorafenib binding to p38 Compared with c-Abl and b-Raf provides structural insights for understanding the selectivity of inhibitors targeting the DFGout form of protein kinases. Biochemistry (Mosc) 2010; 49: 3611-8.
22. Bairy S, Wong CF. Influence of kinetics of drug binding on EGFR signaling: A comparative study of three EGFR signaling pathway models. Proteins: Struct Funct and Bioinform 2011; 79: 2491-504.
23. Huse M, Kuriyan J. The conformational plasticity of protein kinases. Cell 2002; 109: 275-82.
24. Kuglstatter A, Wong A, Tsing S, et al. Insights into the conformational flexibility of Bruton's tyrosine kinase from multiple ligand complex structures. Protein Sci 2011; 20: 428-36.
25. Engh RA, Bossemeyer D. Structural aspects of protein kinase control-role of conformational flexibility. Pharmacol Ther 2002; 93: 99-111.
26. Seifert MH, Breitenlechner CB, Bossemeyer D, Huber R, Holak TA, Engh RA. Phosphorylation and flexibility of cyclic-AMPdependent protein kinase (PKA) using (31)P NMR spectroscopy. Biochemistry (Mosc) 2002; 41: 5968-77.
27. Sours KM, Kwok SC, Rachidi T, et al. Hydrogen-exchange mass spectrometry reveals activation-induced changes in the conformational mobility of p38alpha MAP kinase. J Mol Biol 2008; 379: 1075-93.
28. Lee T, Hoofnagle AN, Resing KA, Ahn NG. Hydrogen exchange solvent protection by an ATP analogue reveals conformational changes in ERK2 upon activation. J Mol Biol 2005; 353: 600-12.
29. Hoofnagle AN, Stoner JW, Lee T, Eaton SS, Ahn NG. Phosphorylation-dependent changes in structure and dynamics in ERK2 detected by SDSL and EPR. Biophys J 2004; 86: 395-403.
30. Johnson DA, Akamine P, Radzio-Andzelm E, Madhusudan M, Taylor SS. Dynamics of cAMP-dependent protein kinase. Chem Rev 2001; 101: 2243-70.
31. Taylor SS, Radzio-Andzelm E, Madhusudan Cheng X, Ten Eyck L, Narayana N. Catalytic subunit of cyclic AMP-dependent protein kinase: structure and dynamics of the active site cleft. Pharmacol Ther 1999; 82: 133-41.
32. Huang Z, Wong CF. Docking flexible peptide to flexible protein by molecular dynamics using two implicit-solvent models: an evaluation in protein kinase and phosphatase systems. J Phys Chem B 2009; 113: 14343-54.
33. Cavasotto CN, Singh N. Docking and high throughput docking: Successes and the challenge of protein flexibility. Current Computer-Aided Drug Design 2008; 4: 221-34.
34. Totrov M, Abagyan R. Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 2008; 18: 178-84.
35. Huang Z, Wong CF, Wheeler RA. Flexible protein-flexible ligand docking with disrupted velocity simulated annealing. Proteins 2008; 71: 440-54.
36. Wong CF. Flexible ligand-flexible protein docking in protein kinase systems. BBA-Proteins and Proteomics 2008; 1784: 244-51.
37. Kamiya N, Yonezawa Y, Nakamura H, Higo J. Protein-inhibitor flexible docking by a multicanonical sampling: Native complex structure with the lowest free energy and a free-energy barrier distinguishing the native complex from the others. Proteins: Struct Funct Bioinform 2008; 70: 41-53.
38. Moitessier N, Englebienne P, Lee D, Lawandi J, Corbeil CR. Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br J Pharmacol 2008; 153: S7-S26.
39. Zhao Y, Sanner MF. FLIPDock: Docking flexible ligands into flexible receptors. Proteins: Struct Funct Bioinform 2007; 68: 726-37.
40. Alonso H, Bliznyuk AA, Gready JE. Combining docking and molecular dynamic simulations in drug design. Med Res Rev 2006; 26: 531-68.
