Computational Approaches in Peptide and Protein Design: An Overview

Peptide and Protein Design for Biopharmaceutical Applications

Volumn , Issue , 2009, Pages 5-48

  Nikiforovich, Gregory V ^a   Marshall, Garland R ^b  

^a MolLife Design LLC   (United States)

^b WASHINGTON UNIVERSITY   (United States)

Author keywords

3D arrangement of functional groups of ligand and '3D pharmacophore'; Cambridge Structural Database (CSD), Protein Data Bank (PDB) and Swiss Prot TrEMBL; Critical assessment of structure predictions (CASP); CXCR4 G protein coupled receptor (GPCR) and cyclopentapeptide inhibitors; Energy estimations based on GROMOS or OPLS AA force fields; GROMOS and OPLS AA maps; Modern peptide and protein drug design problems structure based and target based design; Peptide and protein design computational approaches; QM MM and polarizable force fields in peptide and protein design; Routine MM force fields AMBER, OPLS, ECEPP

Indexed keywords

EID: 84890595282     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470749708.ch2     Document Type: Chapter

References (115)

1. H. Hu, M. Elstner and J. Hermans, Comparison of a QM/MM force field and molecular mechanics force fields in simulations of alanine and glycine 'dipeptides' (Ace-Ala-Nme and Ace-Gly-Nme) in water in relation to the problem of modeling the unfolded peptide backbone in solution, Proteins, 50, 451-463 (2003).
2. S. C. Lovell, I. W. Davis, W. B. Arendall, 3rd, P. I. de Bakker, J. M. Word, M. G. Prisant, J. S. Richardson and D. C. Richardson, Structure validation by Ca geometry: ', c and Cb deviation, Proteins, 50, 437-450 (2003).
3. G. V.Nikiforovich, Computationalmolecularmodeling in peptide design, Int. J. Peptide Protein Res., 44, 513-531 (1994).
4. G. R. Marshall, D. D. Beusen and G. V. Nikiforovich, Peptide conformation: Stability and dynamics, in Peptides. Synthesis, structures, and applications, B. Gutte (Ed), Academic Press, San Diego, 1995.
5. H. A. Scheraga, Predicting three-dimensional structures of oligopeptides, in Reviews in computational chemistry, K. B. Lipkowitz and D. B. Boyd (Eds), VCH Publishers, New York, 1992.
6. J. Va°benø, G. V. Nikiforovich and G. R. Marshall, A minimalistic 3D pharmacophore model for cyclopentapeptide CXCR4 antagonists, Biopolymers, 84, 459-471 (2006).
7. J. Va°benø, G. V. Nikiforovich and G. R. Marshall, Insight into the binding mode for cyclopentapeptide antagonists of the CXCR4 receptor, Chem. Biol.&Drug Design, 67, 346-354 (2006).
8. J. R. Deschamps, C. George and J. L. Flippen-Anderson, Structural studies of opioid peptides: A review of recent progress in x-ray diffraction studies, Biopolymers, 40, 121-139 (1996).
9. J. Drews, Drug discovery: A historical perspective, Science, 287, 1960-1964 (2000).
10. K.Palczewski,T.Kumasaka,T.Hori,C.A.Behnke,H.Motoshima,B.A.Fox, I.LeTrong, D. C.Teller, T.Okada, R. E. Stenkamp,M.Yamamoto andM.Miyano, Crystal structure of rhodopsin: AGprotein-coupled receptor, Science, 289, 739-745 (2000).
11. S. G. F. Rasmussen, H. Choi, J,, D. M. Rosenbaum, T. S. Kobilka, F. S. Thian, P. C. Edwards, M. Burghammer, V. R. P. Rathala, R. Sanishvili, R. F. Fischetti, G. F. Shertler, W. I. Weiss and B. K. Kobilka, Crystal structure of the human b2 adrenergic G-protein-coupled receptor, Nature, 450, 383-387 (2007).
12. V. Lafont, A. A. Armstrong, H. Ohtaka, Y. Kiso, L. Mario Amzel and E. Freire, Compensating enthalpic and entropic changes hinder binding affinity optimization, Chem. Biol. & Drug Design, 69, 413-422 (2007).
13. D. A. Pearlman, D. A. Case, J. W. Caldwell, W. S. Ross, T. E. Cheatham III, S. Debolt, D. Ferguson, G. Seibel and P. Kollman, AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis and free energy calculations to simulate the structural and energetic properties of molecules, Comp. Phys. Comm., 91, 1-141 (1995).
