Copyright © 2012 Brian Olson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

1. C. A. Floudas and P. M. Pardalos, Encyclopedia of Optimization, Kluwer Academic Publishers, Norwell, Mass, USA, 2001.
2. “Nonconvex optimization and its applications,” in Global Optimization: Scientific and Engineering Case Studies, J. Pinter, Ed., vol. 85 of Mathematics and Statistics, Springer Science and Business Media, New York, NY, USA, 2006.
3. D. J. Wales and J. P. K. Doye, “Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms,” Journal of Physical Chemistry A, vol. 101, no. 28, pp. 5111–5116, 1997. View at Scopus
4. A. Tiano, F. Pizzochero, and P. Venini, “A global optimization approach to nonlinear system identification,” in Conference on Control and Automation, pp. 752–761, 1999.
5. A. R. Leach, “A survey of methods for searching the conformational space of small and medium-sized molecules,” in Reviews in Computational Chemistry, vol. 2, pp. 1–55, VCH Publishing, New York, NY, USA, 1991.
6. H. A. Scheraga, “Predicting three-dimensional structures of oligopeptides,” in Reviews in Computational Chemistry, K. B. Lipkowtiz and D. B. Boyd, Eds., vol. 3, pp. 73–142, VCH Publishing, New York, NY, USA, 1992.
7. R. V. Pappu, R. K. Hart, and J. W. Ponder, “Analysis and application of potential energy smoothing and search methods for global optimization,” Journal of Physical Chemistry B, vol. 102, no. 48, pp. 9725–9742, 1998. View at Scopus
8. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983. View at Scopus
9. A. Nayeem, J. Vila, and H. A. Scheraga, “A comparative study of the simulated-annealing and monte carlowith-minimization approaches to the minimum-energy structures of polypeptides: [Met]-enkephalin,” Journal of Computational Chemistry, vol. 12, no. 5, pp. 594–605, 1991.
10. R. C. Brower, G. Vasmatzis, M. Silverman, and C. Delisi, “Exhaustive conformational search and simulated annealing for models of lattice peptides,” Biopolymers, vol. 33, no. 3, pp. 329–334, 1993. View at Scopus
11. W. F. van Gunsteren, D. Bakowies, R. Baron et al., “Biomolecular modeling: goals, problems, perspectives,” Angewandte Chemie, vol. 45, no. 25, pp. 4064–4092, 2006. View at Publisher · View at Google Scholar · View at Scopus
12. A. Shehu, “Conformational search for the protein native state,” in Protein Structure Prediction: Method and Algorithms, Rangwala and G. Karypis, Eds., Wiley Book Series on Bioinformatics, chapter 21, Fairfax, VA, USA, 2010.
13. R. H. Leary, “Global optimization on funneling landscapes,” Journal of Global Optimization, vol. 18, no. 4, pp. 367–383, 2000. View at Scopus
14. M. Iwamatsu and Y. Okabe, “Basin hopping with occasional jumping,” Chemical Physics Letters, vol. 399, no. 4–6, pp. 396–400, 2004. View at Publisher · View at Google Scholar · View at Scopus
15. M. A. Miller and D. J. Wales, “Novel structural motifs in clusters of dipolar spheres: knots, links, and coils,” Journal of Physical Chemistry B, vol. 109, no. 49, pp. 23109–23112, 2005. View at Publisher · View at Google Scholar · View at Scopus
16. J. M. Carr and D. J. Wales, “Global optimization and folding pathways of selected alpha-helical proteins,” The Journal of Chemical Physics, vol. 123, no. 23, p. 234901, 2005. View at Publisher · View at Google Scholar · View at Scopus
17. T. James, D. J. Wales, and J. Hernández-Rojas, “Global minima for water clusters (H₂O)n, n ≤ 21, described by a five-site empirical potential,” Chemical Physics Letters, vol. 415, no. 4–6, pp. 302–307, 2005. View at Publisher · View at Google Scholar · View at Scopus
18. R. Gehrke and K. Reuter, “Assessing the efficiency of first-principles basin-hopping sampling,” Physical Review B, vol. 79, no. 8, Article ID 085412, 10 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
19. D. J. Wales, Energy Landscapes and Structure Prediction Using Basin-Hopping, Wiley-VCH Verlag GmbH and Co. KGaA, 2010.
