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Most Probable Druggable Pockets in Mutant p53-Arg175His Clusters Extracted from Gaussian Accelerated Molecular Dynamics Simulations

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Abstract

p53, a tumor suppressor protein, is essential for preventing cancer development. Enhancing our understanding of the human p53 function and its modifications in carcinogenesis will aid in developing more highly effective strategies for cancer prevention and treatment. In this study, we have modeled five human p53 forms, namely, inactive, distal-active, proximal-active, distal-Arg175His mutant, and proximal-Arg175His mutant forms. These forms have been investigated using Gaussian accelerated molecular dynamics (GaMD) simulations in OPC water model at physiological temperature and pH. Our observations, obtained throughout \(200~\text {ns}\) of production run, are in good agreement with the relevant results in the classical molecular dynamics (MD) studies. Therefore, GaMD method is more economic and efficient method than the classical MD method for studying biomolecular systems. The featured dynamics of the five human p53-DBD forms include noticeable conformational changes of L1 and \({\alpha }1\)–\({\beta }5\) loops as well as \({\beta }6\)–\({\beta }7\) and \({\beta }7\)–\({\beta }8\) turns. We have identified two clusters that represent two distinct conformational states in each p53-DBD form. The free-energy profiles of these clusters demonstrate the flexibility of the protein to undergo a conformational transition between the two clusters. We have predicted two out of seven possible druggability pockets on the clusters of the Arg175His forms. These two druggability pockets are near the mutation site and are expected to be actual pockets, which will be helpful for the compound clinical progression studies.

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The authors declare that the data that supports the findings of this study are available in the supplementary material of this article.

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References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424

    Article  PubMed  Google Scholar 

  2. Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin DM, Pineros M, Znaor A, Bray F (2019) Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer 144:1941–1953

    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. Hong B, van den Heuvel APJ, Prabhu VV, Zhang S, El-Deiry WS (2014) Targeting tumor suppressor p53 for cancer therapy: strategies, challenges and opportunities. Curr Drug Targets 15:80–89

    Article  CAS  PubMed  Google Scholar 

  5. Joerger AC, Fersht AR (2010) The tumor suppressor p53: from structures to drug discovery. Cold Spring Harb Perspect Biol 2:a000919

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kruiswijk F, Labuschagne CF, Vousden KH (2015) p53 in survival, death and metabolic health: a lifeguard with a licence to kill. Nat Rev Mol Cell Biol 16:393–405

    Article  CAS  PubMed  Google Scholar 

  7. Kruse J-P, Gu W (2009) Modes of p53 regulation. Cell 137:609–622

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Joerger AC, Fersht AR (2008) Structural biology of the tumor suppressor p53. Annu Rev Biochem 77:557–582

    Article  CAS  PubMed  Google Scholar 

  9. Pavletich NP, Chambers KA, Pabo CO (1993) The DNA-binding domain of p53 contains the four conserved regions and the major mutation hot spots. Genes Dev 7:2556–2564

    Article  CAS  PubMed  Google Scholar 

  10. Jeffrey PD, Gorina S, Pavletich NP (1995) Crystal structure of the tetramerization domain of the p53 tumor suppressor at 1.7 angstroms. Science 267:1498–1502

    Article  CAS  PubMed  Google Scholar 

  11. Marcel V, Tran PLT, Sagne C, Martel-Planche G, Vaslin L, Teulade-Fichou M-P, Hall J, Mergny J-L, Hainaut P, Van Dyck E (2011) G-quadruplex structures in TP53 intron 3: role in alternative splicing and in production of p53 mRNA isoforms. Carcinogenesis 32:271–278

    Article  CAS  PubMed  Google Scholar 

  12. Chuikov S, Kurash JK, Wilson JR, Xiao B, Justin N, Ivanov GS, McKinney K, Tempst P, Prives C, Gamblin SJ, Barlev NA, Reinberg D (2004) Regulation of p53 activity through lysine methylation. Nature 432:353–360

    Article  CAS  PubMed  Google Scholar 

  13. Sheng Y, Saridakis V, Sarkari F, Duan S, Wu T, Arrowsmith CH, Frappier L (2006) Molecular recognition of p53 and MDM2 by USP7/HAUSP. Nat Struct Mol Biol 13:285–291