14. D. A. Case, T. E. Cheatham III, T. Darden, H. Gohlke, R. Luo, K. M. Mertz, A. Onufriev, C. Simmerling, B. Wang and R. J. Woods, The AMBER biomolecular simulation programs, J. Comp. Chem., 26, 1668-1688 (2005).
15. L. G. Dunfield, A. W. Burgess and H. A. Scheraga, Energy parameters in polypeptides. 8. Empirical potential energy algorithm for the conformational analysis of large molecules, J. Phys. Chem., 82, 2609-2616 (1978).
16. G. Nemethy, M. S. Pottle and H. A. Scheraga, Energy parameters in polypeptides. 9. Updating of geometrical parameters, nonbonded interactions, and hydrogen bond interactions for the naturally occuring aminoacids, J.PhysChem, 87, 1883-1887(1983).
17. G. Nemethy, K. D. Gibson, K. A. Palmer, C. N. Yoon, G. Paterlini, A. Zagari, S. Rumsey and H. A. Scheraga, Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides, J. Phys. Chem., 96, 6472-6484 (1992).
18. W. L. Jorgensen and J. Tirado-Rives, The OPLS potential functions for proteins. Energy minimizations for crystals of cyclic peptides and crambin, J. Am. Chem. Soc., 110, 1657-1666 (1988).
19. W. L. Jorgensen and J. Tirado-Rives, Potential energy functions for atomic-level simulations of water and organic and biomolecular systems, PNAS, 102, 6665- 6670 (2005).
20. W. R. P. Scott, P. H. Hunenberg, I. G. Tironi, A. E. Mark, S. R. Billeter, J. Fennen, A. E. Torda, T. Huber, P. Kruger and W. F. van Gunsteren, The GROMOS biomolecular simulation program package, J. Phys Chem, 103, 3596-3607 (1999).
21. B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan and M. Karplus, CHARMM: A program for macromolecular energy, minimization and dynamics calculations, J. Comp. Chem., 4, 187-217 (1983).
22. J. W. Ponder and D. A. Case, Force fields for protein simulations, Adv. Protein Chem., 66, 27-85 (2003).
23. A. D. Mackerell, Empirical force fields for biological macromolecules, J. Comp. Chem., 25, 1584-1604 (2004).
24. I. K. Roterman, M. H. Lambert, K. D. Gibson and H. A. Scheraga, A comparison of the CHARMM, AMBER and ECEPP potentials for peptides. II. '-c maps for N-acetyl alanine N0-methyl amide: Comparisons, contrasts and simple experimental tests, J. Biomol. Struct. & Dynamics, 7, 421-453 (1989).
25. A. D. Mackerell, M. Feig and B. R. Brooks, Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distrubutions in molecular dynamics simulations, J. Comp. Chem., 25, 1400-1415 (2004).
26. H. A. Scheraga, M. Khalili and A. Liwo, Protein-folding dynamics: Overview of molecular simulation techniques, Ann. Rev. Phys. Chem., 58, 57-83 (2007).
27. R. A. Friesner, Combined quantum and molecular mechanics (QM/MM), Drug Discovery Today: Technologies, 1, 253-260 (2004).
28. R. A. Friesner and V. Guallar, Ab initio quantum chemical and mixed quantum mechanics/molecular mechanics (QM/MM) methods for studying enzymatic catalysis, Ann. Rev. Phys. Chem., 56, 389-427 (2005).
29. M. Elstner, T. Frauenheim, E. Kaxiras, G. Seifert and S. Suhai, A self-consistent charge density-functional based tight-binding scheme for large biomolecules, Phys. Status Solidi, B217, 357-376 (2000).
30. G. M. Seabra, R. C. Walker, M. Elstner, D. A. Case and A. E. Roitberg, Implementation of the SCC-DFTB method for hybrid QM/MM simulations within the amber molecular dynamics package, J. Phys Chem, A111, 5655-5664 (2007).
31. S. Patel and B. R. Brooks, Fluctuating change force fields: Recent developments and applications from small molecules to macromolecular biological systems, Molecular Simulations, 32, 231-249 (2006).
