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Investigating Conformational Dynamics and Allostery in the p53 DNA-Binding Domain Using Molecular Simulations

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Allostery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2253))

Abstract

The p53 tumor suppressor is a multifaceted context-dependent protein, which is involved in multiple cellular pathways, with the ability to either keep the cells alive or to kill them through mechanisms such as apoptosis. To complicate this picture, cancer cells that express mutant p53 becomes addicted to the mutant activity, so that the mutant variant features a myriad of gain-of-function activities, opening different venues for therapy. This makes essential to think outside the box and apply new approaches to the study of p53 structure–(mis)function relationship to find new critical components of its pathway or to understand how known parts are interconnected, compete, or cooperate. In this context, I will here illustrate how to integrate different computational methods to the identification of possible allosteric effects transmitted from the DNA binding interface of p53 to regions for cofactor recruitment. The protocol can be extended to any other cases of study. Indeed, it does not necessarily apply only to the study of DNA-induced effects, but more broadly to the investigation of long-range effects induced by a biological partner that binds to a biomolecule of interest.

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References

  1. Zhang Y, Lozano G (2017) p53: multiple facets of a rubik’s cube. Annu Rev Cancer Biol 1:185–201. https://doi.org/10.1146/annurev-cancerbio-050216-121926

    Article  PubMed  Google Scholar 

  2. Vousden KH, Prives C (2009) Blinded by the light: the growing complexity of p53. Cell 137:413–431

    Article  CAS  PubMed  Google Scholar 

  3. Vogelstein B, Lane D, Levine AJ (2000) Surfing the p53 network. Nature 408:307–310

    Article  CAS  PubMed  Google Scholar 

  4. Aylon Y, Oren M (2016) The paradox of p53: what, how, and why? Cold Spring Harb Perspect Med 6(10):a026328

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Fischer M (2017) Census and evaluation of p53 target genes. Oncogene 36:3943–3956

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Luo Q, Beaver JM, Liu Y et al (2017) Dynamics of p53: a master decider of cell fate. Genes 8:66

    Article  PubMed Central  CAS  Google Scholar 

  7. White E (2016) Autophagy and p53. Cold Spring Harb Perspect Med 6:1–10

    Article  CAS  Google Scholar 

  8. Pant V, Lozano G (2014) Limiting the power of p53 through the ubiquitin proteasome pathway. Genes Dev 28:1739–1751

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kandoth C, McLellan MD, Vandin F et al (2013) Mutational landscape and significance across 12 major cancer types. Nature 502:333–339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Leroy B, Anderson M, Soussi T (2014) TP53 mutations in human cancer: database reassessment and prospects for the next decade. Hum Mutat 35:672–688

    Article  CAS  PubMed  Google Scholar 

  11. Brosh R, Rotter V (2009) When mutants gain new powers: news from the mutant p53 field. Nat Rev Cancer 9:701–713

    Article  CAS  PubMed  Google Scholar 

  12. Joerger AC, Fersht AR (2016) The p53 pathway: origins, inactivation in cancer, and emerging therapeutic approaches. Annu Rev Biochem 85:375–404. https://doi.org/10.1146/annurev-biochem-060815-014710

    Article  CAS  PubMed  Google Scholar 

  13. Wasylishen AR, Lozano G (2016) Attenuating the p53 pathway in human cancers: many means to the same end. Cold Spring Harb Perspect Med 6(8):a026211

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Lambrughi M, De Gioia L, Gervasio FL et al (2016) DNA-binding protects p53 from interactions with cofactors involved in transcription-independent functions. Nucleic Acids Res 44:9096–9109

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Green DR, Kroemer G (2009) Cytoplasmic functions of the tumour suppressor p53. Nature 458:1127–1130

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Speidel D (2010) Transcription-independent p53 apoptosis: an alternative route to death. Trends Cell Biol 20:14–24

    Article  CAS  PubMed  Google Scholar 

  17. Tasdemir E, Maiuri MC, Galluzzi L et al (2008) Regulation of autophagy by cytoplasmic p53. Nat Cell Biol 10:676–687

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Vaseva AV, Moll UM (2009) The mitochondrial p53 pathway. Biochim Biophys Acta Bioenerg 1787:414–420