41. Moitessier N, Therrien E, Hanessian S. A method for induced-fit docking, scoring, and ranking of flexible ligands. Application to peptidic and pseudopeptidic beta-secretase (BACE 1) inhibitors. J Med Chem 2006; 49: 5885-94.
42. Meiler J, Baker D. ROSETTALIGAND: Protein-small molecule docking with full side-chain flexibility. Proteins: Struct Funct Bioinform 2006; 65: 538-48.
43. Sherman W, Day T, Jacobson MP, Friesner RA, Farid R. Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 2006; 49: 534-53.
44. Mizutani MY, Takamatsu Y, Ichinose T, Nakamura K, Itai A. Effective handling of induced-fit motion in flexible docking. Proteins: Struct Funct Bioinform 2006; 63: 878-91.
45. Frembgen-Kesner T, Elcock AH. Computational sampling of a cryptic drug binding site in a protein receptor: Explicit solvent molecular dynamics and inhibitor docking to p38 MAP kinase. J Mol Biol 2006; 359: 202-14.
46. Ma XH, Li CH, Shen LZ, Gong XQ, Chen WZ, Wang CX. Biologically enhanced sampling geometric docking and backbone flexibility treatment with multiconformational superposition. Proteins 2005; 60: 319-23.
47. Taufer M, Crowley M, Price DJ, Chien AA, Brooks CL. Study of a highly accurate and fast protein-ligand docking method based on molecular dynamics. Concurrency and Computation-Practice & Experience 2005; 17: 1627-41.
48. Cavasotto CN, Kovacs JA, Abagyan RA. Representing receptor flexibility in ligand docking through relevant normal modes. J Am Chem Soc 2005; 127: 9632-40.
49. Ferrari AM, Wei BQQ, Costantino L, Shoichet BK. Soft docking and multiple receptor conformations in virtual screening. J Med Chem 2004; 47: 5076-84.
50. Taylor RD, Jewsbury PJ, Essex JW. FDS: Flexible ligand and receptor docking with a continuum solvent model and soft-core energy function. J Comput Chem 2003; 24: 1637-56.
51. Claussen H, Buning C, Rarey M, Lengauer T. FlexE: efficient molecular docking considering protein structure variations. J Mol Biol 2001; 308: 377-95.
52. Broughton HB. A method for including protein flexibility in protein-ligand docking: Improving tools for database mining and virtual screening. J Mol Graphics Model 2000; 18: 247-257+302-4.
53. Zacharias M, Sklenar H. Harmonic modes as variables to approximately account for receptor flexibility in ligand-receptor docking simulations: Application to DNA minor groove ligand complex. J Comput Chem 1999; 20: 287-300.
54. Leis S, Zacharias M. Efficient inclusion of receptor flexibility in grid-based protein-ligand docking. J Comput Chem 2011; 32: 3433-9.
55. Leach AR. Ligand docking to proteins with discrete side-chain flexibility. J Mol Biol 1994; 235: 345-56.
56. Lin JH, Perryman A, Schames J, McCammon JA. The relaxed complex method: accommodating receptor flexibility for drug design with an improved scoring scheme. Biopolymers 2003; 68: 47-62.
57. Lin JH, Perryman AL, Schames JR, McCammon JA. Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J Am Chem Soc 2002; 124: 5632-3.
58. Wong CF, Kua J, Zhang Y, Straatsma TP, McCammon JA. Molecular docking of balanol to dynamics snapshots of protein kinase A. Proteins 2005; 61: 850-8.
59. Hamelberg D, Mongan J, McCammon J. Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules. J Chem Phys 2004; 120: 11919-29.
60. Laio A, Parrinello M. Escaping free-energy minima. Proc Natl Acad Sci U S A 2002; 99: 12562-6.
61. Mitsutake A, Sugita Y, Okamoto Y. Generalized-ensemble algorithms for molecular simulations of biopolymers. Biopolymers 2001; 60: 96-123.
62. Sanbonmatsu KY, Garcia AE. Structure of Met-enkephalin in explicit aqueous solution using replica exchange molecular dynamics. Proteins: Struct Funct Genet 2002; 46: 225-34.
