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A stochastic roadmap method to model protein structural transitions

Published online by Cambridge University Press:  11 December 2015

Kevin Molloy ,
Rudy Clausen  and
Amarda Shehu
Kevin Molloy
Affiliation:
Department of Computer Science, George Mason University, Fairfax, 22030 VA, USA E-mails: kmolloy1@gmu.edu, rclausen@gmu.edu
Rudy Clausen
Affiliation:
Department of Computer Science, George Mason University, Fairfax, 22030 VA, USA E-mails: kmolloy1@gmu.edu, rclausen@gmu.edu
Amarda Shehu*
Affiliation:
Department of Computer Science, George Mason University, Fairfax, 22030 VA, USA E-mails: kmolloy1@gmu.edu, rclausen@gmu.edu Department of Bioengineering, George Mason University, Fairfax, 22030 VA, USA School of Systems Biology, George Mason University, Fairfax, 20110 VA, USA
*
*Corresponding author. E-mail: amarda@gmu.edu
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Summary

Evidence is emerging that the role of protein structure in disease needs to be rethought. Sequence mutations in proteins are often found to affect the rate at which a protein switches between structures. Modeling structural transitions in wildtype and variant proteins is central to understanding the molecular basis of disease. This paper investigates an efficient algorithmic realization of the stochastic roadmap simulation framework to model structural transitions in wildtype and variants of proteins implicated in human disorders. Our results indicate that the algorithm is able to extract useful information on the impact of mutations on protein structure and function.

Keywords

Protein dynamicsStructural transitionsConformational switchingSOD1 and Ras variantsHuman disordersStochastic roadmap simulationMarkov state modelFirst-step analysisPath smoothingEnergy landscape
Type
Articles
Information
Robotica , Volume 34 , Issue 8 , August 2016 , pp. 1705 - 1733
Copyright
Copyright © Cambridge University Press 2015 

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References

1. Jenzler-Wildman, K. and Kern, D., “Dynamic personalities of proteins,” Nature 450, 964–972 (2007).CrossRefGoogle Scholar
2. Anfinsen, C. B., “Principles that govern the folding of protein chains,” Science 181 (4096), 223–230 (1973).CrossRefGoogle ScholarPubMed
3. Soto, C., “Protein misfolding and neurodegeneration,” JAMA Neurology 65 (2), 184–189 (2008).Google Scholar
4. Boehr, D. D., McElheny, D., Dyson, J. and Wright, P. E., “The dynamic energy landscape of dihydrofolate reductase catalysis,” Science 313 (5793), 1638–1642 (2006).Google Scholar
5. Boehr, D. D., Nussinov, R. and Wright, P. E., “The role of dynamic conformational ensembles in biomolecular recognition,” Nature Chem. Biol. 5 (11), 789–96 (2009).Google Scholar
6. Fernández-Medarde, A. and Santos, E., “Ras in cancer and developmental diseases,” Genes Cancer 2 (3), 344–358 (2011).Google Scholar
7. Grant, B. J., Gorfe, A. A. and McCammon, J. A., “Ras conformational switching: Simulating nucleotide-dependent conformational transitions with accelerated molecular dynamics,” PLoS Comp. Biol. 5 (3), e1000325 (2009).Google Scholar
8. Apaydin, M. S., Brutlag, D. L., Guestrin, C., Hsu, D. and Latombe, J.-C., “Stochastic roadmap simulation: An efficient representation and algorithm for analyzing molecular motion,” J. Comp. Biol. 10 (3–4), 257–281 (2003).Google Scholar
9. Clausen, R. and Shehu, A., “A data-driven evolutionary algorithm for mapping multi-basin protein energy landscapes,” J. Comp. Biol. 22 (9), 844–860 (2015).Google Scholar