20. A. Grosso, A. R. M. J. U. Jamali, M. Locatelli, and F. Schoen, “Solving the problem of packing equal and unequal circles in a circular container,” Journal of Global Optimization, vol. 47, no. 1, pp. 63–81, 2010. View at Publisher · View at Google Scholar · View at Scopus
21. M. Locatelli, “On the multilevel structure of global optimization problems,” Computational Optimization and Applications, vol. 30, no. 1, pp. 5–22, 2005. View at Publisher · View at Google Scholar · View at Scopus
22. B. Olson and A. Shehu, “Evolutionary-inspired probabilistic search for enhancing sampling of local minima in the protein energy surface,” Proteome Science, vol. 10, no. supplement 1, p. S5, 2012.
23. O. M. H. R. Lourenco and T. Stutzle, “Iterated local search,” in Handbook of Metaheuristics, F. Glover and G. Kochenberger, Eds., vol. 57, no. 513 of Operations Research & Management Science, pp. 321–353, Kluwer Academic Publishers, 2002.
24. Z. Li and H. A. Scheraga, “Monte Carlo-minimization approach to the multiple-minima problem in protein folding,” Proceedings of the National Academy of Sciences of the United States of America, vol. 84, no. 19, pp. 6611–6615, 1987. View at Scopus
25. K. A. Dill and H. S. Chan, “From levinthal to pathways to funnels,” Nature Structural Biology, vol. 4, no. 1, pp. 10–19, 1997. View at Publisher · View at Google Scholar · View at Scopus
26. J. N. Onuchic and P. G. Wolynes, “Theory of protein folding,” Current Opinion in Structural Biology, vol. 14, no. 1, pp. 70–75, 2004. View at Publisher · View at Google Scholar · View at Scopus
27. J. Moult, K. Fidelis, A. Kryshtafovych, and A. Tramontano, “Critical assessment of methods of protein structure prediction (CASP) round IX,” Proteins, vol. 79, supplement 10, pp. 1–5, 2009. View at Publisher · View at Google Scholar · View at Scopus
28. P. Bradley, K. M. S. Misura, and D. Baker, “Toward highresolution de novo structure prediction for small proteins,” Science, vol. 309, no. 5742, pp. 1868–1871, 2005. View at Publisher · View at Google Scholar · View at Scopus
29. S. Yin, F. Ding, and N. V. Dokholyan, “Eris: an automated estimator of protein stability,” Nature Methods, vol. 4, no. 6, pp. 466–467, 2007. View at Publisher · View at Google Scholar · View at Scopus
30. T. Kortemme and D. Baker, “Computational design of protein-protein interactions,” Current Opinion in Chemical Biology, vol. 8, no. 1, pp. 91–97, 2004. View at Publisher · View at Google Scholar · View at Scopus
31. V. Hornak, R. Abel, A. Okur, B. Strockbine, A. Roitberg, and C. Simmerling, “Comparison of multiple amber force fields and development of improved protein backbone parameters,” Proteins, vol. 65, no. 3, pp. 712–725, 2006. View at Publisher · View at Google Scholar · View at Scopus
32. A. Verma, A. Schug, K. H. Lee, and W. Wenzel, “Basin hopping simulations for all-atom protein folding,” The Journal of Chemical Physics, vol. 124, no. 4, p. 044515, 2006. View at Publisher · View at Google Scholar · View at Scopus
33. A. Shehu, L. E. Kavraki, and C. Clementi, “Multiscale characterization of protein conformational ensembles,” Proteins, vol. 76, no. 4, pp. 837–851, 2009. View at Publisher · View at Google Scholar · View at Scopus
34. R. Bonneau, C. E. M. Strauss, C. A. Rohl et al., “De novo prediction of three-dimensional structures for major protein families,” Journal of Molecular Biology, vol. 322, no. 1, pp. 65–78, 2002. View at Publisher · View at Google Scholar · View at Scopus
35. T. J. Brunette and O. Brock, “Guiding conformation space search with an all-atom energy potential,” Proteins, vol. 73, no. 4, pp. 958–972, 2008. View at Publisher · View at Google Scholar · View at Scopus
36. J. DeBartolo, A. Colubri, A. K. Jha, J. E. Fitzgerald, K. F. Freed, and T. R. Sosnick, “Mimicking the folding pathway to improve homology-free protein structure prediction,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 10, pp. 3734–3739, 2009. View at Publisher · View at Google Scholar · View at Scopus
37. J. Debartolo, G. Hocky, M. Wilde, J. Xu, K. F. Freed, and T. R. Sosnick, “Protein structure prediction enhanced with evolutionary diversity: SPEED,” Protein Science, vol. 19, no. 3, pp. 520–534, 2010. View at Publisher · View at Google Scholar · View at Scopus