63. Huang Z, Wong CF. Conformational selection of protein kinase A revealed by flexible-ligand flexible-protein docking. J Comput Chem 2009; 30: 631-44.
64. Huang Z, Wong CF. A mining minima approach to exploring the docking pathways of p-nitrocatechol sulfate to YopH. Biophys J 2007; 93: 4141-50.
65. Huang Z, He Y, Zhang X, et al. Derivatives of salicylic acid as inhibitors of YopH in Yersinia pestis. Chem Biol Drug Des 2010; 76: 85-99.
66. Lee MS, Salsbury FR, Brooks CL. Novel generalized Born methods. J Chem Phys 2002; 116: 10606-14.
67. Lee MS, Feig M, Salsbury FR, Jr., Brooks CL, III. New analytical approximation to the standard molecular volume definition and its application to generalized born calculations. J Comput Chem 2003; 24: 1348-56.
68. Ferrara P, Gohlke H, Price DJ, Klebe G, Brooks CL. Assessing scoring functions for protein-ligand interactions. J Med Chem 2004; 47: 3032-47.
69. Kirkpatrick S, Gelatt CD, Jr., Vecchi MP. Optimization by Simulated Annealing. Science 1983; 220: 671-80.
70. Roux B, Simonson T. Implicit solvent models. Biophys Chem 1999; 78: 1-20.
71. Bashford D, Case DA. Generalized born models of macromolecular solvation effects. Annu Rev Phys Chem 2000; 51: 129-52.
72. Chen J, Brooks Iii CL, Khandogin J. Recent advances in implicit solvent-based methods for biomolecular simulations. Curr Opin Struct Biol 2008; 18: 140-8.
73. Feig M, Brooks C. Recent advances in the development and application of implicit solvent models in biomolecule simulations. Curr Opin Struct Biol 2004; 14: 217-24.
74. Gallicchio E, Levy RM. AGBNP: An analytic implicit solvent model suitable for molecular dynamics simulations and highresolution modeling. J Comput Chem 2004; 25: 479-99.
75. Feig M, Im W, Brooks CL, 3rd. Implicit solvation based on generalized Born theory in different dielectric environments. J Chem Phys 2004; 120: 903-11.
76. Lazaridis T, Karplus M. Effective energy function for proteins in solution. Proteins-Struct Funct Genet 1999; 35: 133-52.
77. Cramer CJ, Truhlar DG. Implicit solvation models: Equilibria, structure, spectra, and dynamics. Chem Rev 1999; 99: 2161-200.
78. Onufriev A, Bashford D, Case DA. Modification of the generalized Born model suitable for macromolecules. J Phys Chem B 2000; 104: 3712-20.
79. Dominy BN, Brooks CL. Development of a generalized born model parametrization for proteins and nucleic acids. J Phys Chem B 1999; 103: 3765-73.
80. Baker NA. Poisson-Boltzmann methods for biomolecular electrostatics. Methods Enzymol 2004; 383: 94-118.
81. Luo R, David L, Gilson MK. Accelerated Poisson-Boltzmann calculations for static and dynamic systems. J Comput Chem 2002; 23: 1244-53.
82. Honig B, Nicholls A. Classical Electrostatics in Biology and Chemistry. Science 1995; 268: 1144-9.
83. Warshel A, Sharma PK, Kato M, Parson WW. Modeling electrostatic effects in proteins. BBA-Proteins and Proteomics 2006; 1764: 1647-76.
84. Hassan SA, Guarnieri F, Mehler EL. A general treatment of solvent effects based on screened Coulomb potentials. J Phys Chem B 2000; 104: 6478-89.
85. Lin B, Wong KY, Hu C, Kokubo H, Pettitt BM. Fast calculations of electrostatic solvation free energy from reconstructed solvent density using proximal radial distribution functions. J Phys Chem Lett 2011; 2: 1626-32.
