The pre-tetramerization loop (PTL) of the human tumor suppressor protein p53 is an intrinsically disordered region (IDR) necessary for the tetramerization process, and its flexibility contributes to the essential conformational changes needed. Although the IDR can be accurately simulated in the traditional manner of molecular dynamics (MD) with the end-to-end distance (EEdist) unhindered, we sought to explore the effects of restraining the EEdist to the values predicted by electron microscopy (EM) and other distances. Simulating the PTL trajectory with a restrained EEdist , we found an increased agreement of nuclear magnetic resonance (NMR) chemical shifts with experiments. Additionally, we observed a plethora of secondary structures and contacts that only appear when the trajectory is restrained. Our findings expand the understanding of the tetramerization of p53 and provide insight into how mutations could make the protein impotent. In particular, our findings demonstrate the importance of restraining the EEdist in studying IDRs and how their conformations change under different conditions. Our results provide a better understanding of the PTL and the conformational dynamics of IDRs in general, which are useful for further studies regarding mutations and their effects on the activity of p53.

Division of Computational Chemistry Department of Chemistry Lund University P O Box 124 SE 221 00 Lund Sweden

Faculty of Pharmacy in Hradec Králové Charles University Akademika Heyrovského 1203 8 500 05 Hradec Králové Czech Republic

LINXS Institute of Advanced Neutron and 10 ray Science Scheelevägen 19 SE 233 70 Lund Sweden

MAX 4 Laboratory Lund Institute of Advanced Neutron and 10 ray Science Scheelevägen 19 SE 223 770 Lund Sweden

Zobrazit více v PubMed

Rutkowski R.; Hofmann K.; Gartner A. Phylogeny and function of the invertebrate p53 superfamily. Cold Spring Harbor Perspect. Biol. 2010, 2, a001131.10.1101/cshperspect.a001131. PubMed DOI PMC

Vousden K. H. Activation of the p53 tumor suppressor protein. Biochim. Biophys. Acta, Rev. Cancer 2002, 1602, 47–59. 10.1016/S0304-419X(02)00035-5. PubMed DOI

Ryan K. M.; Phillips A. C.; Vousden K. H. Regulation and function of the p53 tumor suppressor protein. Curr. Opin. Cell Biol. 2001, 13, 332–337. 10.1016/S0955-0674(00)00216-7. PubMed DOI

Oren M. Regulation of the p53 tumor suppressor protein. J. Biol. Chem. 1999, 274, 36031–36034. 10.1074/jbc.274.51.36031. PubMed DOI

Surget S.; Khoury M. P.; Bourdon J.-C. Uncovering the role of p53 splice variants in human malignancy: a clinical perspective. OncoTargets Ther. 2013, 7, 57–68. 10.2147/OTT.S53876. PubMed DOI PMC

Kato S.; Han S.-Y.; Liu W.; Otsuka K.; Shibata H.; Kanamaru R.; Ishioka C. Understanding the function–structure and function–mutation relationships of p53 tumor suppressor protein by high-resolution missense mutation analysis. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 8424–8429. 10.1073/pnas.1431692100. PubMed DOI PMC

Ozaki T.; Nakagawara A. Role of p53 in cell death and human cancers. Cancers 2011, 3, 994–1013. 10.3390/cancers3010994. PubMed DOI PMC

Rizzotto D.; Englmaier L.; Villunger A. At a crossroads to cancer: how p53-induced cell fate decisions secure genome integrity. Int. J. Mol. Sci. 2021, 22, 10883.10.3390/ijms221910883. PubMed DOI PMC

Belyi V. A.; Ak P.; Markert E.; Wang H.; Hu W.; Puzio-Kuter A.; Levine A. J. The origins and evolution of the p53 family of genes. Cold Spring Harbor Perspect. Biol. 2009, 2, a001198.10.1101/cshperspect.a001198. PubMed DOI PMC

Belyi V. A.; Ak P.; Markert E.; Wang H.; Hu W.; Puzio-Kuter A.; Levine A. J. The origins and evolution of the p53 family of genes. Cold Spring Harbor Perspect. Biol. 2010, 2, a001198.10.1101/cshperspect.a001198. PubMed DOI PMC

