πDMD Simulation as a Strategy for Refinement of AlphaFold2 Modeled Fuzzy Protein Complexes Structures

Lotthammer J.M., Ginell G.M., Griffith D., Emenecker R.J., Holehouse A.S. 2024. Direct prediction of intrinsically disordered protein conformational properties from sequences. Nat. Methods. 21 (3), 465‒476. https://doi.org/10.1038/s41592-023-02159-5

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shrestha U.R., Smith J.C., Petridis L. 2021. Full structural ensembles of intrinsically disordered proteins from unbiased molecular dynamics simulations. Commun. Biol. 4 (1), 243.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gong X., Zhang Y., Chen J. 2021. Advanced sampling methods for multiscale simulation of disordered proteins and dynamic interactions. Biomolecules. 11, 1416.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hartman, A.M., Elgaher W.A.M., Hertrich N., Andrei S.A., Ottmann C., Hirsch A.K.H. 2020. Discovery of small-molecule stabilizers of 14-3-3γ protein–protein interactions via dynamic combinatorial chemistry. ACS Med. Chem. Lett. 11, 1041–1046.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Somsen B.A., Cossar P.J., Arkin M.R., Brunsveld L., Ottmann C. 2024. 14-3-3 protein–protein interactions: from mechanistic understanding to their small-molecule stabilization. Chembiochem. 25 (14), e202400214.

Article  CAS  PubMed  Google Scholar 

Liu J., Cao S., Ding G., Wang B., Li Y., Zhao Y., Shao Q., Feng J., Liu S., Qin L., Xiao Y. 2021. The role of 14-3-3 proteins in cell signalling pathways and virus infection. J. Cell Mol. Med. 25, 4173–4182.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yang X., Lee W.H., Sobott F., Papagrigoriou E., Robinson C.V., Grossmann J.G., Sundström M., Doyle D.A., Elkins J.M. 2006. Structural basis for protein–protein interactions in the 14-3-3γ protein family. Proc. Natl. Acad. Sci. U. S. A. 103, 17237–17242.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pitasse-Santos P., Hewitt-Richards I., Abeywickrama Wijewardana Sooriyaarachchi M.D., Doveston R.G. 2024. Harnessing the 14-3-3γ protein–protein interaction network. Curr. Opin. Struct. Biol. 86, 102822.

Article  CAS  PubMed  Google Scholar 

Falcicchio M., Ward J.A., Macip S., Doveston R.G. 2020. Regulation of p53 by the 14-3-3γ protein interaction network: New opportunities for drug discovery in cancer. Cell Death Discovery. 6 (1), 126.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Muradyan N., Arakelov V., Sargsyan A., Paronyan A., Arakelov G., Nazaryan K. 2024. Impact of mutations on the stability of SARS-CoV-2 nucleocapsid protein structure. Sci. Rep. 14 (1), 5870.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cubuk J., Alston J.J., Incicco J.J., Singh S., Stuchell-Brereton M.D., Ward M.D., Zimmerman M.I., Vithani N., Griffith D., Wagoner J.A., Bowman G.R., Hall K.B., Soranno A., Holehouse A.S. 2021. The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA. Nat. Commun. 12 (1), 1936.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ni X., Han Y., Zhou R., Zhou Y., Lei J. 2023. Structural insights into ribonucleoprotein dissociation by nucleocapsid protein interacting with non-structural protein 3 in SARS-CoV-2. Commun. Biol. 6 (1), 193.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Tugaeva K.V., Hawkins D.E.D.P., Smith J.L.R., Bayfield O.W., Ker D.S., Sysoev A.A., Klychnikov O.I., Antson A.A., Sluchanko N.N. 2021. The mechanism of SARS-CoV-2 nucleocapsid protein recognition by the human 14-3-3γ proteins. J. Mol. Biol. 433, 166875.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Joerger A.C., Fersht A.R. 2008. Structural biology of the tumor suppressor p53. Annu. Rev. Biochem. 77, 557–582.

Article  CAS  PubMed  Google Scholar 

Rajagopalan S., Sade R.S., Townsley F.M., Fersht A.R. 2009. Mechanistic differences in the transcriptional activation of p53 by 14-3-3γ isoforms. Nucleic Acids Res. 38, 893–906.