32. Z.-X. Wang, W. Zhang, C. Wu, H. Lei, P. Cieplak and Y. Duan, Strike a balance: Optimization of backbone torsion parameters of amber polarizable force field for simulations of proteins and peptides, J. Comp. Chem., 27, 781-790 (2006).
33. P. Ferrara, H. Gohlke, D. J. Price, G. Klebe and C. L. Brooks, 3rd, Assessing scoring functions for protein-ligand interactions, J. Med. Chem., 47, 3032-3047 (2004).
34. S. F. Sousa, P. A. Fernandes and M. J. Ramos, Protein-ligand docking: Current status and future challenges, Proteins, 65, 15-26 (2006).
35. R. Rajamani and A. C. Good, Ranking poses in structure-based lead discovery and optimization: Current trends in scoring function development, Curr. Opin. Drug Discov. & Dev., 10, 308-315 (2007).
36. A. N. Jain, Scoring functions for protein-ligand docking, Curr. Prot.&Pept. Sci., 7, 407-420 (2006).
37. J. Fernandez-Recio, R. Abagyan and M. Totrov, Improving CAPRI predictions: Optimized desolvation for rigid-body docking, Proteins, 60, 308-313 (2005).
38. H. J. Böhm, The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure, J. Comp.-Aid. Mol. Design, 8, 243-256 (1994).
39. H. J. Böhm, Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs, J. Comp.-Aid. Mol. Design, 12, 309-323 (1998).
40. I. Muegge and Y. C. Martin, A general and fast scoring function for protein-ligand interactions: A simplified potential approach, J. Med. Chem., 42, 791-804 (1999).
41. I. Muegge, PMF scoring revisited, J. Med. Chem., 49, 5895-5902 (2006).
42. R. Teramoto and H. Fukunishi, Supervised consensus scoring for docking and virtual screening, J. Chem. Inf. & Model., 47, 526-534 (2007).
43. R. Wang, Y. Lu and S. Wang, Comparative evaluation of 11 scoring functions for molecular docking, J. Med. Chem., 46, 2287-2303 (2003).
44. E. Perola, W. P. Walters and P. S. Charifson, A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance, Proteins, 56, 235-249 (2004).
45. W. T. Mooij and M. L. Verdonk, General and targeted statistical potentials for protein-ligand interactions, Proteins, 61, 272-287 (2005).
46. D. Tobi and I. Bahar, Optimal design of protein docking potentials: Efficiency and limitations, Proteins, 62, 970-981 (2006).
47. C. Y. Yang, R. Wang and S. Wang, M-score: A knowledge-based potential scoring function accounting for protein atom mobility, J. Med. Chem., 49, 5903-5911 (2006).
48. J. Bernauer, J. Aze, J. Janin and A. Poupon, A new protein-protein docking scoring function based on interface residue properties, Bioinformatics, 23, 555-562 (2007).
49. W. Muller and H. Sticht, A protein-specifically adapted scoring function for the reranking of docking solutions, Proteins, 67, 98-111 (2007).
50. F. Fogolari, L. Pieri, A. Dovier, L. Bortolussi, G. Giugliarelli, A. Corazza, G. Esposito and P. Viglino, Scoring predictive models using a reduced representation of proteins: Model and energy definition, BMC Struct. Biol., 7, 15 (2007).
51. P. Ferrara, A. Curioni, E. Vangrevelinghe, T. Meyer, T. Mordasini, W. Andreoni, P. Acklin and E. Jacoby, New scoring functions for virtual screening from molecular dynamics simulations with a quantum-refined force-field (QRFF-MD). Application to cyclin-dependent kinase 2, J. Chem. Inf. & Model., 46, 254-263 (2006).
52. R. A. Dammkoehler, S. F. Karasek, E. F. Shands and G. R. Marshall, Constrained search of conformational hyperspace, J. Comp.-Aid. Mol. Design, 3, 3-21 (1989).
53. X. Zhang, G. V. Nikiforovich and G. R. Marshall, Conformational templates for rational drug design: Flexibility of cyclo(D-Pro1-Ala2-Ala3-Ala4-Ala5) in DMSO solution, J. Med. Chem., 50, 2921-2925 (2007).
54. N. Metropolis, R.A.E., M. N. Rosenbluth, A. H. Teller and E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys., 21, 1087-1092 (1953).
