T-cell epitope prediction and immune complex simulation using molecular dynamics: State of the art and persisting challenges

Immunome Research

Volumn 6, Issue SUPPL. 2, 2010, Pages

  Flower, Darren R ^a   Phadwal, Kanchan ^b   MacDonald, Isabel K ^c   Coveney, Peter V ^d   Davies, Matthew N ^e   Wan, Shunzhou ^d  

^a ASTON UNIVERSITY   (United Kingdom)

^b JOHN RADCLIFFE HOSPITAL   (United Kingdom)

^c NOTTINGHAM CITY HOSPITAL   (United Kingdom)

^d UNIVERSITY COLLEGE LONDON   (United Kingdom)

^e KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EPITOPE; T LYMPHOCYTE RECEPTOR;

AB INITIO CALCULATION; ANTIGEN ANTIBODY COMPLEX; BIOINFORMATICS; IMMUNE SYNAPSE; IMMUNOGENICITY; IMMUNOINFORMATICS; MAJOR HISTOCOMPATIBILITY COMPLEX; MOLECULAR DYNAMICS; MOLECULAR MODEL; MONTE CARLO METHOD; NEUROIMMUNOLOGY; QUANTUM MECHANICS; REVIEW; T LYMPHOCYTE; THERMODYNAMICS;

EID: 78149316174     PISSN: None     EISSN: 17457580     Source Type: Journal    
DOI: 10.1186/1745-7580-6-S2-S4     Document Type: Review

References (151)

1. Immunoinformatics and modeling perspective of T cell epitope-based cancer immunotherapy: a holistic picture. S Mishra S Sinha, J Biomol Struct Dyn 2009 27 3 293 306 19795913
2. Immunoinformatics: Current trends and future directions. JC Tong EC Ren, Drug Discov Today 2009 14 13-14 684 9 10.1016/j.drudis.2009.04.001 19379830
3. Immunoinformatics and the in silico prediction of immunogenicity. An introduction. DR Flower, Methods Mol Biol 2007 409 1 15 full-text 18449989
4. Recent advances in immunoinformatics: application of in silico tools to drug development. MC Evans, Curr Opin Drug Discov Devel 2008 11 2 233 41 18283611
5. Towards in silico prediction of immunogenic epitopes. DR Flower, Trends Immunol 2003 24 12 667 74 10.1016/j.it.2003.10.006 14644141
6. Immunoinformatics comes of age. B Korber M LaBute K Yusim, PLoS Comput Biol 2006 2 6 71 10.1371/journal.pcbi.0020071 16846250
7. Bioinformatics for Vaccinology. D Flower, Wiley 1 2008
8. Harnessing bioinformatics to discover new vaccines. MN Davies DR Flower, Drug Discov Today 2007 12 9-10 389 95 10.1016/j.drudis.2007.03.010 17467575
9. Immunomics: discovering new targets for vaccines and therapeutics. AS De Groot, Drug Discov Today 2006 11 5-6 203 9 10.1016/S1359-6446(05)03720-7 16580597
10. From genome to vaccine - new immunoinformatics tools for vaccine design. AS De Groot JA Berzofsky, Methods 2004 34 4 425 8 10.1016/j.ymeth.2004.06.004 15542367
11. Genome-based approaches to develop vaccines against bacterial pathogens. D Serruto,, et al. Vaccine 2009 27 25-26 3245 50 10.1016/j.vaccine.2009.01.072 19200820
12. Vaccinology in the genome era. CD Rinaudo,, et al. J Clin Invest 2009 119 9 2515 25 10.1172/JCI38330 19729849
13. Genome-based vaccine development: a short cut for the future. DG Moriel,, et al. Adv Exp Med Biol 2009 655 81 9 full-text 20047037
14. The use of genomics in microbial vaccine development. S Bambini R Rappuoli, Drug Discov Today 2009 14 5-6 252 60 10.1016/j.drudis.2008.12.007 19150507
15. Vaccines in the era of genomics: the pneumococcal challenge. MA Barocchi S Censini R Rappuoli, Vaccine 2007 25 16 2963 73 10.1016/j.vaccine.2007.01.065 17324490
16. Microbial genomes and vaccine design: refinements to the classical reverse vaccinology approach. M Mora,, et al. Curr Opin Microbiol 2006 9 5 532 6 10.1016/j.mib.2006.07.003 16890009
17. Post-genomic vaccine development. D Serruto R Rappuoli, FEBS Lett 2006 580 12 2985 92 10.1016/j.febslet.2006.04.084 16716781