10. Clausen, R. and Shehu, A., “A Multiscale Hybrid Evolutionary Algorithm to Obtain Sample-Based Representations of Multi-Basin Protein Energy Landscapes,” ACM Conference on Bioinformatics, Computational Biology (BCB), Newport Beach, CA (Sep. 2014) pp. 269–278.CrossRefGoogle Scholar
11. Conwit, R. A., “Preventing familial ALS: A clinical trial may be feasible but is an efficacy trial warranted?” J. Neurol. Sci. 251 (1–2), 1–2 (2006).Google Scholar
12. Karnoub, A. E. and Weinberg, R. A., “Ras oncogenes: Split personalities,” Nature Rev. Mol. Cell Biol. 9, 517–531 (2008).Google Scholar
13. Fischer, S. and 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. 194 (3), 252–261 (1992).Google Scholar
14. Kavraki, L. E., Svetska, P., Latombe, J.-C. and Overmars, M., “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Trans. Robot. Autom. 12 (4), 566–580 (1996).Google Scholar
15. Singh, A. P., Latombe, J.-C. and Brutlag, D. L., “A Motion Planning Approach to Flexible Ligand Binding,” In: Proceedigs of the International Conference on Intelligent Systems for Molecular Biology (ISMB) vol. 7, (Schneider, R., Bork, P., Brutlag, D. L., Glasgow, J. I., Mewes, H.-W. and Zimmer, R., eds.) (AAAI, Heidelberg, Germany, 1999) pp. 252–261.Google Scholar
16. Song, G. and Amato, N. M., “A motion-planning approach to folding: From paper craft to protein folding. Technical Report TR00-001, Department of Computer Science, Texas A & M University, Jan. 2000.Google Scholar
17. Amato, N. M., Dill, K. A. and Song, G., “Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures,” J. Comp. Biol. 10 (3–4), 239–255 (2002).Google Scholar
18. Song, G. and Amato, N. M., “A motion planning approach to folding: From paper craft to protein folding,” IEEE Trans. Robot. Autom. 20 (1), 60–71 (2004).Google Scholar
19. Thomas, S., Song, G. and Amato, N. M., “Protein folding by motion planning,” J. Phys. Biol. 2 (4), 148 (2005).Google Scholar
20. Thomas, S., Tang, X., Tapia, L. and Amato, N. M., “Simulating protein motions with rigidity analysis,” J. Comput. Biol. 14 (6), 839–855 (2007).Google Scholar
21. Tapia, L., Tang, X., Thomas, S. and Amato, N., “Kinetics analysis methods for approximate folding landscapes,” Bioinformatics 23, i539–i548 (2007).Google Scholar
22. Tang, X., Thomas, S., Tapia, L., Giedroc, D. P. and Amato, N., “Simulating rna folding kinetics on approximated energy landscapes,” J. Mol. Biol. 381 (4), 1055–1067 (2008).CrossRefGoogle ScholarPubMed
23. Tapia, L., Thomas, S. and Amato, N., “A motion planning approach to studying molecular motions,” Commun. Inform. Syst. 10 (1), 53–68 (2010).Google Scholar
24. Moll, M., Schwartz, D. and Kavraki, L. E., “Roadmap methods for protein folding,” Methods Mol. Biol. 413, 219–239 (2008).Google Scholar
25. Molloy, K. and Shehu, A., “A Probabilistic Roadmap-Based Method to Model Conformational Switching of a Protein Among Many Functionally-Relevant Structures,” International Conference on Bioinformatics & Computational Biology (BICoB), Las Vegas, NV (2014) pp. 1–6.Google Scholar
26. Molloy, K. and Shehu, A., “Interleaving Global and Local Search for Protein Motion Computation. In: LNCS: Bioinformatics Research and Applications, vol. 9096, (Harrison, R., Li, Y. and Mandoiu, I., eds.) (Springer International Publishing, Norfolk, VA, 2015) pp. 175–186.Google Scholar
27. Shehu, A., “Probabilistic Search and Optimization for Protein Energy Landscapes,” In: Handbook of Computational Molecular Biology (Aluru, S. and Singh, A., eds.) (Chapman & Hall/CRC, Computer & Information Science Series, 2013).Google Scholar