Steele R. J.; Thompson A. M.; Hall P. A.; Lane D. P. The p53 tumour suppressor gene. Br. J. Surg. 2003, 85, 1460–1467. 10.1046/j.1365-2168.1998.00910.x. PubMed DOI

Petitjean A.; Achatz M. I.; Borresen-Dale A. L.; Hainaut P.; Olivier M. TP53 mutations in human cancers: Functional selection and impact on cancer prognosis and outcomes. Oncogene 2007, 26, 2157–2165. 10.1038/sj.onc.1210302. PubMed DOI

Marei H. E.; Althani A.; Afifi N.; Hasan A.; Caceci T.; Pozzoli G.; Morrione A.; Giordano A.; Cenciarelli C. p53 signaling in cancer progression and therapy. Cancer Cell Int. 2021, 21, 703.10.1186/s12935-021-02396-8. PubMed DOI PMC

Natan E.; Baloglu C.; Pagel K.; Freund S. M.; Morgner N.; Robinson C. V.; Fersht A. R.; Joerger A. C. Interaction of the p53 DNA-binding domain with its N-terminal extension modulates the stability of the P53 tetramer. J. Mol. Biol. 2011, 409, 358–368. 10.1016/j.jmb.2011.03.047. PubMed DOI PMC

Han C. W.; Lee H. N.; Jeong M. S.; Park S. Y.; Jang S. B. Structural basis of the p53 DNA binding domain and Puma Complex. Biochem. Biophys. Res. Commun. 2021, 548, 39–46. 10.1016/j.bbrc.2021.02.049. PubMed DOI

Marques M. A.; de Oliveira G. A.; Silva J. L. The chameleonic behavior of p53 in health and disease: the transition from a client to an aberrant condensate scaffold in cancer. Essays Biochem. 2022, 66, 1023–1033. 10.1042/EBC20220064. PubMed DOI

Baughman H. E.; Narang D.; Chen W.; Villagrán Suárez A. C.; Lee J.; Bachochin M. J.; Gunther T. R.; Wolynes P. G.; Komives E. A. An intrinsically disordered transcription activation domain increases the DNA binding affinity and reduces the specificity of NFκB p50/RelA. J. Biol. Chem. 2022, 298, 102349.10.1016/j.jbc.2022.102349. PubMed DOI PMC

Baptiste N.; Friedlander P.; Chen X.; Prives C. The proline-rich domain of p53 is required for cooperation with anti-neoplastic agents to promote apoptosis of tumor cells. Oncogene 2002, 21, 9–21. 10.1038/sj.onc.1205015. PubMed DOI

Jenkins L. M.; Durell S. R.; Mazur S. J.; Appella E. P53 N-terminal phosphorylation: A defining layer of complex regulation. Carcinogenesis 2012, 33, 1441–1449. 10.1093/carcin/bgs145. PubMed DOI PMC

Luciani M.; Hutchins J. R.; Zheleva D.; Hupp T. R. The C-terminal regulatory domain of P53 contains a functional docking site for cyclin A. J. Mol. Biol. 2000, 300, 503–518. 10.1006/jmbi.2000.3830. PubMed DOI

Chène P. The role of tetramerization in p53 function. Oncogene 2001, 20, 2611–2617. 10.1038/sj.onc.1204373. PubMed DOI

Jeffrey P. D.; Gorina S.; Pavletich N. P. Crystal structure of the tetramerization domain of the p53 tumor suppressor at 1.7 angstroms. Science 1995, 267, 1498–1502. 10.1126/science.7878469. PubMed DOI

Gencel-Augusto J.; Lozano G. P53 tetramerization: At the center of the dominant-negative effect of mutant p53. Genes Dev. 2020, 34, 1128–1146. 10.1101/gad.340976.120. PubMed DOI PMC

Solares M. J.; Jonaid G. M.; Luqiu W. Y.; Berry S.; Khadela J.; Liang Y.; Evans M. C.; Pridham K. J.; Dearnaley W. J.; Sheng Z.; Kelly D. F. High-Resolution Imaging of Human Cancer Proteins Using Microprocessor Materials. ChemBioChem 2022, 23, e20220031010.1002/cbic.202200310. PubMed DOI PMC