18. Statistical comparison of established T-cell epitope predictors against a large database of human and murine antigens. AJ Deavin TR Auton PJ Greaney, Mol Immunol 1996 33 2 145 55 10.1016/0161-5890(95)00120-4 8649436
19. A community resource benchmarking predictions of peptide binding to MHC-I molecules. B Peters,, et al. PLoS Comput Biol 2006 2 6 65 10.1371/journal.pcbi. 0020065 16789818
20. Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. HH Lin,, et al. BMC Immunol 2008 9 8 10.1186/1471-2172-9-8 18366636
21. On evaluating MHC-II binding peptide prediction methods. Y El-Manzalawy D Dobbs V Honavar, PLoS One 2008 3 9 3268 10.1371/journal.pone.0003268 18813344
22. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. HH Lin,, et al. BMC Bioinformatics 2008 9 Suppl 12 22 10.1186/1471-2105-9-S12-S22 19091022
23. In silico tools for predicting peptides binding to HLA-class II molecules: more confusion than conclusion. U Gowthaman JN Agrewala, J Proteome Res 2008 7 1 154 63 10.1021/pr070527b 18034454
24. Antibody-protein interactions: benchmark datasets and prediction tools evaluation. JV Ponomarenko PE Bourne, BMC Struct Biol 2007 7 64 10.1186/1472-6807-7-64 17910770
25. Benchmarking B cell epitope prediction: underperformance of existing methods. MJ Blythe DR Flower, Protein Sci 2005 14 1 246 8 10.1110/ps.041059505 15576553
26. Molecular modelling : principles and applications. AR Leach, Harlow: Prentice Hall 2 2001 xiv 744
27. Molecular Mechanics - the Mm3 Force-Field for Hydrocarbons.1. NL Allinger YH Yuh JH Lii, Journal of the American Chemical Society 1989 111 23 8551 8566 10.1021/ja00205a001 (Pubitemid 20021795)
28. The GROMOS software for biomolecular simulation: GROMOS05. M Christen,, et al. J Comput Chem 2005 26 16 1719 51 10.1002/jcc.20303 16211540
29. New-generation amber united-atom force field. LJ Yang,, et al. Journal of Physical Chemistry B 2006 110 26 13166 13176 10.1021/jp060163v (Pubitemid 44115654)
30. Development and testing of a general amber force field. JM Wang,, et al. Journal of Computational Chemistry 2004 25 9 1157 1174 10.1002/jcc.20035 15116359
31. Amber - Assisted Model-Building with Energy Refinement - a General Program for Modeling Molecules and Their Interactions. PK Weiner PA Kollman, Journal of Computational Chemistry 1981 2 3 287 303 10.1002/jcc.540020311
32. CHARMM: the biomolecular simulation program. BR Brooks,, et al. J Comput Chem 2009 30 10 1545 614 10.1002/jcc.21287 19444816
33. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. K Vanommeslaeghe,, et al. J Comput Chem 2010 31 4 671 90 19575467
34. Computational evaluation of protein-small molecule binding. O Guvench AD MacKerell Jr., Curr Opin Struct Biol 2009 19 1 56 61 10.1016/j.sbi.2008.11.009 19162472
35. Rapid non-empirical approaches for estimating relative binding free energies. AE Mark,, et al. Acta Biochim Pol 1995 42 4 525 35 8852343
36. Protocol for MM/PBSA molecular dynamics simulations of proteins. F Fogolari A Brigo H Molinari, Biophysical Journal 2003 85 1 159 166 10.1016/S0006-3495(03)74462-2 12829472
37. How to obtain statistically converged MM/GBSA results. S Genheden U Ryde, J Comput Chem 2010 31 4 837 46 19598265
38. Usefulness and limitations of normal mode analysis in modeling dynamics of biomolecular complexes. J Ma, Structure 2005 13 3 373 80 10.1016/j.str.2005. 02.002 15766538
39. Computational techniques for efficient conformational sampling of proteins. A Liwo,, et al. Curr Opin Struct Biol 2008 18 2 134 9 18215513