    Article  CAS  Google Scholar 

  19. Kokontis JM, Wagner AJ, O’Leary M et al (2001) A transcriptional activation function of p53 is dispensable for and inhibitory of its apoptotic function. Oncogene 20:659–668

    Article  CAS  PubMed  Google Scholar 

  20. Leu JI-J, Dumont P, Hafey M et al (2004) Mitochondrial p53 activates Bak and causes disruption of a Bak–Mcl1 complex. Nat Cell Biol 6:443–450

    Article  CAS  PubMed  Google Scholar 

  21. Chipuk JE, Green DR (2008) How do BCL-2 proteins induce mitochondrial outer membrane permeabilization? Trends Cell Biol 18:157–164

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Follis AV, Llambi F, Ou L et al (2014) The DNA-binding domain mediates both nuclear and cytosolic functions of p53. Nat Struct Mol Biol 21:535–543

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhang X, Li CF, Zhang L et al (2016) TRAF6 restricts p53 mitochondrial translocation, apoptosis, and tumor suppression. Mol Cell 64:803–814

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Giorgi C, Bonora M, Sorrentino G et al (2015) p53 at the endoplasmic reticulum regulates apoptosis in a Ca 2+ −dependent manner. Proc Natl Acad Sci 112:1779–1784

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kroemer G, Bravo-San Pedro JM, Galluzzi L (2015) Novel function of cytoplasmic p53 at the interface between mitochondria and the endoplasmic reticulum. Cell Death Dis 6:e1698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Joerger AC, Fersht AR (2007) Structural biology of the tumor suppressor p53 and cancer-associated mutants. Adv Cancer Res 97:1–23

    Article  CAS  PubMed  Google Scholar 

  27. Dai C, Gu W (2010) P53 post-translational modification: deregulated in tumorigenesis. Trends Mol Med 16:528–536

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Petty TJ, Emamzadah S, Costantino L et al (2011) An induced fit mechanism regulates p53 DNA binding kinetics to confer sequence specificity. EMBO J 30:2167–2176

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cho Y, Gorina S, Jeffrey PD et al (1994) Crystal structure of a p53 tumor suppressor-DNA complex: understanding tumorigenic mutations. Science 265:346–355

    Article  CAS  PubMed  Google Scholar 

  30. Khoo KH, Andreeva A, Fersht AR (2009) Adaptive evolution of p53 thermodynamic stability. J Mol Biol 393:161–175

    Article  CAS  PubMed  Google Scholar 

  31. Soussi T, Curie M (2014) The TP53 gene network in a postgenomic era. Hum Mutat 35(6):641–642

    Article  CAS  PubMed  Google Scholar 

  32. Soussi T, Wiman KG (2015) TP53: an oncogene in disguise. Cell Death Differ 22:1239–1249

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Abramo MD, Besker N, Desideri A et al (2015) The p53 tetramer shows an induced- fit interaction of the C-terminal domain with the DNA-binding domain. Oncogene 35:3272–3281

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Chillemi G, Davidovich P, D’Abramo M et al (2013) Molecular dynamics of the full-length p53 monomer. Cell Cycle 12:3098–3108

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Terakawa T, Takada S (2015) p53 dynamics upon response element recognition explored by molecular simulations. Sci Rep 5:17107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Demir Ö, Ieong PU, Amaro RE (2017) Full-length p53 tetramer bound to DNA and its quaternary dynamics. Oncogene 36:1451–1460

    Article  CAS  PubMed  Google Scholar 

  37. Saha T, Kar RK, Sa G (2015) Structural and sequential context of p53: a review of experimental and theoretical evidence. Prog Biophys Mol Biol 117(2-3):250–263

    Article  CAS  PubMed  Google Scholar 

  38. Lu Q, Tan YH, Luo R (2007) Molecular dynamics simulations of p53 DNA-binding domain. J Phys Chem B 111:11538–11545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lukman S, Lane DP, Verma CS (2013) Mapping the structural and dynamical features of multiple p53 DNA binding domains: insights into loop 1 intrinsic dynamics. PLoS One 8:e80221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Pan Y, Nussinov R (2010) Lysine120 interactions with p53 response elements can allosterically direct p53 organization. PLoS Comput Biol 6:e1000878

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Pan Y (2008) p53-induced DNA bending: the interplay between p53. J Phys Chem B 112:6716–6724