38. A. Shehu, L. E. Kavraki, and C. Clementi, “Unfolding the fold of cyclic cysteine-rich peptides,” Protein Science, vol. 17, no. 3, pp. 482–493, 2008. View at Publisher · View at Google Scholar · View at Scopus
39. M. C. Prentiss, C. Hardin, M. P. Eastwood, C. Zong, and P. G. Wolynes, “Protein structure prediction: the next generation,” Journal of Chemical Theory and Computation, vol. 2, no. 3, pp. 705–716, 2006. View at Publisher · View at Google Scholar · View at Scopus
40. A. Shehu and B. Olson, “Guiding the search for native-like protein conformations with an Ab-initio tree-based exploration,” International Journal of Robotics Research, vol. 29, no. 8, pp. 1106–1127, 2010. View at Publisher · View at Google Scholar · View at Scopus
41. B. Olson, K. Molloy, and A. Shehu, “In search of the protein native state with a probabilistic sampling approach,” Journal of Bioinformatics and Computational Biology, vol. 9, no. 3, pp. 383–398, 2011. View at Publisher · View at Google Scholar · View at Scopus
42. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, “Equation of state calculations by fast computing machines,” The Journal of Chemical Physics, vol. 21, no. 6, pp. 1087–1092, 1953. View at Scopus
43. R. Abagyan and M. Totrov, “Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins,” Journal of Molecular Biology, vol. 235, no. 3, pp. 983–1002, 1994. View at Publisher · View at Google Scholar · View at Scopus
44. P. N. Mortenson, D. A. Evans, and D. J. Wales, “Energy landscapes of model polyalanines,” Journal of Chemical Physics, vol. 117, no. 3, pp. 1363–1376, 2002. View at Publisher · View at Google Scholar · View at Scopus
45. M. C. Prentiss, D. J. Wales, and P. G. Wolynes, “Protein structure prediction using basin-hopping,” The Journal of Chemical Physics, vol. 128, no. 22, Article ID 225106, 9 pages, 2008. View at Publisher · View at Google Scholar · View at Scopus
46. A. S. Manalan and C. B. Klee, “Calmodulin,” Advances in Cyclic Nucleotide and Protein Phosphorylation Research, vol. 18, pp. 227–278, 1984. View at Scopus
47. A. R. Means, “Molecular mechanisms of action of calmodulin,” Recent Progress in Hormone Research, vol. 44, pp. 223–262, 1988. View at Scopus
48. K. T. O'Neil and W. F. DeGrado, “How calmodulin binds its targets: sequence independent recognition of amphiphilic α-helices,” Trends in Biochemical Sciences, vol. 15, no. 2, pp. 59–64, 1990. View at Scopus
49. B. E. Finn, J. Evenas, T. Drakenberg, J. P. Waltho, E. Thulin, and S. Forsen, “Calcium-induced structural changes and domain autonomy in calmodulin,” Nature Structural Biology, vol. 2, no. 9, pp. 777–783, 1995. View at Scopus
50. B. W. Zhang, D. Jasnow, and D. M. Zuckermann, “Efficient and verified simulation of a path ensemble for conformational change in a united-residue model of calmodulin,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 46, pp. 18043–18048, 2007.
51. I. Hashmi, B. Akbal-Delibas, N. Haspel, and A. Shehu, “Guiding protein docking with geometric and evolutionary information,” Journal of Bioinformatics and Computational Biology, vol. 10, no. 3, Article ID 1242008, 16 pages, 2012. View at Publisher · View at Google Scholar
52. A. A. Canutescu, A. A. Shelenkov, and R. L. Dunbrack Jr., “A graph-theory algorithm for rapid protein side-chain prediction,” Protein Science, vol. 12, no. 9, pp. 2001–2014, 2003. View at Publisher · View at Google Scholar · View at Scopus
53. M. Zhang and L. E. Kavraki, “A new method for fast and accurate derivation of molecular conformations,” Journal of Chemical Information and Computer Sciences, vol. 42, no. 1, pp. 64–70, 2002. View at Publisher · View at Google Scholar · View at Scopus
54. G. A. Papoian, J. Ulander, M. P. Eastwood, Z. Luthey-Schulten, and P. G. Wolynes, “Water in protein structure prediction,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 10, pp. 3352–3357, 2004. View at Publisher · View at Google Scholar · View at Scopus
55. A. Shehu, “An ab-initio tree-based exploration to enhance sampling of low-energy protein conformations,” in Robotics: Science and Systems, pp. 241–248, Seattle, Wash, USA, 2009.