40. Peptide binding to MHC class I and II proteins: new avenues from new methods. R Yaneva,, et al. Mol Immunol 2010 47 4 649 57 10.1016/j.molimm.2009. 10.008 19910050
41. Structural principles that govern the peptide-binding motifs of class I MHC molecules. C Zhang A Anderson C DeLisi, J Mol Biol 1998 281 5 929 47 10.1006/jmbi.1998.1982 9719645
42. Consistency in structural energetics of protein folding and peptide recognition. C Zhang JL Cornette C Delisi, Protein Sci 1997 6 5 1057 64 10.1002/pro.5560060512 9144777
43. Computational determination of side chain specificity for pockets in class I MHC molecules. G Vasmatzis,, et al. Mol Immunol 1996 33 16 1231 9 10.1016/S0161-5890(96)00090-9 9129159
44. TcR recognition of the MHC-peptide dimer: structural properties of a ternary complex. G Vasmatzis,, et al. J Mol Biol 1996 261 1 72 89 10.1006/jmbi.1996.0442 8760503
45. Free energy mapping of class I MHC molecules and structural determination of bound peptides. U Sezerman S Vajda C DeLisi, Protein Sci 1996 5 7 1272 81 10.1002/pro.5560050706 8819160
46. Prediction of protein complexes using empirical free energy functions. Z Weng S Vajda C Delisi, Protein Sci 1996 5 4 614 26 10.1002/pro.5560050406 8845751
47. Flexible docking of peptides to class I major-histocompatibility-complex receptors. R Rosenfeld,, et al. Genet Anal 1995 12 1 1 21 7648466
48. Effect of conformational flexibility and solvation on receptor-ligand binding free energies. S Vajda,, et al. Biochemistry 1994 33 47 13977 88 10.1021/bi00251a004 7947806
49. Computing the structure of bound peptides. Application to antigen recognition by class I major histocompatibility complex receptors. R Rosenfeld,, et al. J Mol Biol 1993 234 3 515 21 10.1006/jmbi.1993.1607 8254656
50. Toward computational determination of peptide-receptor structure. U Sezerman,, et al. Protein Sci 1993 2 11 1827 43 10.1002/pro.5560021105 7505681
51. Role of conserved regions of class I MHC molecules in the activation of CD8+ cytotoxic T lymphocytes by peptide and purified cell-free class I molecules. T Takeshita,, et al. Int Immunol 1993 5 9 1129 38 10.1093/intimm/5.9.1129 8241055
52. Graphical representations of the class I MHC cleft. JL Cornette,, et al. J Mol Graph 1993 11 3 174 9 187 10.1016/0263-7855(93)80069-4 8110663
53. An HLA-B27 polymorphism (B*2710) that is critical for T-cell recognition has limited effects on peptide specificity. F Garcia,, et al. Tissue Antigens 1998 51 1 1 9 10.1111/j.1399-0039.1998.tb02941.x 9459498
54. Fine specificity of antigen binding to two class I major histocompatibility proteins (B*2705 and B*2703) differing in a single amino acid residue. D Rognan,, et al. J Comput Aided Mol Des 1997 11 5 463 78 10.1023/A:1007963901092 9385550
55. Molecular dynamics and structure-based drug design for predicting non-natural nonapeptide binding to a class I MHC protein. L Scapozza,, et al. Acta Crystallogr D Biol Crystallogr 1995 51 Pt 4 541 9 10.1107/S0907444995002678 15299842
56. Rational design of nonnatural peptides as high-affinity ligands for the HLA-B*2705 human leukocyte antigen. D Rognan,, et al. Proc Natl Acad Sci U S A 1995 92 3 753 7 10.1073/pnas.92.3.753 7846047
57. Molecular dynamics simulation of MHC-peptide complexes as a tool for predicting potential T cell epitopes. D Rognan,, et al. Biochemistry 1994 33 38 11476 85 10.1021/bi00204a009 7522551