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Fraser JA, Madhumalar A, Blackburn E et al (2010) A novel p53 phosphorylation site within the MDM2 ubiquitination signal II. A model in which phosphorylation at SER 269 induces a mutant. J Biol Chem 285:37773–37786

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Henzler-Wildman KA, Lei M, Thai V et al (2007) A hierarchy of timescales in protein dynamics is linked to enzyme catalysis. Nature 450:913–916

    Article  CAS  PubMed  Google Scholar 

  44. Tang C, Schwieters CD, Clore GM (2007) Open-to-closed transition in apo maltose-binding protein observed by paramagnetic NMR. Nature 449:1078–1082

    Article  CAS  PubMed  Google Scholar 

  45. Baldwin AJ, Kay LE (2009) NMR spectroscopy brings invisible protein states into focus. Nat Chem Biol 5:808–814

    Article  CAS  PubMed  Google Scholar 

  46. Papaleo E, Saladino G, Lambrughi M et al (2016) The role of protein loops and linkers in conformational dynamics and allostery. Chem Rev 116:6391–6423

    Article  CAS  PubMed  Google Scholar 

  47. Cui Q, Karplus M (2008) Allostery and cooperativity revisited. Protein Sci 17:1295–1307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Tsai C-J, Nussinov R (2014) A unified view of “how allostery works”. PLoS Comput Biol 10:e1003394

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Ribeiro AAST, Ortiz V (2016) A chemical perspective on allostery. Chem Rev 116:6488–6502

    Article  CAS  PubMed  Google Scholar 

  50. Bray D, Duke T (2004) Conformational spread: the propagation of allosteric states in large multiprotein complexes. Annu Rev Biophys Biomol Struct 33:53–73

    Article  CAS  PubMed  Google Scholar 

  51. Nussinov R, Tsai C-J (2015) Allostery without a conformational change? Revisiting the paradigm. Curr Opin Struct Biol 30:17–24

    Article  CAS  PubMed  Google Scholar 

  52. Feher VA, Durrant JD, Van Wart AT et al (2014) Computational approaches to mapping allosteric pathways. Curr Opin Struct Biol 25:98–103

    Article  CAS  PubMed  Google Scholar 

  53. Dror RO, Dirks RM, Grossman JP et al (2012) Biomolecular simulation: a computational microscope for molecular biology. Annu Rev Biophys 41:429–452

    Article  CAS  PubMed  Google Scholar 

  54. Papaleo E (2015) Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity. Front Mol Biosci 2:28

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Torchia DA (2015) NMR studies of dynamic biomolecular conformational ensembles. Prog Nucl Magn Reson Spectrosc 84–85:14–32

    Article  PubMed  CAS  Google Scholar 

  56. O’Rourke KF, Gorman SD, Boehr DD (2016) Biophysical and computational methods to analyze amino acid interaction networks in proteins. Comput Struct Biotechnol J 14:245–251

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Fraser JS, Clarkson MW, Degnan SC et al (2009) Hidden alternative structures of proline isomerase essential for catalysis. Nature 462:669–673

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Laio A, Gervasio FL (2008) Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep Prog Phys 71:126601

    Article  CAS  Google Scholar 

  59. Abrams C, Bussi G (2013) Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration. Entropy 16:163–199

    Article  CAS  Google Scholar 

  60. Luitz M, Bomblies R, Ostermeir K et al (2015) Exploring biomolecular dynamics and interactions using advanced sampling methods. J Phys Condens Matter 27:323101

    Article  PubMed  CAS  Google Scholar 

  61. Marino KA, Sutto L, Gervasio FL (2015) The effect of a widespread cancer-causing mutation on the inactive to active dynamics of the B-Raf kinase. J Am Chem Soc 137:5280–5283

    Article  CAS  PubMed  Google Scholar 

  62. Sutto L, Gervasio FL (2013) Effects of oncogenic mutations on the conformational free-energy landscape of EGFR kinase. Proc Natl Acad Sci 110:10616–10621

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Papaleo E, Sutto L, Gervasio FL et al (2014) Conformational changes and free energies in a proline isomerase. J Chem Theory Comput 10:4169–4174

    Article  CAS  PubMed  Google Scholar 

  64. Wang Y, Papaleo E, Lindorff-Larsen K (2016) Mapping transiently formed and sparsely populated conformations on a complex energy landscape. elife 5:e17505