55. G. M. Torrie and J. P. Valleau, Nonphysical sampling distributions in Monte Carlo free-energy estimation - umbrella sampling, J. Comp. Chem., 23, 187-199 (1977).
56. Y. Sugita and Y. Okamoto, Replica-exchange molecular dynamics method for protein folding, Chem. Phys. Lett., 314, 141-151 (1999).
57. Y. M. Rhee and V. S. Pande, Multiplexed-replica exchange molecular dynamics method for protein folding simulation, Biophys. J., 84, 775-786 (2003).
58. M. R. Shirts and V. S. Pande, Screensavers of the world unite!, Science, 290, 1903-1904 (2001).
59. B. Zagrovic, E. J. Sorin and V. Pande, Beta-hairpin folding simulations in atomistic detail using an implicit solvent model, J. Mol. Biol., 313, 151-169 (2001).
60. B. Zagrovic, C. D. Snow, M. R. Shirts and V. S. Pande, Simulation of folding of a small alpha-helical protein in atomistic detail using worldwide-distributed computing, J. Mol. Biol., 323, 927-937 (2002).
61. K. Dill, S. B. Ozkan, T. R. Weikl, J. D. Chodera and V. A. Voelz, The protein folding problem: When it will be solved?, Curr. Opin. Struct Biol., 17, 342-346 (2007).
62. J. Moult, A decade of CASP: Progress, bottlenecks and prognosis in protein structure prediction, Curr. Opin. Struct Biol., 15, 285-289 (2005).
63. A. Sali and T. L. Blundell, Comparative protein modelling by satisfaction of spatial restraints, J. Mol. Biol., 234, 779-815 (1993).
64. A. Fiser and A. Sali, Modeller: Generation and refinement of homology-based protein structure models, Methods in Enzymology, 374, 461-491 (2003).
65. A. Fiser and A. Sali, Modloop: Automated modeling of loops in protein structures, Bioinformatics, 19, 2500-2501 (2003).
66. A. Godzik, Fold recognition methods, Methods of Biochemical Analysis, 44, 525-546 (2003).
67. R. T. Miller, D. T. Jones and J. M. Thornton, Protein fold recognition by sequence threading: Tools and assessment techniques, FASEB Journal, 10, 171- 178 (1996).
68. R. Day, D. A. Beck, R. S. Armen and V. Daggett, A consensus view of fold space: Combining SCOP, CATH, and the DALI domain dictionary, Protein Sci., 12, 2150-2160 (2003).
69. Y. Zhang and J. Skolnick, The protein structure prediction problem could be solved using the current PDB library, PNAS, 102, 1029-1034 (2005).
70. R. Casadio, P. Fariselli, P. L. Martelli and G. Tasco, Thinking the impossible: How to solve the protein folding problem with and without homologous structures and more, Methods in Molecular Biology, 350, 305-320 (2007).
71. H. Zhou and Y. Zhou, Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments, Proteins, 58, 321-328 (2005).
72. Y. Zhang, A. K. Arakaki and J. Skolnick, TASSER: An automated method for the prediction of protein tertiary structures inCASP6, Proteins, 61 Suppl. 7, 91-98 (2005).
73. Y. An and R. A. Friesner, A novel fold recognition method using composite predicted secondary structures, Proteins, 48, 352-366 (2002).
74. S. Wu, J. Skolnick and Y. Zhang, Ab initio modeling of small proteins by iterative tasser simulations, BMC Biology, 5, 17 (2007).
75. S. Wu and Y. Zhang, Lomets: A local meta-threading-server for protein structure prediction, Nucleic Acids Res., 35, 3375-3382 (2007).
76. L. Rychlewski and D. Fischer, Livebench-8: The large-scale, continuous assessment of automated protein structure prediction, Protein Sci., 14, 240-245 (2005).
77. C. A. Rohl, C. E. M. Strauss, K. M. S. Misura and D. Baker, Protein structure prediction using Rosetta, Methods in Enzymology, 383, 66-93 (2004).
78. Z. Li and H. A. Scheraga, Monte Carlo-minimization approach to the multipleminima problem in protein folding, PNAS, 84, 6611-6615 (1987).
79. C. A. Rohl, C. E. Strauss, D. Chivian and D. Baker, Modeling structurally variable regions in homologous proteins with Rosetta, Proteins, 55, 656-677 (2004).