58. Molecular modeling of an antigenic complex between a viral peptide and a class I major histocompatibility glycoprotein. D Rognan,, et al. Proteins 1992 13 1 70 85 10.1002/prot.340130107 1594579
59. Monte Carlo docking of oligopeptides to proteins. A Caflisch P Niederer M Anliker, Proteins 1992 13 3 223 30 10.1002/prot.340130305 1603811
60. Selection of peptides that bind to the HLA-A2.1 molecule by molecular modelling. JS Lim,, et al. Mol Immunol 1996 33 2 221 30 10.1016/0161-5890(95) 00065-8 8649443
61. A predictive method for the evaluation of peptide binding in pocket 1 of HLA-DRB1 via global minimization of energy interactions. IP Androulakis,, et al. Proteins 1997 29 1 87 102 10.1002/(SICI)1097-0134(199709)29:1<87::AID- PROT7>3.0.CO;2-C 9294869
62. Model for the peptide-free conformation of class II MHC proteins. CA Painter,, et al. PLoS One 2008 3 6 2403 10.1371/journal.pone.0002403 18545669
63. Conformational flexibility of the MHC class I alpha1-alpha2 domain in peptide bound and free states: a molecular dynamics simulation study. M Zacharias S Springer, Biophys J 2004 87 4 2203 14 10.1529/biophysj.104.044743 15454423
64. Flexibility of the MHC class II peptide binding cleft in the bound, partially filled, and empty states: a molecular dynamics simulation study. R Yaneva S Springer M Zacharias, Biopolymers 2009 91 1 14 27 10.1002/bip.21078 18767126
65. On the calculation of binding free energies using continuum methods: application to MHC class I protein-peptide interactions. N Froloff A Windemuth B Honig, Protein Sci 1997 6 6 1293 301 10.1002/pro.5560060617 9194189
66. Solvation energy density occlusion approximation for evaluation of desolvation penalties in biomolecular interactions. N Arora D Bashford, Proteins 2001 43 1 12 27 10.1002/1097-0134(20010401)43:1<12::AID-PROT1013>3.0. CO;2-7 11170210
67. 3-Layer-based analysis of peptide-MHC interaction: in silico prediction, peptide binding affinity and T cell activation in a relevant allergen-specific model. B Knapp,, et al. Mol Immunol 2009 46 8-9 1839 44 10.1016/j.molimm.2009. 01.009 19232439
68. Binding free energy differences in a TCR-peptide-MHC complex induced by a peptide mutation: a simulation analysis. O Michielin M Karplus, J Mol Biol 2002 324 3 547 69 10.1016/S0022-2836(02)00880-X 12445788
69. Modeling of the TCR-MHC-peptide complex. O Michielin I Luescher M Karplus, J Mol Biol 2000 300 5 1205 35 10.1006/jmbi.2000.3788 10903865
70. Protein-protein interaction investigated by steered molecular dynamics: the TCR-pMHC complex. MA Cuendet O Michielin, Biophys J 2008 95 8 3575 90 10.1529/biophysj.108.131383 18621828
71. Mechanical signaling on the single protein level studied using steered molecular dynamics. GZ Genchev,, et al. Cell Biochem Biophys 2009 55 3 141 52 10.1007/s12013-009-9064-5 19669741
72. Targeted Molecular-Dynamics - a New Approach for Searching Pathways of Conformational Transitions. J Schlitter M Engels P Kruger, Journal of Molecular Graphics 1994 12 2 84 89 10.1016/0263-7855(94)80072-3 7918256
73. Targeted Molecular-Dynamics Simulation of Conformational Change - Application to the T[ - ]R Transition in Insulin. J Schlitter,, et al. Molecular Simulation 1993 10 2-6 291 & 10.1080/08927029308022170
74. The T-Reversible-Arrow-R Structural Transition of Insulin - Pathways Suggested by Targeted Energy Minimization. M Engels,, et al. Protein Engineering 1992 5 7 669 677 10.1093/protein/5.7.669 1480621