86. Rocchia W, Alexov E, Honig B. Extending the applicability of the nonlinear Poisson-Boltzmann equation: Multiple dielectric constants and multivalent ions. J Phys Chem B 2001; 105: 6507-14.
87. Warwicker J. Improved continuum electrostatic modelling in proteins, with comparison to experiment. J Mol Biol 1994; 236: 887-903.
88. Davis ME, Madura JD, Luty BA, McCammon JA. Electrostatics and diffusion of molecules in solution-simulations with the university-of-houston-brownian dynamics program. Comput Phys Commun 1991; 62: 187-97.
89. Madura JD, Briggs JM, Wade RC, et al. Electrostatics and diffusion of molecules in solution-simulations with the university of houston brownian dynamics program. Comput Phys Commun 1995; 91: 57-95.
90. Simonson T. Macromolecular electrostatics: continuum models and their growing pains. Curr Opin Struct Biol 2001; 11: 243-52.
91. Kollman PA, Massova I, Reyes C, et al. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 2000; 33: 889-97.
92. 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: 5221-30.
93. Massova I, Kollman PA. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect Drug Discov Des 2000; 18: 113-35.
94. Lee MR, Duan Y, Kollman PA. Use of MM-PB/SA in estimating the free energies of proteins: Application to native, intermediates, and unfolded villin headpiece. Proteins: Struct Funct Genet 2000; 39: 309-16.
95. Kuhn B, Kollman PA. Binding of a diverse set of ligands to avidin and streptavidin: An accurate quantitative prediction of their relative affinities by a combination of molecular mechanics and continuum solvent models. J Med Chem 2000; 43: 3786-91.
96. Zwanzig RW. High-temperature equation of state by perturbation method. I. nonpolar gases. J Chem Phys 1954; 22: 1420-26.
97. Rogers DJ, Tanimoto TT. A computer program for classifying plants. Science 1960; 132: 1115-8.
98. Zhou B, Wong CF, unpublished results.
99. Wang ML, Wong CF. Rank-ordering protein-ligand binding affinity by a quantum mechanics/molecular mechanics/Poisson-Boltzmann-surface area model. J Chem Phys 2007; 126: 026101.
100. Shi J, Lu Q, Zhang M, Wang M, Wong CF, Liu J. Supplementing the PBSA approach with quantum mechanics to study the binding between CDK2 and N2-substituted O6-cyclohexylmethoxyguanine inhibitors. J Theor Comput Chem 2010; 9: 543-59.
101. Soler JM, Artacho E, Gale JD, et al. The SIESTA method for ab initio order-N materials simulation. J Phys Conden Matter 2002; 14: 2745-79.
102. Briggs JM, Madura JD, Davis ME, et al. UHBD. Houston 1989.
103. Wang ML, Wong CF, Liu J, Zhang P. Efficient quantum mechanical calculation of solvation energies based on density functional theory, numerical atomic orbitals and Poisson-Boltzmann equation. Chem Phys Lett 2007; 442: 464-7.
104. Reddy MR, Erion MD. Relative solvation free energies calculated using an ab initio QM/MM-based free energy perturbation method: Dependence of results on simulation length. J Comput-Aided Mol Des 2009; 23: 837-43.
105. Rathore RS, Aparoy P, Reddanna P, Kondapi AK, Rami Reddy M. Minimum MD simulation length required to achieve reliable results in free energy perturbation calculations: Case study of relative binding free energies of fructose-1,6-bisphosphatase inhibitors. J Comput Chem 2011; 32: 2097-103.
106. Jorgensen WL. Free energy calculations: a breakthrough for modeling organic chemistry in solution. Acc Chem Res 1989; 22: 184-9.
107. Beveridge DL, DiCapua FM. Free energy via molecular simulation: applications to chemical and biomolecular systems. Annu Rev Biophys Biophys Chem 1989; 18: 431-92.
108. Mezei M, Beveridge DL. Free Energy Simulations. Ann Acad Sci NY 1986; 482: 1-23.
109. Bash PA, Singh UC, Langridge R, Kollman PA. Free energy calculation by computer simulation. Science 1987; 236: 564-8.