    Article  CAS  PubMed  Google Scholar 

  14. Feng H, Jenkins LMM, Durell SR, Hayashi R, Mazur SJ, Cherry S, Tropea JE, Miller M, Wlodawer A, Appella E, Bai Y (2009) Structural basis for p300 Taz2-p53 TAD1 binding and modulation by phosphorylation. Structure 17:202–210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Schumacher B, Mondry J, Thiel P, Weyand M, Ottmann C (2010) Structure of the p53 C-terminus bound to 14-3-3: implications for stabilization of the p53 tetramer. FEBS Lett 584:1443–1448

    Article  CAS  PubMed  Google Scholar 

  16. Wang L, Li L, Zhang H, Luo X, Dai J, Zhou S, Gu J, Zhu J, Atadja P, Lu C, Li E, Zhao K (2011) Structure of human SMYD2 protein reveals the basis of p53 tumor suppressor methylation. J Biol Chem 286:38725–38737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Tong Q, Mazur SJ, Rincon-Arano H, Rothbart SB, Kuznetsov DM, Cui G, Liu WH, Gete Y, Klein BJ, Jenkins L, Mer G, Kutateladze AG, Strahl BD, Groudine M, Appella E, Kutateladze TG (2015) An acetyl-methyl switch drives a conformational change in p53. Structure 23:322–331

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Martinez-Zapien D, Ruiz FX, Poirson J, Mitschler A, Ramirez J, Forster A, Cousido-Siah A, Masson M, Pol SV, Podjarny A, Travé G, Zanier K (2016) Structure of the E6/E6AP/p53 complex required for HPV-mediated degradation of p53. Nature 529:541–545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kitayner M, Rozenberg H, Kessler N, Rabinovich D, Shaulov L, Haran TE, Shakked Z (2006) Structural basis of DNA recognition by p53 tetramers. Mol Cell 22:741–753

    Article  CAS  PubMed  Google Scholar 

  20. Ghosh R, Kaypee S, Shasmal M, Kundu TK, Roy S, Sengupta J (2019) Tumor suppressor p53-mediated structural reorganization of the transcriptional coactivator p300. Biochemistry 58:3434–3443

    Article  CAS  PubMed  Google Scholar 

  21. Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N, Boutselakis H, Cole CG, Creatore C, Dawson E, Fish P, Harsha B, Hathaway C, Jupe SC, Kok CY, Noble K, Ponting L, Ramshaw CC, Rye CE, Speedy HE, Stefancsik R, Thompson SL, Wang S, Ward S, Campbell PJ, Forbes SA (2018) COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res 47:D941–D947

    Article  PubMed Central  Google Scholar 

  22. Butler JS, Loh SN (2003) Structure, function, and aggregation of the zinc-free form of the p53 DNA binding domain. Biochemistry 42:2396–2403

    Article  CAS  PubMed  Google Scholar 

  23. Butler JS, Loh SN (2006) Folding and misfolding mechanisms of the p53 DNA binding domain at physiological temperature. Protein Sci 15:2457–2465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lane DP, Cheok CF, Lain S (2010) p53-based cancer therapy. Cold Spring Harb Perspect Biol 2:a001222

    Article  PubMed  PubMed Central  Google Scholar 

  25. Hussein HA, Geneix C, Petitjean M, Borrel A, Flatters D, Camproux A-C (2017) Global vision of druggability issues: applications and perspectives. Drug Discovery Today 22:404–415

    Article  Google Scholar 

  26. Miao Y, Feher VA, McCammon JA (2015) Gaussian accelerated molecular dynamics: unconstrained enhanced sampling and free energy calculation. J Chem Theory Comput 11:3584–3595

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Miao Y (2018) Acceleration of biomolecular kinetics in Gaussian accelerated molecular dynamics. J Chem Phys 149:072308

    Article  PubMed  Google Scholar 

  28. Bhattarai A, Miao Y (2018) Gaussian accelerated molecular dynamics for elucidation of drug pathways. Expert Opin Drug Discovery 13:1055–1065

    Article  CAS  Google Scholar 

  29. Wang J, Miao Y (2019) Mechanistic insights into specific G protein interactions with adenosine receptors. J Phys Chem B 123:6462–6473