75. Ligand binding: Molecular mechanics calculation of the streptavidin biotin rupture force. H Grubmuller B Heymann P Tavan, Science 1996 271 5251 997 999 10.1126/science.271.5251.997 8584939
76. Theory, analysis, and interpretation of single-molecule force spectroscopy experiments. OK Dudko G Hummer A Szabo, Proceedings of the National Academy of Sciences of the United States of America 2008 105 41 15755 15760 10.1073/pnas.0806085105 18852468
77. Extracting kinetics from single-molecule force spectroscopy: Nanopore unzipping of DNA hairpins. OK Dudko,, et al. Biophysical Journal 2007 92 12 4188 4195 10.1529/biophysj.106.102855 17384066
78. Theory and single molecule experiments. I Gopich G Hummer A Szabo, Abstracts of Papers of the American Chemical Society 2003 226 287 U287
79. Kinetics from nonequilibrium single-molecule pulling experiments. G Hummer A Szabo, Biophysical Journal 2003 85 1 5 15 10.1016/S0006-3495(03)74449-X 12829459
80. Free energy reconstruction from nonequilibrium single-molecule pulling experiments. G Hummer A Szabo, Proceedings of the National Academy of Sciences of the United States of America 2001 98 7 3658 3661 10.1073/pnas.071034098 11274384
81. Calculating potentials of mean force from steered molecular dynamics simulations. S Park K Schulten, Journal of Chemical Physics 2004 120 13 5946 5961 10.1063/1.1651473 15267476
82. Free energy calculation from steered molecular dynamics simulations using Jarzynski's equality. S Park,, et al. Journal of Chemical Physics 2003 119 6 3559 3566 10.1063/1.1590311
83. Large-scale molecular dynamics simulations of HLA-A*0201 complexed with a tumor-specific antigenic peptide: can the alpha3 and beta2m domains be neglected? S Wan P Coveney DR Flower, J Comput Chem 2004 25 15 1803 13 10.1002/jcc.20100 15386470
84. Peptide recognition by the T cell receptor: comparison of binding free energies from thermodynamic integration, Poisson-Boltzmann and linear interaction energy approximations. S Wan PV Coveney DR Flower, Philos Transact A Math Phys Eng Sci 2005 363 1833 2037 53 10.1098/rsta.2005.1627 16099765
85. Molecular basis of peptide recognition by the TCR: affinity differences calculated using large scale computing. S Wan PV Coveney DR Flower, J Immunol 2005 175 3 1715 23 16034112
86. Molecular dynamics simulations: bring biomolecular structures alive on a computer. S Wan PV Coveney DR Flower, Methods Mol Biol 2007 409 321 39 full-text 18450012
87. Toward an atomistic understanding of the immune synapse: large-scale molecular dynamics simulation of a membrane-embedded TCR-pMHC-CD4 complex. S Wan DR Flower PV Coveney, Mol Immunol 2008 45 5 1221 30 10.1016/j.molimm.2007.09. 022 17980430
88. T cells as a self-referential, sensory organ. MM Davis,, et al. Annu Rev Immunol 2007 25 681 95 10.1146/annurev.immunol.24.021605.090600 17291190
89. How T cells 'see' antigen. M Krogsgaard MM Davis, Nat Immunol 2005 6 3 239 45 10.1038/ni1173 15716973
90. Dynamics of cell surface molecules during T cell recognition. MM Davis,, et al. Annu Rev Biochem 2003 72 717 42 10.1146/annurev.biochem.72.121801.161625 14527326
91. Direct observation of ligand recognition by T cells. DJ Irvine,, et al. Nature 2002 419 6909 845 9 10.1038/nature01076 12397360
92. The immunological synapse: a molecular machine controlling T cell activation. A Grakoui,, et al. Science 1999 285 5425 221 7 10.1126/science.285. 5425.221 10398592
93. The immunological synapse balances T cell receptor signaling and degradation. KH Lee,, et al. Science 2003 302 5648 1218 22 10.1126/science. 1086507 14512504