28. Chiang, T. H., Hsu, D. and Latombe, J. C. “Markov dynamic models for long-timescale protein motion,” Bioinformatics 26 (12), 269–277 (2010).Google Scholar
29. Singhal, N., Snow, C. D. and Pande, V. S., “Using path sampling to build better markovian state models: Predicting the folding rate and mechanism of a tryptophan zipper beta hairpin,” J. Chem. Phys. 121 (1), 415–425 (2004).Google Scholar
30. Beauchamp, K. A., Ensign, D. L., Das, R. and Pande, V. S., “Quantitative comparison of villin headpiece subdomain simulations and triplettriplet energy transfer experiments,” Proc. Natl. Acad. Sci. USA 108 (31), 12734–12739 (2011).CrossRefGoogle ScholarPubMed
31. Beauchamp, K. A., Bowman, G. R., Lane, T. J., Maibaum, L., Haque, I. S. and Pande, V. S., “MSMBuilder2: Modeling conformational dynamics at the picosecond to millisecond scale,” J. Chem. Theory Comput. 7 (10), 3412–3419 (2011).Google Scholar
32. Noé, F., Schutte, C., Vanden, E.-Eijnden, Reich, L. and Weikl, T. R., “Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations,” Proc. Natl. Acad. Sci. USA 106 (45), 19011–19016 (2009).Google Scholar
33. Malmstrom, R. D., Lee, C. T., Van Wart, A. T. and Amaro, R. E., “Application of molecular-dynamics based markov state models to functional proteins,” J. Chem. Theory Comput. 10 (7), 2648–2657 (2014).Google Scholar
34. Held, M. and Noé, F., “Calculating kinetics and pathways of protein-ligand association,” Eur. J. Cell Biol. 91 (4), 357–364 (2012).Google Scholar
35. Ma, J. and Karplus, M., “Molecular switch in signal transduction: Reaction paths of the conformational changes in ras p21,” Proc. Natl. Acad. Sci. USA 94 (22), 11905–11910 (1997).Google Scholar
36. Beckstein, O., Denning, E. J., Perilla, J. R. and Woolf, T. B., “Zipping and unzipping of adenylate kinase: Atomistic insights into the ensemble of open-closed transitions,” J. Mol. Biol. 394 (1), 160–176 (2009).Google Scholar
37. Hamelberg, D., Mongan, J. and McCammon, J. A., “Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules,” J. Chem. Phys. 120 (24), 11919–11929 (2004).Google Scholar
38. Senne, M., Trendelkamp-Schroer, B., Mey, A. S., Schütte, C. and Noé, F., “EMMA: A software package for markov model building and analysis,” J. Chem. Theory Comput. 8 (7), 2223–2238 (2012).Google Scholar
39. Bowman, G. R., Pande, V. S. and Noé, F., An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation (Springer, Heidelberg, Germany, 2014).Google Scholar
40. Gipson, B., Hsu, D., Kavraki, L. E. and Latombe, J.-C., “Computational models of protein kinematics and dynamics: Beyond simulation,” Annu. Rev. Anal. Chem. 5, 273–291 (2012).Google Scholar
41. Hub, J. S. and de Groot, B. L., “Detection of functional modes in protein dynamics,” PLoS Comp. Biol. 5 (8), e1000480 (2009).CrossRefGoogle ScholarPubMed
42. Ciu, Q. and Bahar, I., Normal Mode Analysis: Theory and Applications to Biological and Chemical Systems, 1st ed. (CRC Press, 2005).Google Scholar
43. Bahar, R. and Rader, A. J., “Coarse-grained normal mode analysis in structural biology,” Curr. Opin. Struct. Biol. 204 (5), 1–7 (2005).Google Scholar
44. Bahar, I., Lezon, T. R., Yang, L. W. and Eyal, E., “Global dynamics of proteins: Bridging between structure and function,” Annu. Rev. Biophys. 39, 23–42 (2010).Google Scholar