Dosztanyi Z.; Csizmok V.; Tompa P.; Simon I. IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics 2005, 21, 3433–3434. 10.1093/bioinformatics/bti541. PubMed DOI

Gopal S. M.; Wingbermühle S.; Schnatwinkel J.; Juber S.; Herrmann C.; Schäfer L. V. Conformational preferences of an intrinsically disordered protein domain: A case study for modern force fields. J. Phys. Chem. B 2020, 125, 24–35. 10.1021/acs.jpcb.0c08702. PubMed DOI

Na J.-H.; Lee W.-K.; Yu Y. G. How do we study the dynamic structure of unstructured proteins: a case study on Nopp140 as an example of a large, intrinsically disordered protein. Int. J. Mol. Sci. 2018, 19, 381.10.3390/ijms19020381. PubMed DOI PMC

Stanley N.; Esteban-Martín S.; De Fabritiis G. Progress in studying intrinsically disordered proteins with atomistic simulations. Prog. Biophys. Mol. Biol. 2015, 119, 47–52. 10.1016/j.pbiomolbio.2015.03.003. PubMed DOI

Akbayrak I. Y.; Caglayan S. I.; Ozcan Z.; Uversky V. N.; Coskuner-Weber O. Current challenges and limitations in the studies of intrinsically disordered proteins in neurodegenerative diseases by computer simulations. Curr. Alzheimer Res. 2021, 17, 805–818. 10.2174/1567205017666201109094908. PubMed DOI

Carballo-Pacheco M.; Strodel B. Comparison of force fields for Alzheimer’s A: A case study for intrinsically disordered proteins. Protein Sci. 2017, 26, 174–185. 10.1002/pro.3064. PubMed DOI PMC

Mu J.; Liu H.; Zhang J.; Luo R.; Chen H.-F. Recent force field strategies for intrinsically disordered proteins. J. Chem. Inf. Model. 2021, 61, 1037–1047. 10.1021/acs.jcim.0c01175. PubMed DOI PMC

Babu M. M.; van der Lee R.; de Groot N. S.; Gsponer J. Intrinsically disordered proteins: regulation and disease. Curr. Opin. Struct. Biol. 2011, 21, 432–440. 10.1016/j.sbi.2011.03.011. PubMed DOI

van der Lee R.; Buljan M.; Lang B.; Weatheritt R. J.; Daughdrill G. W.; Dunker A. K.; Fuxreiter M.; Gough J.; Gsponer J.; Jones D. T.; Kim P. M.; Kriwacki R. W.; Oldfield C. J.; Pappu R. V.; et al. Classification of intrinsically disordered regions and proteins. Chem. Rev. 2014, 114, 6589–6631. 10.1021/cr400525m. PubMed DOI PMC

Janin J.; Sternberg M. J. Protein flexibility, not disorder, is intrinsic to molecular recognition. F1000Prime Rep. 2013, 5, 2.10.3410/b5-2. PubMed DOI PMC

Marsh J. A.; Teichmann S. A.; Forman-Kay J. D. Probing the diverse landscape of protein flexibility and binding. Curr. Opin. Struct. Biol. 2012, 22, 643–650. 10.1016/j.sbi.2012.08.008. PubMed DOI

Williams R.; Obradovic Z.; Mathura V.; Braun W.; Garner E.; Young J.; Takayama S.; Brown C. J.; Dunker A. K.. Biocomputing 2001; World Scientific, 2000, pp 89–100. PubMed

Kim D.-H.; Han K.-H. Transient secondary structures as general target-binding motifs in intrinsically disordered proteins. Int. J. Mol. Sci. 2018, 19, 3614.10.3390/ijms19113614. PubMed DOI PMC

Mandal R.; Kohoutova K.; Petrvalska O.; Horvath M.; Srb P.; Veverka V.; Obsilova V.; Obsil T. FOXO4 interacts with p53 TAD and CRD and inhibits its binding to DNA. Protein Sci. 2022, 31, e428710.1002/pro.4287. PubMed DOI PMC