56. B. S. Olson, K. Molloy, S. F. Hendi, and A. Shehu, “Guiding search in the protein conformational space with structural profiles,” Journal of Bioinformatics and Computational Biology, vol. 10, no. 3, Article ID 1242005, 2012.
57. J. A. Hegler, J. Laetzer, A. Shehu, C. Clementi, and P. G. Wolynes, “Restriction vs. guidance: fragment assembly and associative memory hamiltonians for protein structure prediction,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 36, pp. 15302–15307, 2009.
58. D. A. Case, T. A. Darden, T. E. I. Cheatham, et al., Amber 9, University of California, San Francisco, Calif, USA, 2006.
59. H. Gong, P. J. Fleming, and G. D. Rose, “Building native protein conformations from highly approximate backbone torsion angles,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 45, pp. 16227–16232, 2005.
60. K. F. Han and D. Baker, “Global properties of the mapping between local amino acid sequence and local structure in proteins,” Proceedings of the National Academy of Sciences of the United States of America, vol. 93, no. 12, pp. 5814–5818, 1996. View at Publisher · View at Google Scholar · View at Scopus
61. H. M. Berman, K. Henrick, and H. Nakamura, “Announcing the worldwide Protein Data Bank,” Nature Structural Biology, vol. 10, no. 12, p. 980, 2003. View at Publisher · View at Google Scholar · View at Scopus
62. I. Hashmi, B. Akbal-Delibas, N. Haspel, and A. Shehu, “Protein docking with information on evolutionary conserved interfaces,” in Bioinformatics and Biomedicine Workshops (BIBMW '11), pp. 358–365, November 2011.
63. G. Terashi, M. Takeda-Shitaka, K. Kanou, M. Iwadate, D. Takaya, and H. Umeyama, “The SKE-DOCK server and human teams based on a combined method of shape complementarity and free energy estimation,” Proteins, vol. 69, no. 4, pp. 866–872, 2007. View at Publisher · View at Google Scholar · View at Scopus
64. D. Schneidman-Duhovny, Y. Inbar, R. Nussinov, and H. J. Wolfson, “PatchDock and SymmDock: servers for rigid and symmetric docking,” Nucleic Acids Research, vol. 33, no. 2, pp. W363–W367, 2005. View at Publisher · View at Google Scholar · View at Scopus
65. Y. Inbar, H. Benyamini, R. Nussinov, and H. J. Wolfson, “Combinatorial docking approach for structure prediction of large proteins and multi-molecular assemblies,” Physical Biology, vol. 2, no. 4, pp. S156–S165, 2005. View at Publisher · View at Google Scholar · View at Scopus
66. Y. Inbar, H. Benyamini, R. Nussinov, H. J. Wolfson, and B. Honig, “Prediction of multimolecular assemblies by multiple docking,” Journal of Molecular Biology, vol. 349, no. 2, pp. 435–447, 2005. View at Publisher · View at Google Scholar · View at Scopus
67. S. Engelen, L. A. Trojan, S. Sacquin-Mora, R. Lavery, and A. Carbone, “Joint evolutionary trees: a large-scale method to predict protein interfaces based on sequence sampling,” PLoS Computational Biology, vol. 5, no. 1, Article ID e1000267, 2009. View at Publisher · View at Google Scholar · View at Scopus
68. M. L. Connolly, “Analytical molecular surface calculation,” Applied Crystallography, vol. 16, no. 5, pp. 548–558, 1983.
69. R. Norel, S. L. Lin, H. J. Wolfson, and R. Nussinov, “Examination of shape complementarity in docking of unbound proteins,” Proteins, vol. 36, no. 3, pp. 307–317, 1999.