45. Krebs, W. G. and Gerstein, M., “The morph server: A standardized system for analyzing and visualizing macromolecular motions in a database framework,” Nucleic Acids Res. 28 (8), 1665–1675 (2000).Google Scholar
46. Echols, N., Milburn, D. and Gerstein, M., “Molmovdb: Analysis and visualization of conformational change and structural flexibility,” Nucleic Acids Res. 31 (1), 478–482 (2003).Google Scholar
47. Weiss, D. R. and Koehl, P., “Morphing methods to visualize coarse-grained protein dynamics,” In: Protein Dynamics, vol. 1084, (Livesayed, Dennis R.) (Humana Press, 2014) pp. 271–282.Google Scholar
48. Kim, M. K., Chirikjian, G. S. and Jernigan, R. L., “Elastic models of conformational transitions in macromolecules,” J. Mol. Graph Model 21 (2), 151–160 (2002).Google Scholar
49. Kim, K. M., Jernigan, R. L. and Chirikjian, G. S., “Efficient generation of feasible pathways for protein conformational transitions,” Biophys. J. 83 (3), 1620–1630 (2002).Google Scholar
50. Schuyler, A. D., Jernigan, R. L., Wasba, P. K., Ramakrishnan, B. and Chirikjian, G. S., “Iterative cluster-nma (icnma): A tool for generating conformational transitions in proteins,” Proteins: Struct. Funct. Bioinf. 74 (3), 760–776 (2009).Google Scholar
51. Maragakis, P. and Karplus, M., “Large amplitude conformational change in proteins explored with a plastic network model: Adenylate kinase,” J. Mol. Biol. 352 (4), 807–822 (2005).Google Scholar
52. Zheng, W., Brooks, B. R., Hummer, G., Protein conformational transitions explored by mixed elastic network models. Proteins: Struct. Funct. Bioinf. 69 (1), 43–57 (2007).Google Scholar
53. Zhu, F. and Hummber, G., “Gating transition of pentameric ligand-gated ion channels,” Biophys. J. 97 (9), 2456–2463 (2009).Google Scholar
54. Miayshita, O., Onuchic, J. N. and Wolynes, P. G., “Nonlinear elasticity, proteinquakes and the energy landscapes of functional transitions in proteins,” Proc. Natl. Acad. Sci. USA 100 (22), 12570–12575 (2003).Google Scholar
55. Miayshita, O., Wolynes, P. G. and Onuchic, J. N., “Simple energy landscape model for the kinetics of functional transitions in proteins,” J. Phys. Chem. B 5 (1959–1969), 109 (2005).Google Scholar
56. Chu, J. W. and Voth, G. A., “Coarse-grained free energy functions for studying protein conformational changes: A double-well network model,” Biophys. J. 93 (11), 3860–3871 (2007).Google Scholar
57. Franklin, J., Koehl, P., Doniach, S. and Delarue, M., “MinActionPath: Maximum likelihood trajectory for large-scale structural transitions in a coarse-grained locally harmonic energy landscape,” Nucleic Acids Res. 35(Web Server issue), W477–W482 (2007).Google Scholar
58. Elber, R. and Karplus, M., “A method for determining reaction paths in large molecules: Application to myoglobin,” Chem. Phys. Lett. 139 (5), 375–380 (1987).Google Scholar
59. Henkelmann, G. and Jónsson, H., “Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points,” J. Chem. Phys. 113, 9978–9985 (2000).Google Scholar
60. Weinan, E., Ren, W. and Vanden-Eijnden, E., “String method for the study of rare events,” Phys. Rev. B 66, 052301 (2002).Google Scholar
61. Chu, J. W., Trout, B. L. and Brooks, C. L. III, “A super-linear minimization scheme for the nudged elastic band method,” J. Chem. Phys. 119 (24), 12708–12717 (2003).Google Scholar
62. Bohner, M. U., Zeman, J., Smiatek, J., Arnold, A. and Kästner, J., “Nudged-elastic band used to find reaction coordinates based on the free energy,” J. Chem. Phys. 140 (7), 074109 (2014).Google Scholar
63. Olender, R. and Elber, R., “Yet another look at the steepest descent path,” J. Mol. Struct. THEOCHEM 398–399, 63–71 (1997).CrossRefGoogle Scholar