94. Synaptic pattern formation during cellular recognition. SY Qi JT Groves AK Chakraborty, Proc Natl Acad Sci U S A 2001 98 12 6548 53 10.1073/pnas. 111536798 11371622
95. Pattern formation during T-cell adhesion. TR Weikl R Lipowsky, Biophys J 2004 87 6 3665 78 10.1529/biophysj.104.045609 15377531
96. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules (vol 117, pg 5179, 1995). WD Cornell,, et al. Journal of the American Chemical Society 1996 118 9 2309 2309 10.1021/ja955032e
97. A 2nd Generation Force-Field for the Simulation of Proteins, Nucleic-Acids, and Organic-Molecules. WD Cornell,, et al. Journal of the American Chemical Society 1995 117 19 5179 5197 10.1021/ja00124a002
98. Comparison of protein force fields for molecular dynamics simulations. O Guvench AD MacKerell Jr., Methods Mol Biol 2008 443 63 88 full-text 18446282
99. All-atom fast protein folding simulations: The villin headpiece. MY Shen KF Freed, Proteins-Structure Function and Genetics 2002 49 4 439 445 10.1002/prot.10230 (Pubitemid 35388130)
100. Implementation of NAMD molecular dynamics non-bonded force-field on the Cell Broadband Engine processor. G Shi V Kindratenko, 2008 Ieee International Symposium on Parallel & Distributed Processing 2008 1-8 2985 2992 3789
101. Scalable molecular dynamics with NAMD. JC Phillips,, et al. Journal of Computational Chemistry 2005 26 16 1781 1802 10.1002/jcc.20289 16222654
102. NAMD: A parallel, object oriented molecular dynamics program. MT Nelson,, et al. International Journal of Supercomputer Applications and High Performance Computing, 1996 10 4 251 268 10.1177/109434209601000401
103. Human telomeric G-DNA - A test example for force field adjustment. E Fadrna,, et al. Journal of Biomolecular Structure & Dynamics 2007 24 6 709 709
104. Determination of electrostatic parameters for a polarizable force field based on the classical Drude oscillator. VM Anisimov,, et al. Journal of Chemical Theory and Computation 2005 1 1 153 168 10.1021/ct049930p
105. Isothermal titration calorimetry in drug discovery. WH Ward GA Holdgate, Prog Med Chem 2001 38 309 76 full-text 11774798
106. Isothermal titration calorimetry: experimental design, data analysis, and probing macromolecule/ligand binding and kinetic interactions. MW Freyer EA Lewis, Methods Cell Biol 2008 84 79 113 full-text 17964929
107. Experimental approaches to evaluate the thermodynamics of protein-drug interactions. WF de Azevedo Jr. R Dias, Curr Drug Targets 2008 9 12 1071 6 10.2174/138945008786949441 19128217
108. Receptor-ligand binding sites and virtual screening. CK Hattotuwagama MN Davies DR Flower, Curr Med Chem 2006 13 11 1283 304 10.2174/092986706776873005 16712470
109. Predicting binding affinities of protein ligands from three-dimensional models: Application to peptide binding to class I major histocompatibility proteins. D Rognan,, et al. Journal of Medicinal Chemistry 1999 42 22 4650 4658 10.1021/jm9910775 10579827
110. Customized versus universal scoring functions: Application to class I MHC-peptide binding free energy predictions. A Logean A Sette D Rognan, Bioorganic & Medicinal Chemistry Letters 2001 11 5 675 679 11266167
111. A structure-based algorithm to predict potential binding peptides to MHC molecules with hydrophobic binding pockets. Y Altuvia,, et al. Hum Immunol 1997 58 1 1 11 10.1016/S0198-8859(97)00210-3 9438204
112. Ranking potential binding peptides to MHC molecules by a computational threading approach. Y Altuvia O Schueler H Margalit, J Mol Biol 1995 249 2 244 50 10.1006/jmbi.1995.0293 7540211
113. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. S Miyazawa RL Jernigan, J Mol Biol 1996 256 3 623 44 10.1006/jmbi.1996.0114 8604144
114. Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. O Schueler-Furman,, et al. Protein Sci 2000 9 9 1838 46 10.1110/ps.9.9.1838 11045629
115. Static energy analysis of MHC class I and class II peptide-binding affinity. MN Davies DR Flower, Methods Mol Biol 2007 409 309 20 full-text 18450011
116. In silico characterization of immunogenic epitopes presented by HLA-Cw*0401. JC Tong,, et al. Immunome Res 2007 3 7 10.1186/1745-7580-3-7 17705876
117. A critical cross-validation of high throughput structural binding prediction methods for pMHC. B Knapp,, et al. J Comput Aided Mol Des 2009 23 5 301 7 10.1007/s10822-009-9259-2 19194661
118. Limitations of Ab initio predictions of peptide binding to MHC class II molecules. H Zhang,, et al. PLoS One 2010 5 2 9272 10.1371/journal.pone.0009272 20174654
119. Automated molecular simulation based binding affinity calculator for ligand-bound HIV-1 proteases. SK Sadiq,, et al. J Chem Inf Model 2008 48 9 1909 19 10.1021/ci8000937 18710212
120. Ground-States of Molecules.38. Mndo Method - Approximations and Parameters. MJS Dewar W Thiel, Journal of the American Chemical Society 1977 99 15 4899 4907 10.1021/ja00457a004
121. Am1 - a New General-Purpose Quantum-Mechanical Molecular-Model (Vol 107, Pg 3902, 1985). MJS Dewar,, et al. Journal of the American Chemical Society 1993 115 12 5348 5348 10.1021/ja00065a080
122. The Development and Use of Quantum-Mechanical Molecular-Models.76. Am1 - a New General-Purpose Quantum-Mechanical Molecular-Model. MJS Dewar,, et al. Journal of the American Chemical Society 1985 107 13 3902 3909 10.1021/ja00299a024
123. Optimization of Parameters for Semiempirical Methods.1. Method. JJP Stewart, Journal of Computational Chemistry 1989 10 2 209 220 10.1002/jcc.540100208
124. Optimization of Parameters for Semiempirical Methods.2. Applications. JJP Stewart, Journal of Computational Chemistry 1989 10 2 221 264 10.1002/jcc.540100209
125. Linear-scaling quantum mechanical calculations of biological molecules: The divide-and-conquer approach. TS Lee JP Lewis WT Yang, Computational Materials Science 1998 12 3 259 277 10.1016/S0927-0256(98)00029-9
126. A linear-scaling quantum mechanical investigation of cytidine deaminase. JP Lewis,, et al. Journal of Computational Physics 1999 151 1 242 263 10.1006/jcph.1999.6219
127. Active species for the ground-state complex of cytidine deaminase: A linear-scaling quantum mechanical investigation. JP Lewis,, et al. Journal of the American Chemical Society 1998 120 22 5407 5410 10.1021/ja973522w (Pubitemid 28305305)
128. Quantum mechanical methods for large biomolecular systems: Applications in the study of the cytidine deaminase enzyme. JP Lewis,, et al. Biophysical Journal 1998 74 2 132 A132
129. Application of the PM6 method to modeling proteins. JJP Stewart, Journal of Molecular Modeling 2009 15 7 765 805 10.1007/s00894-008-0420-y 19066990
130. Application of an integrated MOZYME plus DFT method to pKa calculations for proteins. K Ohno,, et al. Chemical Physics Letters 2001 341 34 387 392 10.1016/S0009-2614(01)00499-7 (Pubitemid 33628245)
131. QM/MM: what have we learned, where are we, and where do we go from here? H Lin DG Truhlar, Theoretical Chemistry Accounts 2007 117 2 185 199 10.1007/s00214-006-0143-z