Article  PubMed  PubMed Central  Google Scholar 

Jumper J., Evans R., Pritzel A., Green T., Figurnov M., Ronneberger O., Tunyasuvunakool K., Bates R., Žídek A., Potapenko A., Bridgland A., Meyer C., Kohl S.A.A., Ballard A.J., Cowie A., Romera-Paredes B., Nikolov S., Jain R., Adler J., Back T., Petersen S., Reiman D., Clancy E., Zielinski M., Steinegger M., Pacholska M., Berghammer T., Bodenstein S., Silver D., Vinyals O., Senior A.W., Kavukcuoglu K., Kohli P., Hassabis D. 2021. Highly accurate protein structure prediction with AlphaFold. Nature. 596, 583–589.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Evans R., O’Neill M., Pritzel A., Antropova N., Senior A., Green T., Žídek A., Bates R., Blackwell S., Yim J., Ronneberger O., Bodenstein S., Zielinski M., Bridgland A., Potapenko A., Cowie A., Tunyasuvunakool K., Jain R., Clancy E., Kohli P., Jumper J., Hassabis D. 2021. Protein complex prediction with AlphaFold-Multimer. bioRxiv. https://doi.org/10.1101/2021.10.04.463034

Dokholyan N.V., Buldyrev S.V., Stanley H.E., Shakhnovich E.I. 1998. Discrete molecular dynamics studies of the folding of a protein-like model. Fold. Des. 3, 577–587.

Article  CAS  PubMed  Google Scholar 

Proctor E.A., Ding F., Dokholyan N.V. 2011. Discrete molecular dynamics. Wiley Interdiscip. Rev. Comput. Mol. Sci. 1, 80–92.

Article  CAS  Google Scholar 

Tubiana T., Carvaillo J.-C., Boulard Y., Bressanelli S. 2018. TTClust: a versatile molecular simulation trajectory clustering program with graphical summaries. J. Chem. Inf. Model. 58, 2178–2182.

Article  CAS  PubMed  Google Scholar 

Pettersen E.F., Goddard T.D., Huang C.C., Meng E.C., Couch G.S., Croll T.I., Morris J.H., Ferrin T.E. 2020. UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82.

Article  PubMed  PubMed Central  Google Scholar 

Proctor E.A., Dokholyan N.V. 2016. Applications of discrete molecular dynamics in biology and medicine. Curr. Opin. Struct. Biol. 37, 9–13.

Article  CAS  PubMed  Google Scholar 

Szöllősi D., Horváth T., Han K.H., Dokholyan N.V., Tompa P., Kalmár L., Hegedűs T. 2014. Discrete molecular dynamics can predict helical prestructured motifs in disordered proteins. PLoS One. 9, e95795

Article  PubMed  PubMed Central  Google Scholar 

Zamel J., Chen J., Zaer S., Harris P.D., Drori P., Lebendiker M., Kalisman N., Dokholyan N.V., Lerner E. 2023. Structural and dynamic insights into α-synuclein dimer conformations. Structure. 31, 411‒423.e6.

Article  CAS  PubMed  Google Scholar 

Ding F., Dokholyan N.V. 2006. Emergence of protein fold families through rational design. PLoS Comput. Biol-. 2, e85.

Article  PubMed  PubMed Central  Google Scholar 

Kasahara K., Terazawa H., Takahashi T., Higo J. 2019. Studies on molecular dynamics of intrinsically disordered proteins and their fuzzy complexes: a mini-review. Comput. Struct. Biotechnol. J. 17, 712–720.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fatafta H., Samantray S., Sayyed-Ahmad A., Coskuner-Weber O., Strodel B. 2021. Molecular simulations of IDPs: from ensemble generation to IDP interactions leading to disorder-to-order transitions. Prog. Mol. Biol. Transl. Sci. 183, 135–185. https://doi.org/10.1016/bs.pmbts.2021.06.003

Article  CAS  PubMed  Google Scholar 

Tesei G., Trolle A.I., Jonsson N., Betz J., Knudsen F.E., Pesce F., Johansson K.E., Lindorff-Larsen K. 2024. Conformational ensembles of the human intrinsically disordered proteome. Nature. 626, 897–904.

Article  CAS  PubMed 

Comments (0)

No login
gif