Abraham M. J.; Murtola T.; Schulz R.; Páll S.; Smith J. C.; Hess B.; Lindahl E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1–2, 19–25. 10.1016/j.softx.2015.06.001. DOI

Pronk S.; Páll S.; Schulz R.; Larsson P.; Bjelkmar P.; Apostolov R.; Shirts M. R.; Smith J. C.; Kasson P. M.; Van Der Spoel D.; Hess B.; Lindahl E. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 2013, 29, 845–854. 10.1093/bioinformatics/btt055. PubMed DOI PMC

Hess B.; Kutzner C.; Van Der Spoel D.; Lindahl E. GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 2008, 4, 435–447. 10.1021/ct700301q. PubMed DOI

Berendsen H. J.; van der Spoel D.; van Drunen R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 1995, 91, 43–56. 10.1016/0010-4655(95)00042-e. DOI

Lindorff-Larsen K.; Piana S.; Palmo K.; Maragakis P.; Klepeis J. L.; Dror R. O.; Shaw D. E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins: Struct., Funct., Bioinf. 2010, 78, 1950–1958. 10.1002/prot.22711. PubMed DOI PMC

Yang S.; Liu H.; Zhang Y.; Lu H.; Chen H. Residue-specific force field improving the sample of intrinsically disordered proteins and folded proteins. J. Chem. Inf. Model. 2019, 59, 4793–4805. 10.1021/acs.jcim.9b00647. PubMed DOI

Henriques J.; Cragnell C.; Skepo M. Molecular dynamics simulations of intrinsically disordered proteins: force field evaluation and comparison with experiment. J. Chem. Theory Comput. 2015, 11, 3420–3431. 10.1021/ct501178z. PubMed DOI

Henriques J.; Skepö M. Molecular dynamics simulations of intrinsically disordered proteins: on the accuracy of the TIP4P-D water model and the representativeness of protein disorder models. J. Chem. Theory Comput. 2016, 12, 3407–3415. 10.1021/acs.jctc.6b00429. PubMed DOI

Rieloff E.; Skepö M. Molecular dynamics simulations of phosphorylated intrinsically disordered proteins: A force field comparison. Int. J. Mol. Sci. 2021, 22, 10174.10.3390/ijms221810174. PubMed DOI PMC

Chen W.; Shi C.; MacKerell A. D.; Shen J. Conformational dynamics of two natively unfolded fragment peptides: comparison of the AMBER and CHARMM force fields. J. Phys. Chem. B 2015, 119, 7902–7910. 10.1021/acs.jpcb.5b02290. PubMed DOI PMC

Zapletal V.; Mládek A.; Melková K.; Louša P.; Nomilner E.; Jaseňáková Z.; Kubáň V.; Makovická M.; Laníková A.; Žídek L.; Hritz J. Choice of force field for proteins containing structured and intrinsically disordered regions. Biophys. J. 2020, 118, 1621–1633. 10.1016/j.bpj.2020.02.019. PubMed DOI PMC

Hanwell M. D.; Curtis D. E.; Lonie D. C.; Vandermeersch T.; Zurek E.; Hutchison G. R. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J. Cheminf. 2012, 4, 17.10.1186/1758-2946-4-17. PubMed DOI PMC

Ozenne V.; Bauer F.; Salmon L.; Huang J.-r.; Jensen M. R.; Segard S.; Bernadó P.; Charavay C.; Blackledge M. Flexible-Meccano: a tool for the generation of explicit ensemble descriptions of intrinsically disordered proteins and their associated experimental observables. Bioinformatics 2012, 28, 1463–1470. 10.1093/bioinformatics/bts172. PubMed DOI

McGibbon R. T.; Beauchamp K. A.; Harrigan M. P.; Klein C.; Swails J. M.; Hernández C. X.; Schwantes C. R.; Wang L.-P.; Lane T. J.; Pande V. S. MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys. J. 2015, 109, 1528–1532. 10.1016/j.bpj.2015.08.015. PubMed DOI PMC