132. QM/MM study of epitope peptides binding to HLA-A*0201: the roles of anchor residues and water. Y Li,, et al. Chem Biol Drug Des 2009 74 6 611 8 10.1111/j.1747-0285.2009.00896.x 19843077
133. Long-timescale molecular dynamics simulations of protein structure and function. JL Klepeis,, et al. Current Opinion in Structural Biology 2009 19 2 120 127 10.1016/j.sbi.2009.03.004 19361980
134. High performance computing in biology: Multimillion atom simulations of nanoscale systems. KY Sanbonmatsu CS Tung, Journal of Structural Biology 2007 157 3 470 480 10.1016/j.jsb.2006.10.023 17187988
135. Molecular dynamics simulations of the complete satellite tobacco mosaic virus. PL Freddolino,, et al. Structure 2006 14 3 437 49 10.1016/j.str.2005.11. 014 16531228
136. Large-scale simulations of the ribosome. KY Sanbonmatsu CS Tung, Biophysical Journal 2004 86 1 415a 415a
137. Atomic-level structural and functional model of a bacterial photosynthetic membrane vesicle. MK Sener,, et al. Proc Natl Acad Sci U S A 2007 104 40 15723 8 10.1073/pnas.0706861104 17895378
138. Molecular anatomy of a trafficking organelle. S Takamori,, et al. Cell 2006 127 4 831 46 10.1016/j.cell.2006.10.030 17110340
139. Real science at the petascale. RS Saksena,, et al. Philos Transact A Math Phys Eng Sci 2009 367 1897 2557 71 10.1098/rsta.2009.0049 19451110
140. Bridging the gap between atomistic and coarse-grained models of polymers: Status and perspectives. J Baschnagel,, et al. Advances in Polymer Science: Viscoelasticity, Atomistic Models, Statistical Chemistry 2000 152 41 156 full-text
141. Toward an atomistic understanding of the immune synapse: Large-scale molecular dynamics simulation of a membrane-embedded TCR-pMHC-CD4 complex. S Wan DR Flower PV Coveney, Molecular Immunology 2008 45 5 1221 1230 10.1016/j.molimm.2007.09.022 17980430
142. Collective and single particle diffusion on surfaces. T Ala-Nissila R Ferrando SC Ying, Advances in Physics 2002 51 3 949 1078 10.1080/ 00018730110107902 (Pubitemid 34590045)
143. Modification of the Overlap Potential to Mimic a Linear Site-Site Potential. JG Gay BJ Berne, Journal of Chemical Physics 1981 74 6 3316 3319 10.1063/1.441483
144. Equilibrium thermodynamics of cell-cell adhesion mediated by multiple ligand-receptor pairs. D Coombs,, et al. Biophys J 2004 86 3 1408 23 10.1016/S0006-3495(04)74211-3 14990470
145. The immunological synapse as a novel therapeutic target. WA Teft J Madrenas, Curr Opin Investig Drugs 2006 7 11 1008 13 17117590
146. Molecular dynamics simulations of biomolecules. M Karplus JA McCammon, Nature Structural Biology 2002 9 9 646 52 10.1038/nsb0902-646 12198485
147. Bioinformatics for vaccinology. DR Flower, Chichester: Wiley 2008 ix 292
148. Prediction of T-cell epitope. H Tsurui T Takahashi, J Pharmacol Sci 2007 105 4 299 316 10.1254/jphs.CR0070056 18094522
149. In silico methods for predicting T-cell epitopes: Dr Jekyll or Mr Hyde? U Gowthaman JN Agrewala, Expert Rev Proteomics 2009 6 5 527 37 10.1586/epr.09.71 19811074
150. Large-scale molecular dynamics simulations of HLA-A*0201 complexed with a tumor-specific antigenic peptide: Can the alpha 3 and beta(2)m domains be neglected? SZ Wan P Coveney DR Flower, Journal of Computational Chemistry 2004 25 15 1803 1813 10.1002/jcc.20100 15386470
151. Molecular basis of peptide recognition by the TCR: Affinity differences calculated using large scale computing. SZ Wan PV Coveney DR Flower, J Immunol 2005 175 3 1715 1723 16034112