64. Maragliano, L., Fiser, A., Vanden-Eijnden, E. J. and Ciccotti, G., “String method in collective variables: Minimum free energy paths and isocommittor surfaces,” J. Chem. Phys. 125, 024106 (2006).Google Scholar
65. Maragliano, L. and Vanden-Eijnden, E., “On-the-fly string method for minimum free energy paths calculation,” Chem. Phys. Lett. 446 (1), 182–190 (2007).Google Scholar
66. Ren, W. and Vanden-Eijnden, E., “Finite temperature string method for the study of rare events,” J. Phys. Chem. B 109 (14), 6688–6693 (2005).Google Scholar
67. Ren, W., Vanden-Eijnden, E., Maragakis, P. and Weinan, E., “Transition pathways in complex systems: Application of the finite-temperature string method to the alanine dipeptide,” J. Chem. Phys. 123 (13), 134109 (2005).Google Scholar
68. Weinan, E., Ren, W. and Vanden-Eijnden, E., “Simplified and improved string method for computing the minimum energy paths in barrier-crossing events,” J. Chem. Phys. 126, 164103 (2007).Google Scholar
69. Goodrow, A., Bell, A. T. and Head-Gordon, M., “Development and application of a hybrid method involving interpolation and ab initio calculations for the determination of transition states,” J. Chem. Phys. 129 (17), 174109/1–174109/12 (2008).Google Scholar
70. Goodrow, A., Bell, A. T. and Head-Gordon, M., “Transition state-finding strategies for use with the growing string method,” J. Chem. Phys. 130 (24), 244108/1–244108/14 (2009).Google Scholar
71. Goodrow, A., Bell, A. T. and Head-Gordon, M., “A strategy for obtaining a more accurate transition state estimate using the growing string method,” Chem. Phys. Lett. 484 (4–6), 392–398 (2010).Google Scholar
72. Behn, A., Zimmerman, P. M., Bell, A. T. and Head-Gordon, M., “Efficient exploration of reaction paths via a freezing string method,” J. Chem. Phys. 135 (22), 224108–224116 (2011).Google Scholar
73. Gan, W., Yang, S. and Roux, B., “Atomistic view of the conformational activation of src kinase using the string method with swarms-of-trajectories,” Biophys. J. 97 (4), L8–L10 (2009).CrossRefGoogle ScholarPubMed
74. Olson, B. and Shehu, A., “Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface,” ACM Conference on Bioinformatics, Computational Biology (BCB), Washington, D. C. (Sep. 2013) pp. 430–439.Google Scholar
75. Olson, B. and Shehu, A., “Efficient Basin Hopping in the Protein Energy Surface,” IEEE International Conference on Bioinformatics and Biomedical, Philadelphia, PA (Oct. 2012) pp. 119–124.Google Scholar
76. Olson, B., De Jong, K. A. and Shehu, A., “Off-Lattice Protein Structure Prediction with Homologous Crossover,” Conference on Genetic and Evolutionary Computation (GECCO), ACM, New York, NY (2013) pp. 287–294.Google Scholar
77. Hashmi, I. and Shehu, A., “idDock+: Integrating machine learning in probabilistic search for protein-protein docking,” J. Comp. Biol. 22 (9), 1–18 (2015).Google Scholar
78. Hashmi, I. and Shehu, A., “Informatics-Driven Protein-Protein Docking,” ACM Conference on Bioinformatics, Computational Biology Workshops (BCBW), Washington, D. C. (Sep. 2013) pp. 772–779.Google Scholar
79. Olson, B., Hashmi, I., Molloy, K. and Shehu, A., “Basin hopping as a general and versatile optimization framework for the characterization of biological macromolecules,” Adv. AI J. 2012 (674832) (2012).Google Scholar
80. Berman, H. M., Henrick, K. and Nakamura, H., “Announcing the worldwide Protein Data Bank,” Nat. Struct. Biol. 10 (12), 980–980 (2003).Google Scholar