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Palazzesi F, Barducci A, Tollinger M et al (2013) The allosteric communication pathways in KIX domain of CBP. Proc Natl Acad Sci U S A 110:14237–14242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Csermely P, Korcsmáros T, Kiss HJM et al (2013) Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 138:333–408

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Angelova K, Felline A, Lee M et al (2011) Conserved amino acids participate in the structure networks deputed to intramolecular communication in the lutropin receptor. Cell Mol Life Sci 68:1227–1239

    Article  CAS  PubMed  Google Scholar 

  68. Tiberti M, Invernizzi G, Lambrughi M et al (2014) PyInteraph: a framework for the analysis of interaction networks in structural ensembles of proteins. J Chem Inf Model 54:1537–1551

    Article  CAS  PubMed  Google Scholar 

  69. Papaleo E, Lindorff-larsen K, De Gioia L (2012) Paths of long-range communication in the E2 enzymes of family 3: a molecular dynamics investigation. Phys Chem Chem Phys 14:12515–12525

    Article  CAS  PubMed  Google Scholar 

  70. Whitley MJ, Lee AL (2009) Frameworks for understanding long-range intra-protein communication. Curr Protein Pept Sci 10:116–127

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Di Paola L, De Ruvo M, Paci P et al (2013) Protein contact networks: an emerging paradigm in chemistry. Chem Rev 113:1598–1613

    Article  PubMed  CAS  Google Scholar 

  72. Ribeiro AAST, Ortiz V (2015) Energy propagation and network energetic coupling in proteins. J Phys Chem A 119:1835–1846

    Article  CAS  Google Scholar 

  73. Papaleo E, Renzetti G, Tiberti M (2012) Mechanisms of intramolecular communication in a hyperthermophilic acylaminoacyl peptidase: a molecular dynamics investigation. PLoS One 7:e35686

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Di Paola L, Giuliani A (2015) Protein contact network topology: a natural language for allostery. Curr Opin Struct Biol 31:43–48

    Article  PubMed  CAS  Google Scholar 

  75. Salamanca Viloria J, Allega MF, Lambrughi M et al (2016) An optimal distance cutoff for contact-based protein structure networks using side chain center of masses. Sci Rep 7:2838

    Article  CAS  Google Scholar 

  76. Nygaard M, Terkelsen T, Olsen AV et al (2016) The mutational landscape of the oncogenic MZF1 SCAN domain in cancer. Front Mol Biosci 3:78

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Ng JWK, Lama D, Lukman S et al (2015) R248Q mutation—beyond p53-DNA binding. Proteins 83:2240–2250

    Article  CAS  PubMed  Google Scholar 

  78. Thayer KM, Quinn TR (2016) p53 R175H hydrophobic patch and H-bond reorganization observed by MD simulation. Biopolymers 105:176–185

    Article  CAS  PubMed  Google Scholar 

  79. Calhoun S, Daggett V (2011) Structural effects of the L145Q, V157F, and R282W cancer-associated mutations in the p53 DNA-binding core domain. Biochemistry 50:5345–5353

    Article  CAS  PubMed  Google Scholar 

  80. Bista M, Freund SM, Fersht AR (2012) Domain-domain interactions in full-length p53 and a specific DNA complex probed by methyl NMR spectroscopy. Proc Natl Acad Sci U S A 109:15752–15756

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Bethuyne J, De Gieter S, Zwaenepoel O et al (2014) A nanobody modulates the p53 transcriptional program without perturbing its functional architecture. Nucleic Acids Res 42:12928–12938

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Tsai CJ, del Sol A, Nussinov R (2008) Allostery: absence of a change in shape does not imply that allostery is not at play. J Mol Biol 378:1–11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Lange OF, Grubmüller H (2006) Can principal components yield a dimension reduced description of protein dynamics on long time scales? J Phys Chem B 110:22842–22852

    Article  CAS  PubMed  Google Scholar 

  84. Daidone I, Amadei A (2012) Essential dynamics: foundation and applications. Wiley Interdiscip Rev Comput Mol Sci 2:762–770