80. C. Bystroff and D. Baker, Blind predictions of local protein structure in CASP2 targets using the i-sites library, Proteins, Suppl. 1, 167-171 (1997).
81. K. T. Simons, R. Bonneau, I. Ruczinski and D. Baker, Ab initio protein structure prediction of CASP III targets using Rosetta, Proteins, Suppl. 3, 171-176 (1999).
82. R. Bonneau, J. Tsai, I. Ruczinski, D. Chivian, C. Rohl, C. E. Strauss and D. Baker, Rosetta in CASP4: Progress in ab initio protein structure prediction, Proteins, Suppl. 5, 119-126 (2001).
83. P. Bradley, D. Chivian, J. Meiler, K. M. Misura, C. A. Rohl, W. R. Schief, W. J. Wedemeyer, O. Schueler-Furman, P. Murphy, J. Schonbrun, C. E. Strauss and D. Baker, Rosetta predictions in CASP5: Successes, failures, and prospects for complete automation, Proteins, 53 Suppl. 6, 457-468 (2003).
84. P. Bradley, L. Malmstrom, B. Qian, J. Schonbrun, D. Chivian, D. E. Kim, J. Meiler, K. M. Misura and D. Baker, Free modeling with Rosetta in CASP6, Proteins, 61 Suppl. 7, 128-134 (2005).
85. R. Bonneau, C. E. Strauss, C. A. Rohl, D. Chivian, P. Bradley, L. Malmstrom, T. Robertson and D. Baker, De novo prediction of three-dimensional structures for major protein families, J. Mol. Biol., 322, 65-78 (2002).
86. B.Kuhlman,G.Dantas,G.C. Ireton,G.Varani,B.L.Stoddardand D.Baker, Designofa novel globularprotein foldwith atomic-level accuracy, Science,302,1364-1368(2003).
87. G. L. Butterfoss and B. Kuhlman, Computer-based design of novel protein structures, Ann. Rev. Biophys. & Biomol. Struct., 35, 49-65 (2006).
88. J. Meiler and D. Baker, Rosettaligand: Protein-small molecule docking with full side-chain flexibility, Proteins, 65, 538-548 (2006).
89. O. Schueler-Furman, C. Wang and D. Baker, Progress in protein-protein docking: Atomic resolution predictions in the CAPRI experiment using Rosettadock with an improved treatment of side-chain flexibility, Proteins, 60, 187-194 (2005).
90. J. J. Gray, S. Moughon, C. Wang, O. Schueler-Furman, B. Kuhlman, C. A. Rohl and D. Baker, Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations, J. Mol. Biol., 331, 281-299 (2003).
91. J. Desmet, M. De Maeyer, B. Hazes and I. Lasters, The dead-end elimination theorem and its use in protein side-chain positioning, Nature, 356, 539-542 (1992).
92. A. R. Leach, Ligand docking to proteins with discrete side-chain flexibility, J. Mol. Biol., 235, 345-356 (1994).
93. M. De Maeyer, J. Desmet and I. Lasters, The dead-end elimination theorem: Mathematical aspects, implementation, optimizations, evaluation, and performance, Methods in Molecular Biology, 143, 265-304 (2000).
94. A. A. Canutescu, A. A. Shelenkov and R. L. Dunbrack, Jr., A graph-theory algorithm for rapid protein side-chain prediction, Protein Sci., 12, 2001-2014 (2003).
95. B. Kuhlman and D. Baker, Native protein sequences are close to optimal for their structures, PNAS, 97, 10383-10388 (2000).
96. C. Wang, O. Schueler-Furman and D. Baker, Improved side-chain modeling for protein-protein docking, Protein Sci., 14, 1328-1339 (2005).
97. M. J. Bower, F. E. Cohen and R. L. Dunbrack, Jr., Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: A new homology modeling tool, J. Mol. Biol., 267, 1268-1282 (1997).
98. R. L. Dunbrack, Jr., Comparative modeling of CASP3 targets using psi-blast and SCWRL, Proteins, Suppl. 3, 81-87 (1999).
99. G. V. Nikiforovich, V. J. Hruby, O. Prakash and C. A. Gehrig, Topographical requirements for delta-selective opioid peptides, Biopolymers, 31, 941-955 (1991).
100. C. C. Bleul, M. Farzan, H. Choe, C. Parolin, I. Clark-Lewis, J. Sodroski and T. A. Springer, The lymphocyte chemoattractant SDF-1 is a ligand for lestr/fusin and blocks HIV-1 entry, Nature, 382, 829-833 (1996).