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bhattarai A, Wang J, Miao Y (2020) G-protein-coupled receptor-membrane interactions depend on the receptor activation state. J Comput Chem 41:460–471

    Article  CAS  PubMed  Google Scholar 

  31. Wang Y, Rosengarth A, Luecke H (2007) Structure of the human p53 core domain in the absence of DNA. Acta Crystallogr Sect D: Biol Crystallogr 63:276–281

    Article  CAS  Google Scholar 

  32. Chen Y, Zhang X, Machado ACD, Ding Y, Chen Z, Qin PZ, Rohs R, Chen L (2013) Structure of p53 binding to the BAX response element reveals DNA unwinding and compression to accommodate base-pair insertion. Nucleic Acids Res 41:8368–8376

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kuszewski J, Gronenborn AM, Clore GM (1999) Improving the packing and accuracy of NMR structures with a pseudopotential for the radius of gyration. J Am Chem Soc 21:2337–2338

    Article  Google Scholar 

  34. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

    Article  CAS  PubMed  Google Scholar 

  35. Šali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815

    Article  PubMed  Google Scholar 

  36. Raman S, Vernon R, Thompson J, Tyka M, Sadreyev R, Pei J, Kim D, Kellogg E, Dimaio F, Lange O, Kinch L, Sheffler W, Kim BH, Das R, Grishin NV, Baker D (2009) Structure prediction for CASP8 with all-atom refinement using Rosetta. Proteins Struct Funct Bioinf 77:89–99

    Article  CAS  Google Scholar 

  37. Song Y, DiMaio F, Wang RY-R, Kim D, Miles C, Brunette T, Thompson J, Baker D (2013) High-resolution comparative modeling with RosettaCM. Structure 21:1735–1742

    Article  CAS  PubMed  Google Scholar 

  38. Case DA, Cheatham TE III, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The amber biomolecular simulation programs. J Comput Chem 26:1668–1688

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Salomon-Ferrer R, Case DA, Walker RC (2013) An overview of the amber biomolecular simulation package. WIREs Comput Mol Sci 3:198–210

    Article  CAS  Google Scholar 

  40. Case DA, Belfon K, Ben-Shalom IY, Brozell SR, Cerutti DS, Cheatham TE III, Cruzeiro VWD, Darden TA, Duke RE, Giambasu G, Gilson MK, Gohlke H, Goetz AW, Harris R, Izadi S, Izmailov SA, Kasavajhala K, Kovalenko A, Krasny R, Kurtzman T, Lee TS, LeGrand S, Li P, Lin C, Liu J, Luchko T, Luo R, Man V, Merz KM, Miao Y, Mikhailovskii O, Monard G, Nguyen H, Onufriev A, Pan F, Pantano S, Qi R, Roe DR, Roitberg A, Sagui C, Schott-Verdugo S, Shen J, Simmerling CL, Skrynnikov NR, Smith J, Swails J, Walker RC, Wang J, Wilson L, Wolf RM, Wu X, Xiong Y, Xue Y, York DM, Kollman PA (2020) AMBER 2020. University of California, Oakland

    Google Scholar 

  41. Wright JD, Noskov SY, Lim C (2002) Factors governing loss and rescue of DNA binding upon single and double mutations in the p53 core domain. Nucleic Acids Res 30:1563–1574

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 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 

  43. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Burley SK, Berman HM, Bhikadiya C, Bi C, Chen L, Costanzo LD, Christie C, Dalenberg K, Duarte JM, Dutta S, Feng Z, Ghosh S, Goodsell DS, Green RK, Guranović V, Guzenko D, Hudson BP, Kalro T, Liang Y, Lowe R, Namkoong H, Peisach E, Periskova I, Prlić A, Randle C, Rose A, Rose P, Sala R, Sekharan M, Shao C, Tan L, Tao Y-P, Valasatava Y, Voigt M, Westbrook J, Woo J, Yang H, Young J, Zhuravleva M, Zardecki C (2019) RCSB protein data bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res 47:D464–D474

    Article  CAS  PubMed  Google Scholar 

  45. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11:3696–3713

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Izadi S, Anandakrishnan R, Onufriev AV (2014) Building water models: a different approach. J Phys Chem Lett 5:3863–3871