81. Clausen, R., Ma, B., Nussinov, R. and Shehu, A., “Mapping the conformation space of wildtype and mutant h-ras with a memetic, cellular, and multiscale evolutionary algorithm,” PLoS Comput. Biol. 11 (9), e1004470 (2015).Google Scholar
82. Kaufmann, K. W., Lemmon, G. H., DeLuca, S. L., Sheehan, J. H. and Meiler, J., “Practically useful: What the rosetta protein modeling suite can do for you,” Biochemistry 49 (14), 2987–2998 (2010).Google Scholar
83. Clausen, R. and Shehu, A., “Exploring the Structure Space of Wildtype Ras Guided by Experimental Data. ACM Conference on Bioinformatics, Computational Biology Workshops (BCBW), Washington, D. C. (Sep. 2013) pp. 757–764.CrossRefGoogle Scholar
84. Hartigan, J. A., Clustering Algorithms (John Wiley and Sons, New York 1975).Google Scholar
85. McLachlan, A. D., “A mathematical procedure for superimposing atomic coordinates of proteins,” Acta Crystallogr. A. 26 (6), 656–657 (1972).Google Scholar
86. Molloy, K. and Shehu, A., “Elucidating the ensemble of functionally-relevant transitions in protein systems with a robotics-inspired method,” BMC Struct. Biol. 13 (Suppl 1), S8 (2013).Google Scholar
87. Al-Bluwi, I., Vaisset, M., Siméon, T. and Cortés, J., “Modeling protein conformational transitions by a combination of coarse-grained normal mode analysis and robotics-inspired methods,” BMC Struct. Biol. 13 (Suppl 1), S8 (2013).Google Scholar
88. Bohlin, R. and Kavraki, L. E., “Path Planning using Lazy PRM,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1 (San Fransisco, CA, IEEE Press, Apr. 2000) pp. 521–528.Google Scholar
89. Pascoal, M. and Martins, E., “A New Implementation of Yen's Ranking Loopless Algorith,” Quart. J. Belg. French Ital. Oper. Res. Soc. 1 (2), 121–133 (2003).Google Scholar
90. Magrane, M. and the UniProt consortium, “UniProt knowledgebase: A hub of integrated protein data,” Database 2011 (bar009), 1–13 (2011).Google Scholar
91. DiDonato, M., Craig, L., Huff, M. E., Thayer, M. M., Cardoso, R. M., Kassmann, C. J., Lo, T. P., Bruns, C. K., Powers, E. T., Kelly, J. W., Getzoff, E. D. and Tainer, J. A., “ALS mutants of human superoxide dismutase form fibrous aggregates via framework destabilization,” J. Mol. Biol. 332 (3), 601–615 (2003).Google Scholar
92. Strange, R. W., Hough, M. A., Antonyuk, S. V. and Hasnain, S. S., “Structural evidence for a copper-bound carbonate intermediate in the peroxidase and dismutase activities of superoxide dismutase,” PLoS One 7 (9), e44811 (2012).Google Scholar
93. Humphrey, W., Dalke, A. and Schulten, K., “VMD - Visual Molecular Dynamics,” J. Mol. Graph. Model. 14 (1), 33–38 (1996). http://www.ks.uiuc.edu/Research/vmd/.CrossRefGoogle ScholarPubMed
94. Prior, I. A., Lewis, P. D. and Mattos, C., “A comprehensive survey of Ras mutations in cancer,” Cancer Res. 72 (10), 2457–2467 (2012).Google Scholar
95. Scheidig, A. J., Burmester, C. and Goody, R. S., “The pre-hydrolysis state of p21(ras) in complex with GTP: New insights into the role of water molecules in the GTP hydrolysis reaction of ras-like proteins,” Structure 7 (11), 1311–1324 (1999).CrossRefGoogle ScholarPubMed
96. Milburn, M. V., Tong, L., deVos, A. M., Brünger, A., Yamaizumi, Z., Nishimura, S. and Kim, S. H., “Molecular switch for signal transduction: Structural differences between active and inactive forms of protooncogenic ras proteins,” Science 247 (4945), 939–945 (1990).CrossRefGoogle ScholarPubMed