101. E. Oberlin, A. Amara, F. Bachelerie, C. Bessia, J. L. Virelizier, F. Arenzana-Seisdedos, O. Schwartz, J. M. Heard, I. Clark-Lewis, D. F. Legler, M. Loetscher, M. Baggiolini and B. Moser, The CXC chemokine SDF-1 is the ligand for lestr/ fusin and prevents infection by t-cell-line-adapted HIV-1, Nature, 382, 833-835 (1996).
102. N. Fujii, S. Oishi, K. Hiramatsu, T. Araki, S. Ueda, H. Tamamura, A. Otaka, S. Kusano, S. Terakubo, H. Nakashima, J. A. Broach, J. O. Trent, Z. X. Wang and S. C. Peiper, Molecular-size reduction of a potent CXCR4-chemokine antagonist using orthogonal combination of conformation- and sequence-based libraries, Angew. Chem. Int. Ed., 42, 3251-3253 (2003).
103. H. Tamamura, T. Araki, S. Ueda, Z. Wang, S. Oishi, A. Esaka, J. O. Trent, H. Nakashima, N. Yamamoto, S. C. Peiper, A. Otaka and N. Fujii, Identification of novel low molecular weight CXCR4 antagonists by structural tuning of cyclic tetrapeptide scaffolds, J. Med. Chem., 48, 3280-3289 (2005).
104. H. Tamamura, A. Esaka, T. Ogawa, T. Araki, S. Ueda, Z. Wang, J. O. Trent, H. Tsutsumi, H. Masuno, H. Nakashima, N. Yamamoto, S. C. Peiper, A. Otaka and N. Fujii, Structure-activity relationship studies on CXCR4 antagonists having cyclic pentapeptide scaffolds, Org. & Biomol. Chem., 3, 4392-4394 (2005).
105. J. H. Viles, J. B. Mitchell, S. L. Gough, P. M. Doyle, C. J. Harris, P. J. Sadler and J. M. Thornton, Multiple solution conformations of the integrin-binding cyclic pentapeptide cyclo(-Ser-D-Leu-Asp-Val-Pro-). Analysis of the (', c) space available to cyclic pentapeptides, Eur. J. Biochem., 242, 352-362 (1996).
106. G. V. Nikiforovich, K. E. Kö vér, W. J. Zhang and G. R. Marshall, Cyclopentapeptides as flexible conformational templates, J. Am. Chem. Soc., 122, 3262-3273 (2000).
107. G. V. Nikiforovich, B. Mihalik, K. J. Catt and G. R. Marshall, Molecular mechanisms of constitutive activity: Mutations at position 111 of the angiotensin AT1 receptor, J. Pept. Res., 66, 236-248 (2005).
108. G. V. Nikiforovich and G. R. Marshall, 3D model for TM region of the AT-1 receptor in complex with angiotensin II independently validated by site-directed mutagenesis data, Biochem. Biophys. Res. Commun., 286, 1204-1211 (2001).
109. G. V. Nikiforovich, S. Galaktionov, J. Balodis and G. R. Marshall, Novel approach to computer modeling of seven-helical transmembrane proteins: Current progress in the test case of bacteriorhodopsin, Acta Biochim. Pol., 48, 53-64 (2001).
110. J. U. Bowie, Helix packing in membrane proteins, J.Mol. Biol., 272, 780-789 (1997).
111. J. Deisenhofer, O. Epp, I. Sinning and H. Michel, Crystallographic refinement at 2.3 A° resolution and refined model of the photosynthetic reaction centre from Rhodopseudomonas viridis, J. Mol. Biol., 246, 429-457 (1995).
112. C. Skold, G. V. Nikiforovich and A. Karlen, Modeling binding modes of angiotensin II and pseudopeptide analogues to the AT2 receptor, J. Mol. Model. Graph., 26, 991-1003 (2008).
113. G. V. Nikiforovich and G. R. Marshall, Modeling flexible loops in the dark-adapted and activated states of rhodopsin, a prototypical G-protein coupled receptor, Biophys. J., 89, 3780-3789 (2005).
114. T. Warne, M. J. Serrano-Vega, J. G. Baker, R. Moukhametzianov, P. C. Edwards, R. Henderson, A. G. Leslie, C. S. Tate and G. F. Schertler, Structure of a b1-adrenergic G-protein-coupled receptor, Nature, 454, 486-491 (2008).
115. V. P. Jaakola, M. T. Griffith, M. A. Hanson, V. Cherezov, E. Y. Chien, J. R. Lane, A. P. Ijzerman and R. C. Stevens, The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist, Science, 322, 1211-1217 (2008).