    Article  CAS  Google Scholar 

  85. Lindorff-Larsen K, Ferkinghoff-Borg J (2009) Similarity measures for protein ensembles. PLoS One 4:e4203

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Tiberti M, Papaleo E, Bengtsen T et al (2015) ENCORE: software for quantitative ensemble comparison. PLoS Comput Biol 11:e1004415

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Martín-García F, Papaleo E, Gomez-Puertas P et al (2015) Comparing molecular dynamics force fields in the essential subspace. PLoS One 10:e0121114

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Ramanathan A, Savol AJ, Langmead CJ et al (2011) Discovering conformational sub-states relevant to protein function. PLoS One 6:e15827

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Savol AJ, Burger VM, Agarwal PK et al (2011) QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin. Bioinformatics 27:i52–i60

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Wriggers W, Stafford KA, Shan Y et al (2009) Automated event detection and activity monitoring in long molecular dynamics simulations. J Chem Theory Comput 5:2595–2605

    Article  CAS  PubMed  Google Scholar 

  91. Kohlhoff KJ, Robustelli P, Cavalli A et al (2009) Fast and accurate predictions of protein NMR chemical shifts from interatomic distances. J Am Chem Soc 131:13894–13895

    Article  CAS  PubMed  Google Scholar 

  92. Sahakyan AB, Vranken WF, Cavalli A et al (2011) Structure-based prediction of methyl chemical shifts in proteins. J Biomol NMR 50:331–346

    Article  CAS  PubMed  Google Scholar 

  93. Li DW, Brüschweiler R (2012) PPM: a side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles. J Biomol NMR 54:257–265

    Article  CAS  PubMed  Google Scholar 

  94. Li D, Brüschweiler R (2015) PPM_One: a static protein structure based chemical shift predictor. J Biomol NMR 62:403–409

    Article  CAS  PubMed  Google Scholar 

  95. Natan E, Baloglu C, Pagel K et al (2011) Interaction of the p53 DNA-binding domain with its n-terminal extension modulates the stability of the p53 tetramer. J Mol Biol 409:358–368

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Liu Q, Kaneko S, Yang L et al (2004) Aurora-A abrogation of p53 DNA binding and transactivation activity by phosphorylation of serine 215. J Biol Chem 279:52175–52182

    Article  CAS  PubMed  Google Scholar 

  97. Fraser JA, Vojtesek B, Hupp TR (2010) A novel p53 phosphorylation site within the MDM2 ubiquitination signal: I. phosphorylation at SER269 in vivo is linked to inactivation of p53 function. J Biol Chem 285:37762–37772

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Invernizzi G, Tiberti M, Lambrughi M et al (2014) Communication routes in ARID domains between distal residues in helix 5 and the DNA-binding loops. PLoS Comput Biol 10:e1003744

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Lim CP, Cao X (2006) Structure, function, and regulation of STAT proteins. Mol BioSyst 2:536

    Article  CAS  PubMed  Google Scholar 

  100. Abraham MJ, Murtola T, Schulz R et al (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2:19–25

    Article  Google Scholar 

  101. Hess B, Kutzner C, van der Spoel D et al (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447

    Article  CAS  PubMed  Google Scholar 

  102. Pronk S, Páll S, Schulz R et al (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:845–854

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Bonomi M, Branduardi D, Bussi G et al (2009) PLUMED: a portable plugin for free-energy calculations with molecular dynamics. Comput Phys Commun 180:1961–1972

    Article  CAS  Google Scholar 

  104. Tribello GA, Bonomi M, Branduardi D et al (2014) PLUMED 2: new feathers for an old bird. Comput Phys Commun 185:604–613

    Article  CAS  Google Scholar 

  105. Seeber M, Felline A, Raimondi F et al (2011) Wordom: a user-friendly program for the analysis of molecular structures, trajectories, and free energy surfaces. J Comput Chem 32:1183–1194

    Article  CAS  PubMed  Google Scholar 

  106. Pasi M, Tiberti M, Arrigoni A et al (2012) xPyder: a PyMOL plugin to analyze coupled residues and their networks in protein structures. J Chem Inf Model 279:1–6

    Google Scholar 

  107. Baspinar A, Cukuroglu E, Nussinov R et al (2014) PRISM: a web server and repository for prediction of protein-protein interactions and modeling their 3D complexes. Nucleic Acids Res 42:W285–W289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Tuncbag N, Gursoy A, Nussinov R et al (2011) Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM. Nat Protoc 6:1341–1354