70. 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,” Journal of Computational Chemistry, vol. 4, no. 2, pp. 187–217, 1983.
71. T. Kortemme and D. Baker, “A simple physical model for binding energy hot spots in protein-protein complexes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 22, pp. 14116–14121, 2002. View at Publisher · View at Google Scholar · View at Scopus
72. I. Hashmi and A. Shehu, “A basin hopping algorithm for protein-protein docking,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM '12), J. Gao, W. Dubitzky, C. Wu et al., Eds., pp. 466–469, Philadelphia, Pa, USA, 2012.
73. J. R. Schnell, H. J. Dyson, and P. E. Wright, “Structure, dynamics, and catalytic function of dihydrofolate reductase,” Annual Review of Biophysics and Biomolecular Structure, vol. 33, pp. 119–140, 2004. View at Publisher · View at Google Scholar · View at Scopus
74. E. Z. Eisenmesser, O. Millet, W. Labeikovsky et al., “Intrinsic dynamics of an enzyme underlies catalysis,” Nature, vol. 438, no. 7064, pp. 117–121, 2005. View at Publisher · View at Google Scholar · View at Scopus
75. K. I. Okazaki, N. Koga, S. Takada, J. N. Onuchic, and P. G. Wolynes, “Multiple-basin energy landscapes for large-amplitude conformational motions of proteins: structure-based molecular dynamics simulations,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 32, pp. 11844–11849, 2006. View at Publisher · View at Google Scholar · View at Scopus
76. Q. Lu and J. Wang, “Single molecule conformational dynamics of adenylate kinase: energy landscape, structural correlations, and transition state ensembles,” Journal of the American Chemical Society, vol. 130, no. 14, pp. 4772–4783, 2008. View at Publisher · View at Google Scholar · View at Scopus
77. P. Majek, H. Weinstein, and R. Elber, Pathways of Conformational Conformational Transitions in Proteins, chapter 13, Taylor and Francis group, 2008.
78. D. R. Weiss and M. Levitt, “Can morphing methods predict intermediate structures?” Journal of Molecular Biology, vol. 385, no. 2, pp. 665–674, 2009. View at Publisher · View at Google Scholar · View at Scopus
79. Y. Zhang and J. Skolnick, “Scoring function for automated assessment of protein structure template quality,” Proteins, vol. 57, no. 4, pp. 702–710, 2004. View at Publisher · View at Google Scholar · View at Scopus
80. A. Zemla, “LGA: a method for finding 3D similarities in protein structures,” Nucleic Acids Research, vol. 31, no. 13, pp. 3370–3374, 2003.
81. A. D. McLachlan, “A mathematical procedure for superimposing atomic coordinates of proteins,” Acta Crystallographica A, vol. 26, no. 6, pp. 656–657, 1972.
82. V. N. Maiorov and G. M. Crippen, “Significance of root-mean-square deviation in comparing three-dimensional structures of globular proteins,” Journal of Molecular Biology, vol. 235, no. 2, pp. 625–634, 1994. View at Publisher · View at Google Scholar · View at Scopus
83. J. Xu and Y. Zhang, “How significant is a protein structure similarity with TM-score = 0.5?” Bioinformatics, vol. 26, no. 7, pp. 889–895, 2010. View at Publisher · View at Google Scholar · View at Scopus
84. J. Meiler and D. Baker, “Coupled prediction of protein secondary and tertiary structure,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 21, pp. 12105–12110, 2003.
85. B. Olson and A. Shehu, “Efficient basin hopping in the protein energy surface,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM '12), J. Gao, W. Dubitzky, C. Wu et al., Eds., pp. 119–124, Philadelphia, Pa, USA, 2012.