Svergun D.; Barberato C.; Koch M. H. CRYSOL–a program to evaluate X-ray solution scattering of biological macromolecules from atomic coordinates. J. Appl. Crystallogr. 1995, 28, 768–773. 10.1107/S0021889895007047. DOI

Chebrek R.; Leonard S.; de Brevern A. G.; Gelly J.-C. PolyprOnline: polyproline helix II and secondary structure assignment database. Database 2014, 2014, bau102.10.1093/database/bau102. PubMed DOI PMC

Shen Y.; Bax A. SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network. J. Biomol. NMR 2010, 48, 13–22. 10.1007/s10858-010-9433-9. PubMed DOI PMC

Scherer M. K.; Trendelkamp-Schroer B.; Paul F.; Pérez-Hernández G.; Hoffmann M.; Plattner N.; Wehmeyer C.; Prinz J.-H.; Noé F. PyEMMA 2: A software package for estimation, validation, and analysis of Markov models. J. Chem. Theory Comput. 2015, 11, 5525–5542. 10.1021/acs.jctc.5b00743. PubMed DOI

Campos S. R.; Baptista A. M. Conformational analysis in a multidimensional energy landscape: study of an arginylglutamate repeat. J. Phys. Chem. B 2009, 113, 15989–16001. 10.1021/jp902991u. PubMed DOI

Kramer O.Scikit-learn. In Machine learning for evolution strategies; Springer; 2016; Vol. 20; pp 45–53.10.1007/978-3-319-33383-0_5. DOI

Konarev P. V.; Volkov V. V.; Sokolova A. V.; Koch M. H.; Svergun D. I. PRIMUS: a Windows PC-based system for small-angle scattering data analysis. J. Appl. Crystallogr. 2003, 36, 1277–1282. 10.1107/S0021889803012779. DOI

DeLano W. L. Pymol: An open-source molecular graphics tool. CCP4 Newsl. Protein Crystallogr. 2002, 40, 82–92.

Pettersen E. F.; Goddard T. D.; Huang C. C.; Couch G. S.; Greenblatt D. M.; Meng E. C.; Ferrin T. E. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 2004, 25, 1605–1612. 10.1002/jcc.20084. PubMed DOI

Koder Hamid M.; Månsson L. K.; Meklesh V.; Persson P.; Skepö M. Molecular dynamics simulations of the adsorption of an intrinsically disordered protein: Force field and water model evaluation in comparison with experiments. Front. Mol. Biosci. 2022, 9, 958175.10.3389/fmolb.2022.958175. PubMed DOI PMC

Jephthah S.; Pesce F.; Lindorff-Larsen K.; Skepo M. Force field effects in simulations of flexible peptides with varying polyproline II propensity. J. Chem. Theory Comput. 2021, 17, 6634–6646. 10.1021/acs.jctc.1c00408. PubMed DOI PMC

Melero R.; Rajagopalan S.; Lázaro M.; Joerger A. C.; Brandt T.; Veprintsev D. B.; Lasso G.; Gil D.; Scheres S. H.; Carazo J. M.; Fersht A. R.; Valle M. Electron microscopy studies on the quaternary structure of p53 reveal different binding modes for p53 tetramers in complex with DNA. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 557–562. 10.1073/pnas.1015520107. PubMed DOI PMC

Okorokov A. L.; Orlova E. V. Structural biology of the p53 tumor suppressor. Curr. Opin. Struct. Biol. 2009, 19, 197–202. 10.1016/j.sbi.2009.02.003. PubMed DOI

Skolnick J.; Gao M.; Zhou H.; Singh S. AlphaFold 2: why it works and its implications for understanding the relationships of protein sequence, structure, and function. J. Chem. Inf. Model. 2021, 61, 4827–4831. 10.1021/acs.jcim.1c01114. PubMed DOI PMC

Streamlining NMR Chemical Shift Predictions for Intrinsically Disordered Proteins: Design of Ensembles with Dimensionality Reduction and Clustering

Deeper Insight of the Conformational Ensemble of Intrinsically Disordered Proteins

Exploring the Functional Landscape of the p53 Regulatory Domain: The Stabilizing Role of Post-Translational Modifications