97. Buhrman, G., Wink, G. and Mattos, C., “Transformation efficiency of RasQ61 mutants linked to structural features of the switch regions in the presence of Raf,” Structure 15 (12), 1618–1629 (2007).Google Scholar
98. Buhrman, G., Holzapfel, G., Fetics, S. and Mattos, C., “Allosteric modulation of Ras positions Q61 for a direct role in catalysis,” Proc. Natl. Acad. Sci. U.S.A. 107 (11), 4931–4936 (2010).Google Scholar
99. Ratovitski, T., Corson, L. B., Strain, J., Wong, P., Cleveland, D. W., Culotta, V. C. and Borchelt, D. R., “Variation in the biochemical/biophysical properties of mutant superoxide dismutase 1 enzymes and the rate of disease progression in familial amyotrophic lateral sclerosis kindreds,” Hum. Mol. Genet. 8 (8), 1451–1460 (1999).Google Scholar
100. Wright, G. S., Antonyuk, S. V., Kershaw, N. M., Strange, R. W. and Hasnain, S., “Ligand binding and aggregation of pathogenic SOD1,” Nat. Commun. 4, 1758 (2013).Google Scholar
101. Case, D. A., Darden, T. A., Cheatham, T. E. III, Simmerling, C. L., Wang, J. Duke, R. E., Luo, R., Walker, R. C., Zhang, W., Merz, K. M., Roberts, B., Hayik, S., Roitberg, A., Seabra, G., Swails, J., Götz, A. W., Kolossváry, I., Wong, K. F., Paesani, F., Vanicek, J., Wolf, R. M., Liu, J., Wu, X., Brozell, S. R., Steinbrecher, T., Gohlke, H., Cai, Q., Ye, X., Wang, J., Hsieh, M.-J., Cui, G., Roe, D. R., Mathews, D. H., Seetin, M. G., Salomon-Ferrer, R., Sagui, C., Babin, V., Luchko, T., Gusarov, S., Kovalenko, A., and Kollman, P. A.. AMBER 12, University of California, San Francisco (2012).Google Scholar
102. Vogel, U. S., Dixon, R. A., Schaber, M. D., Diehl, R. E., Marshall, M. S., Scolnick, E. M., et al, “Cloning of bovine gap and its interaction with oncogenic Ras p21,” Nature 335 (6185), 90–93 (1988).Google Scholar
103. Gremer, L., Gilsbach, B., Ahmadian, M. R. and Wittinghofer, A., “Fluoride complexes of oncogenic Ras mutants to study the Ras-RasGap interaction,” Biol. Chem. 389 (9), 1163–1171 (2008).Google Scholar
104. Gibbs, J. B., Schaber, M. D., Allard, W. J., Sigal, I. S. and Scolnick, E. M., “Purification of Ras GTPase activating protein from bovine brain,” Proc. Natl. Acad. Sci. USA 85 (14), 5026–5030 (1988).CrossRefGoogle ScholarPubMed
105. Neal, S. E., Eccleston, J. F. and Webb, M. R., “Hydrolysis of GTP by p21NRAS, the NRAS protooncogene product, is accompanied by a conformational change in the wild-type protein: Use of a single fluorescent probe at the catalytic site,” Proc. Natl. Acad. Sci. USA 87 (9), 3562–3565 (1990).Google Scholar
106. Diaz, J. F., Wroblowski, B. and Engelborghs, Y., “Molecular dynamics simulation of the solution structures of Ha-ras-p21 GDP and GTP complexes: Flexibility, possible hinges, and levers of the conformational transition,” Biochemistry 34 (37), 12038–12047 (Sep. 1995).Google Scholar
107. Noé, F., Ille, F., Smith, J. C. and Fischer, S., “Automated computation of low-energy pathways for complex rearrangements in proteins: Application to the conformational switch of Ras p21,” Proteins: Struct. Funct. Bioinf. 59 (3), 534–544 (1990).Google Scholar
108. Nussinov, R., Jang, H. and Tsai, C.-J., “The structural basis for cancer treatment decisions,” Oncotarget 5 (17), 7285–7302 (2014).Google Scholar
109. Grant, B. J., Lukman, S., Hocker, H. J., Sayyah, J., Brown, J. H., Mc, J. A.Cammon and Gorfe, A. A., “Novel allosteric sites on ras for lead generation,” PLoS One 6 (10), e25711 (2011).Google Scholar
110. Molloy, K., Probabilistic Algorithms for Modeling Protein Structure and Dynamics Ph.D. Thesis (Fairfax, VA: George Mason University, 2015).Google Scholar