86. P. J. Ballester and W. G. Richards, “Ultrafast shape recognition to search compound databases for similar molecular shapes,” Journal of Computational Chemistry, vol. 28, no. 10, pp. 1711–1723, 2007. View at Publisher · View at Google Scholar · View at Scopus
87. M. L. Teodoro, G. N. Phillips, and L. E. Kavraki, “Understanding protein flexibility through dimensionality reduction,” Journal of Computational Biology, vol. 10, no. 3-4, pp. 617–634, 2003. View at Publisher · View at Google Scholar · View at Scopus
88. P. Das, M. Moll, H. Stamati, L. E. Kavraki, and C. Clementi, “Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 26, pp. 9885–9890, 2006. View at Publisher · View at Google Scholar · View at Scopus
89. H. Stamati, C. Clementi, and L. E. Kavraki, “Application of nonlinear dimensionality reduction to characterize the confonrmational landscape of small peptides,” Proteins, vol. 78, no. 2, pp. 223–235, 2010. View at Publisher · View at Google Scholar · View at Scopus
90. M. A. Rohrdanz, W. Zheng, M. Maggioni, and C. Clementi, “Determination of reaction coordinates via locally scaled diffusion map,” Journal of Chemical Physics, vol. 134, no. 12, Article ID 124116, 2011. View at Publisher · View at Google Scholar · View at Scopus
91. E. Kanamori, Y. Murakami, Y. Tsuchiya, D. M. Standley, H. Nakamura, and K. Kinoshita, “Docking of protein molecular surfaces with evolutionary trace analysis,” Proteins, vol. 69, no. 4, pp. 832–838, 2007. View at Publisher · View at Google Scholar · View at Scopus
92. H. Kuboniwa, N. Tjandra, S. Grzesiek, H. Ren, C. B. Klee, and A. Bax, “Solution structure of calcium-free calmodulin,” Nature Structural Biology, vol. 2, no. 9, pp. 768–776, 1995. View at Scopus
93. R. Chattopadhyaya, W. E. Meador, A. R. Means, and F. A. Quiocho, “Calmodulin structure refined at 1.7 Å resolution,” Journal of Molecular Biology, vol. 228, no. 4, pp. 1177–1192, 1992. View at Publisher · View at Google Scholar · View at Scopus
94. J. L. Fallon, D. B. Halling, S. L. Hamilton, and F. A. Quiocho, “Structure of calmodulin bound to the hydrophobic IQ domain of the cardiac Cav1.2 calcium channel,” Structure, vol. 13, no. 12, pp. 1881–1886, 2005. View at Publisher · View at Google Scholar · View at Scopus
95. D. G. Rhoads and J. M. Lowenstein, “Initial velocity and equilibrium kinetics of myokinase,” Journal of Biological Chemistry, vol. 243, no. 14, pp. 3963–3972, 1968. View at Scopus
96. C. W. Müller, G. J. Schlauderer, J. Reinstein, and G. E. Schulz, “Adenylate kinase motions during catalysis: an energetic counterweight balancing substrate binding,” Structure, vol. 4, no. 2, pp. 147–156, 1996. View at Scopus
97. U. Abele and G. E. Schulz, “High-resolution structures of adenylate kinase from yeast ligated with inhibitor Ap5A, showing the pathway of phosphoryl transfer,” Protein Science, vol. 4, no. 7, pp. 1262–1271, 1995. View at Scopus
98. G. J. Schlauderer, K. Proba, and G. E. Schulz, “Structure of a mutant adenylate kinase ligated with an ATP-analogue showing domain closure over ATP,” Journal of Molecular Biology, vol. 256, no. 2, pp. 223–227, 1996. View at Publisher · View at Google Scholar · View at Scopus
99. K. Diederichs and G. E. Schulz, “The refined structure of the complex between adenylate kinase from beef heart mitochondrial matrix and its substrate AMP at 1.85 Å resolution,” Journal of Molecular Biology, vol. 217, no. 3, pp. 541–549, 1991. View at Scopus
100. J. Ådén and M. Wolf-Watz, “NMR identification of transient complexes critical to adenylate kinase catalysis,” Journal of the American Chemical Society, vol. 129, no. 45, pp. 14003–14012, 2007. View at Publisher · View at Google Scholar · View at Scopus
101. C. Snow, G. Qi, and S. Hayward, “Essential dynamics sampling study of adenylate kinase: comparison to citrate synthase and implication for the hinge and shear mechanisms of domain motions,” Proteins, vol. 67, no. 2, pp. 325–337, 2007. View at Publisher · View at Google Scholar · View at Scopus
102. B. Olson and A. Shehu, “Populating local minima in the protein conformational space,” in IEEE International Conference on Bioinformatics and Biomedicine (BIBM '11), pp. 114–117, November 2011.
103. H. Choset, K. M. Lynch, S. Hutchinson, et al., Principles of Robot Motion: Theory, Algorithms, and Implementations, MIT Press, Cambridge, Mass, USA, 1st edition, 2005.