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Li P, Song LF, Merz KM Jr (2015) Systematic parameterization of monovalent ions employing the nonbonded model. J Chem Theory Comput 11:1645–1657

    Article  CAS  PubMed  Google Scholar 

  48. Li P, Merz KM Jr (2014) Taking into account the ion-induced dipole interaction in the nonbonded model of ions. J Chem Theory Comput 10:289–297

    Article  CAS  PubMed  Google Scholar 

  49. Jorgensen WL, Chandrasekhar J, Madura JD (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935

    Article  CAS  Google Scholar 

  50. Tian C, Kasavajhala K, Belfon KAA, Raguette L, Huang H, Migues AN, Bickel J, Wang Y, Pincay J, Wu Q, Simmerling C (2020) ff19SB: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J Chem Theory Comput 16:528–552

    Article  PubMed  Google Scholar 

  51. Duan J, Nilsson L (2006) Effect of Zn\(^{2+}\) on DNA recognition and stability of the p53 DNA-binding domain. Biochemistry 45:7483–7492

    Article  CAS  PubMed  Google Scholar 

  52. Chillemi G, Davidovich P, D’Abramo M, Mametnabiev T, Garabadzhiu AV, Desideri A, Melino G (2013) Molecular dynamics of the full-length p53 monomer. Cell Cycle 12:3098–3108

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Peters MB, Yang Y, Wang B, Füsti-Molnár L, Weaver MN, Merz KM Jr (2010) Structural survey of zinc-containing proteins and development of the zinc AMBER force field (ZAFF). J Chem Theory Comput 6:2935–2947

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Pastor RW, Brooks BR, Szabo A (1988) An analysis of the accuracy of Langevin and molecular dynamics algorithms. Mol Phys 65:1409–1419

    Article  Google Scholar 

  55. Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690

    Article  CAS  Google Scholar 

  56. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: An N log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092

    Article  CAS  Google Scholar 

  57. van Gunsteren WF, Berendsen HJC (1977) Algorithms for macromolecular dynamics and constraint dynamics. Mol Phys 34:1311–1327

    Article  Google Scholar 

  58. R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  59. Van Rossum G, Drake FL (2009) Python 3 reference manual. CreateSpace, Scotts Valley, CA

    Google Scholar 

  60. Roe DR, Cheatham TE III (2013) PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data. J Chem Theory Comput 9:3084–3095

    Article  CAS  PubMed  Google Scholar 

  61. Grant BJ, Rodrigues APC, ElSawy KM, McCammon JA, Caves LSD (2006) Bio3d: an R package for the comparative analysis of protein structures. Bioinformatics 22:2695–2696

    Article  CAS  PubMed  Google Scholar 

  62. Skjaeven L, Yao X-Q, Scarabelli G, Grant BJ (2014) Integrating protein structural dynamics and evolutionary analysis with Bio3D. BMC Bioinf 15:399

    Article  Google Scholar 

  63. Skjærven L, Jariwala S, Yao X-Q, Grant BJ (2016) Online interactive analysis of protein structure ensembles with Bio3D-web. Bioinformatics 32:3510–3512

    PubMed  PubMed Central  Google Scholar 

  64. Lobanov MY, Bogatyreva NS, Galzitskaya OV (2008) Radius of gyration as an indicator of protein structure compactness. Mol Biol 42:623–628

    Article  CAS  Google Scholar 

  65. David CC, Jacobs DJ (2014) Principal component analysis: a method for determining the essential dynamics of proteins. In: Livesay DR (ed) Protein dynamics: methods and protocols, vol 1084. Humana Press, Totowa, NJ, pp 193–226

    Chapter  Google Scholar 

  66. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Gr 14:33–38

    Article  CAS  Google Scholar 

  67. Wolf A, Kirschner KN (2013) Principal component and clustering analysis on molecular dynamics data of the ribosomal L11-23S subdomain. J Mol Model 19:539–549

    Article  CAS  PubMed  Google Scholar 

  68. Charrad M, Ghazzali N, Boiteau V, Niknafs A (2014) NbClust: an R package for determining the relevant number of clusters in a data set. J Stat Softw 61:1–36