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Cho Y, Gorina S, Jeffrey PD et al (1994) Crystal structure of p53 tumor suppressor-DNA complex: understanding tumorigenic mutations. Science 265(5170):346–355

    Article  CAS  PubMed  Google Scholar 

  110. Dolinsky TJ, Czodrowski P, Li H et al (2007) PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res 35:522–525

    Article  Google Scholar 

  111. Mackerell AD, Feig M, Brooks CL (2004) Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J Comput Chem 25:1400–1415

    Article  CAS  PubMed  Google Scholar 

  112. Piana S, Lindorff-Larsen K, Shaw DE (2011) How robust are protein folding simulations with respect to force field parameterization? Biophys J 100:L47–L49

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Mackerell AD, Banavali NK (2000) All atom empirical force field for nucleic acids: II. Application to molecular dynamics simulations of DNA and RNA in solution. J Comput Chem 21:105–120

    Article  CAS  Google Scholar 

  114. Parsons DW, Li M, Zhang X et al (2011) The genetic landscape of the childhood cancer medulloblastoma. Science 331:435–439

    Article  CAS  PubMed  Google Scholar 

  115. Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926

    Article  CAS  Google Scholar 

  116. Bjelkmar P, Larsson P, Cuendet MA et al (2010) Implementation of the CHARMM force field in GROMACS: analysis of protein stability effects from correction Maps, virtual interaction sites, and water models. J Chem Theory Comput 6:459–466

    Article  CAS  PubMed  Google Scholar 

  117. Hess B, Bekker H, Berendsen H et al (1993) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 12:1463–1472

    Google Scholar 

  118. Essmann U, Perera L, Berkowitz ML et al (1995) A smooth particle mesh Ewald method. J Chem Phys 103:8577

    Article  CAS  Google Scholar 

  119. Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126:014101

    Article  PubMed  CAS  Google Scholar 

  120. Amadei A, Linssen AB, Berendsen HJ (1993) Essential dynamics of proteins. Proteins 17:412–425

    Article  CAS  PubMed  Google Scholar 

  121. Papaleo E, Pasi M, Tiberti M et al (2011) Molecular dynamics of mesophilic-like mutants of a cold-adapted enzyme: insights into distal effects induced by the mutations. PLoS One 6:e24214

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Atilgan AR, Akan P, Baysal C (2004) Small-world communication of residues and significance for protein dynamics. Biophys J 86:85–91

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Bussi G, Gervasio FL, Laio A et al (2006) Free-energy landscape for beta hairpin folding from combined parallel tempering and metadynamics. J Am Chem Soc 128:13435–13441

    Article  CAS  PubMed  Google Scholar 

  124. Bonomi M, Barducci A, Parrinello M (2009) Reconstructing the equilibrium Boltzmann distribution from well-tempered metadynamics. J Comput Chem 30:1615–1621

    Article  CAS  PubMed  Google Scholar 

  125. Mellacheruvu D, Wright Z, Couzens AL et al (2013) The CRAPome: a contaminant repository for affinity purification – mass spectrometry data. Nat Methods 10:730–736

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Kuzu G, Gursoy A, Nussinov R et al (2014) Exploiting conformational ensembles in modeling protein-protein interactions on the proteome scale. J Proteome Res 12:2641–2653

    Article  CAS  Google Scholar 

  127. Gao M, Skolnick J (2011) New benchmark metrics for protein-protein docking methods. Proteins 79:1623–1634

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by the ISCRA-CINECA HPC Grants (HP10BLFPW4 and HP10C8LO8N) and the EU-PRACE DECI project DyNet. I would like to thank Matteo Lambrughi for fruitful inputs in the writing of this protocol.

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Correspondence to Elena Papaleo .

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Papaleo, E. (2021). Investigating Conformational Dynamics and Allostery in the p53 DNA-Binding Domain Using Molecular Simulations. In: Di Paola, L., Giuliani, A. (eds) Allostery. Methods in Molecular Biology, vol 2253. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1154-8_13

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  • DOI: https://doi.org/10.1007/978-1-0716-1154-8_13

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1153-1

  • Online ISBN: 978-1-0716-1154-8

  • eBook Packages: Springer Protocols

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