110. Pearlman DA, Kollman PA. A new method for carrying out free energy perturbation calculations: dynamically modified windows. J Chem Phys 1989; 90: 2460-70.
111. Lybrand TP. Computer simulation of biomolecular systems using molecular dynamics and free energy perturbation methods. Rev Comput Chem. VCH Publishers: New York 1990; pp. 295-320.
112. Straatsma TP. Free Energy by Molecular Simulation. Rev Comput Chem. VCH Publishers: New York 1996; pp. 81-127.
113. Reddy MR, Erion MD eds. Free Energy Calculations in Rational Drug Design. Kluwer Academic: New York 2001.
114. Simonson T, Archontis G, Karplus M. Free energy simulations come of age: protein-ligand recognition. Acc Chem Res 2002; 35: 430-7.
115. Min D, Yang W. Energy difference space random walk to achieve fast free energy calculations. J Chem Phys 2008; 128: 191102.
116. Zheng L, Carbone IO, Lugovskoy A, Berg BA, Yang W. A hybrid recursion method to robustly ensure convergence efficiencies in the simulated scaling based free energy simulations. J Chem Phys 2008; 129: 034105.
117. Hendrix DA, Jarzynski C. A "fast growth" method of computing free energy differences. J Chem Phys 2001; 114: 5974-81.
118. Jarzynski C. Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach. Phy Rev E 1997; 56: 5018-5035.
119. Zuckerman DM, Woolf TB. Overcoming finite-sampling errors in fast-switching free-energy estimates: extrapolative analysis of a molecular system. Chem Phys Lett 2002; 351: 445-53.
120. Sugita Y, Kitao A, Okamoto Y. Multidimensional replica-exchange method for free-energy calculations. J Chem Phys 2000; 113: 6042-51.
121. Sugita Y, Okamoto Y. Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 1999; 314: 141-51.
122. Sugita Y, Okamoto Y. Replica-exchange multicanonical algorithm and multicanonical replica-exchange method for simulating systems with rough energy landscape. Chem Phys Lett 2000; 329: 261-70.
123. Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993; 234: 779-815.
124. Tummino PJ, Copeland RA. Residence time of receptor-ligand complexes and its effect on biological function. Biochemistry (Mosc) 2008; 47: 5481-92.
125. Copeland RA, Pompliano DL, Meek TD. Drug-target residence time and its implications for lead optimization. Nat Rev Drug Discov 2006; 5: 730-9.
126. Zhang R, Monsma F. The importance of drug-target residence time. Curr Opin Drug Discov Devel 2009; 12: 488-96.
127. Swinney DC. The role of binding kinetics in therapeutically useful drug action. Curr Opin Drug Discov Devel 2009; 12: 31-9.
128. Swinney DC. Can binding kinetics translate to a clinically differentiated drug? From theory to practice. Lett Drug Design and Discov 2006; 3: 569-74.
129. Swinney DC. Biochemical mechanisms of New Molecular Entities (NMEs) approved by United States FDA during 2001-2004: Mechanisms leading to optimal efficacy and safety. Curr Top Med Chem 2006; 6: 461-78.
130. Swinney DC. Biochemical mechanisms of drug action: What does it take for success? Nature Reviews Drug Discovery 2004; 3: 801-8.
131. Lipton SA. Paradigm shift in neuroprotection by NMDA receptor blockade: memantine and beyond. Nat Rev Drug Discov 2006; 5: 160-70.
132. Kapur S, Seeman P. Does fast dissociation from the dopamine d(2) receptor explain the action of atypical antipsychotics?: A new hypothesis. Am J Psychiatry 2001; 158: 360-9.
133. Wu H, Pfarr DS, Tang Y, et al. Ultra-potent antibodies against respiratory syncytial virus: effects of binding kinetics and binding valence on viral neutralization. J Mol Biol 2005; 350: 126-44.
134. Berezov A, Zhang HT, Greene MI, Murali R. Disabling erbB receptors with rationally designed exocyclic mimetics of antibodies: structure-function analysis. J Med Chem 2001; 44: 2565-74.