    Article  Google Scholar 

  69. Miao Y, Sinko W, Pierce L, Bucher D, Walker RC, McCammon JA (2014) Improved reweighting of accelerated molecular dynamics simulations for free energy calculation. J Chem Theory Comput 10:2677–2689

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Rodríguez A, Tsallis C (2010) A generalization of the cumulant expansion. Application to a scale-invariant probabilistic model. J Math Phys 51:073301

    Article  Google Scholar 

  71. Hussein HA, Borrel A, Geneix C, Petitjean M, Regad L, Camproux A-C (2015) PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins. Nucleic Acids Res 43:W436–W442

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Chen VB, Arendall WB III, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, Murray LW, Richardson JS, Richardson DC (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr Sect D: Biol Crystallogr D66:12–21

    Article  Google Scholar 

  73. Benkert P, Biasini M, Schwede T (2011) Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27:343–350

    Article  CAS  PubMed  Google Scholar 

  74. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, de Beer TAP, Rempfer C, Bordoli L, Lepore R, Schwede T (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46:W296–W303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Bowie JU, Lüthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170

    Article  CAS  PubMed  Google Scholar 

  76. Roland Lüthy DE, Bowie James U (1992) Assessment of protein models with three-dimensional profiles. Nature 356:83–85

    Article  Google Scholar 

  77. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2:1511–1519

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Pontius J, Richelle J, Wodak SJ (1996) Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 264:121–136

    Article  CAS  PubMed  Google Scholar 

  79. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291

    Article  CAS  Google Scholar 

  80. Pradhan MR, Siau JW, Kannan S, Nguyen MN, Ouaray Z, Kwoh CK, Lane DP, Ghadessy F, Verma CS (2019) Simulations of mutant p53 DNA binding domains reveal a novel druggable pocket. Nucleic Acids Res 47:1637–1652

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Offutt TL, Ieong PU, Demir O, Amaro RE (2018) Dynamics and molecular mechanisms of p53 transcriptional activation. Biochemistry 57:6528–6537

    Article  CAS  PubMed  Google Scholar 

  82. Hess B (2002) Convergence of sampling in protein simulations. Phys Rev E: Stat Nonlinear Soft Matter Phys 65:031910

    Article  Google Scholar 

  83. Pérez-Canadillas JM, Tidow H, Freund SMV, Rutherford TJ, Ang HC, Fersht AR (2006) Solution structure of p53 core domain: structural basis for its instability. Proc Natl Acad Sci USA 103:2109–2114

    Article  Google Scholar 

  84. 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 

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

    Article  CAS  PubMed  Google Scholar 

  86. Chen Y, Dey R, Chen L (2010) Crystal structure of the p53 core domain bound to a full consensus site as a self-assembled tetramer. Structure 18:246–256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Xu J, Reumers J, Couceiro JR, Smet FD, Gallardo R, Rudyak S, Cornelis A, Rozenski J, Zwolinska A, Marine J-C, Lambrechts D, Suh Y-A, Rousseau F, Schymkowitz J (2011) Gain of function of mutant p53 by coaggregation with multiple tumor suppressors. Nat Chem Biol 7:285–295

    Article  CAS  PubMed  Google Scholar 

  88. Connolly ML (1983) Analytical molecular surface calculation. J Appl Crystallogr 16:548–558

    Article  CAS  Google Scholar 

  89. Eldar A, Rozenberg H, Diskin-Posner Y, Rohs R, Shakked Z (2013) Structural studies of p53 inactivation by DNA-contact mutations and its rescue by suppressor mutations via alternative protein-DNA interactions. Nucleic Acids Res 41:8748–8759

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 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 

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Acknowledgements

We acknowledge the Center for Computational Sciences at University of Kentucky (Lexington, KY, USA) for allocations of compute time on the high performance computing facility (Lipscomb Cluster). Molecular graphics and analyses performed with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311.

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  1. Department of Chemistry, School of Science, The University of Jordan, Amman, 11942, Jordan

    Morad Mustafa & Mohammed Gharaibeh

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Mustafa, M., Gharaibeh, M. Most Probable Druggable Pockets in Mutant p53-Arg175His Clusters Extracted from Gaussian Accelerated Molecular Dynamics Simulations. Protein J 41, 27–43 (2022). https://doi.org/10.1007/s10930-022-10041-0

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