135. Fuss H, Dubitzky W, Downes CS, Kurth MJ. SRC family kinases and receptors: analysis of three activation mechanisms by dynamic systems modeling. Biophys J 2008; 94: 1995-2006.
136. Wang Z, Zhang L, Sagotsky J, Deisboeck TS. Simulating nonsmall cell lung cancer with a multiscale agent-based model. Theor Biol Med Model 2007; 4: 50.
137. Hornberg JJ, Binder B, Bruggeman FJ, Schoeberl B, Heinrich R, Westerhoff HV. Control of MAPK signalling: from complexity to what really matters. Oncogene 2005; 24: 5533-42.
138. Sasagawa S, Ozaki Y, Fujita K, Kuroda S. Prediction and validation of the distinct dynamics of transient and sustained ERK activation. Nat Cell Biol 2005; 7: 365-73.
139. Orton RJ, Sturm OE, Vyshemirsky V, Calder M, Gilbert DR, Kolch W. Computational modelling of the receptor-tyrosinekinase-activated MAPK pathway. Biochem J 2005; 392: 249-61.
140. Kolch W, Calder M, Gilbert D. When kinases meet mathematics: the systems biology of MAPK signalling. FEBS Lett 2005; 579: 1891-5.
141. Suresh Babu CV, Yoon S, Nam HS, Yoo YS. Simulation and sensitivity analysis of phosphorylation of EGFR signal transduction pathway in PC12 cell model. Syst Biol (Stevenage) 2004; 1: 213-21.
142. Schoeberl B, Eichler-Jonsson C, Gilles ED, Muller G. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol 2002; 20: 370-5.
143. Goyal M, Rizzo M, Schumacher F, Wong CF. Beyond thermodynamics: drug binding kinetics could influence epidermal growth factor signaling. J Med Chem 2009; 52: 5582-5.
144. Frauenfelder H, Sligar SG, Wolynes PG. The Energy Landscapes and Motions of Proteins. Science 1991; 254: 1598-603.
145. Pan AC, Sezer D, Roux B. Finding transition pathways using the string method with swarms of trajectories. J Phys Chem B 2008; 112: 3432-40.
146. Elber R. A milestoning study of the kinetics of an allosteric transition: Atomically detailed simulations of deoxy Scapharca hemoglobin. Biophys J 2007; 92: L85-7.
147. Hu J, Ma A, Dinner AR. Bias annealing: A method for obtaining transition paths de novo. J Chem Phys 2006; 125: 114101.
148. Cardenas AE, Elber R. Kinetics of cytochrome C folding: Atomically detailed simulations. Proteins: Struct Funct Genet 2003; 51: 245-57.
149. Cardenas AE, Elber R. Atomically detailed Simulations of helix formation with the stochastic difference equation. Biophys J 2003; 85: 2919-39.
150. Bolhuis PG, Dellago C, Geissler PL, Chandler D. Transition path sampling: throwing ropes over mountains in the dark. J Phys Conden Matter 2000; 12: A147-52.
151. Henkelman G, Uberuaga BP, Jonsson H. A climbing image nudged elastic band method for finding saddle points and minimum energy paths. J Chem Phys 2000; 113: 9901-4.
152. Chu JW, Trout BL, Brooks BR. A super-linear minimization scheme for the nudged elastic band method. J Chem Phys 2003; 119: 12708-17.
153. Fischer S, Karplus M. Conjugate peak refinement-an algorithm for finding reaction paths and accurate transition-states in systems with many degrees of freedom. Chem Phys Lett 1992; 194: 252-61.
154. Birtwistle MR, Hatakeyama M, Yumoto N, Ogunnaike BA, Hoek JB, Kholodenko BN. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol Syst Biol 2007; 3: 144.
155. Guex N, Peitsch MC. SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 1997; 18: 2714-23.
156. Brooks BR, Brooks CL, 3rd, Mackerell AD, Jr., et al. CHARMM: the biomolecular simulation program. J Comput Chem 2009; 30: 1545-614.