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Fractals and self-organized criticality in proteins

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  • Phillips, J.C.

Abstract

Self-organized criticality, a powerful concept, originated in 1987 as an extension of fractal geometries to thermodynamic systems in the vicinities of instabilities. The value of fractal methods can be greatly enhanced in realistic models that exploit accurate fractal values derived from homogeneous (possibly curated) Big Data. We illustrate this point by discussing the derivation of fractal exponents describing protein–water interactions, and their application to protein roughness, protein binding and potentially protein engineering. The examples studied are evolution of lysozyme c and acylphosphatase, and mutational effects on their aggregation.

Suggested Citation

  • Phillips, J.C., 2014. "Fractals and self-organized criticality in proteins," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 440-448.
    • Handle: RePEc:eee:phsmap:v:415:y:2014:i:c:p:440-448
    DOI: 10.1016/j.physa.2014.08.034
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    References listed on IDEAS

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    1. Laurienti, Paul J. & Joyce, Karen E. & Telesford, Qawi K. & Burdette, Jonathan H. & Hayasaka, Satoru, 2011. "Universal fractal scaling of self-organized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3608-3613.
    2. Fabrizio Chiti & Massimo Stefani & Niccolò Taddei & Giampietro Ramponi & Christopher M. Dobson, 2003. "Rationalization of the effects of mutations on peptide andprotein aggregation rates," Nature, Nature, vol. 424(6950), pages 805-808, August.
    3. Naumis, G.G. & Phillips, J.C., 2012. "Diffusion of knowledge and globalization in the web of twentieth century science," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 3995-4003.
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    Citations

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    Cited by:

    1. Sachdeva, Vedant & Phillips, James C., 2016. "Oxygen channels and fractal wave–particle duality in the evolution of myoglobin and neuroglobin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 1-11.
    2. Phillips, J.C., 2017. "Giant hub Src and Syk tyrosine kinase thermodynamic profiles recapitulate evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 330-336.
    3. Phillips, J.C., 2015. "Similarity is not enough: Tipping points of Ebola Zaire mortalities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 277-281.
    4. Xu, Xiu-Lian & Shi, Jin-Xuan & Wang, Jun & Li, Wenfei, 2021. "Long-range correlation and critical fluctuations in coevolution networks of protein sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    5. Filho, A.S. Nascimento & Araújo, M.L.V. & Miranda, J.G.V. & Murari, T.B. & Saba, H. & Moret, M.A., 2018. "Self-affinity and self-organized criticality applied to the relationship between the economic arrangements and the dengue fever spread in Bahia," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 619-628.
    6. Phillips, J.C., 2017. "Autoantibody recognition mechanisms of MUC1," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 244-249.
    7. Lahmiri, Salim, 2016. "Clustering of Casablanca stock market based on hurst exponent estimates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 310-318.
    8. Phillips, J.C., 2021. "Synchronized attachment and the Darwinian evolution of coronaviruses CoV-1 and CoV-2," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    9. Phillips, J.C., 2016. "Autoantibody recognition mechanisms of p53 epitopes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 162-170.
    10. Phillips, J.C., 2017. "Hidden thermodynamic information in protein amino acid mutation tables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 676-680.
    11. Phillips, J.C. & Moret, Marcelo A. & Zebende, Gilney F. & Chow, Carson C., 2022. "Phase transitions may explain why SARS-CoV-2 spreads so fast and why new variants are spreading faster," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    12. Voorhoeve, Niels & Allan, Douglas C. & Moret, M.A. & Zebende, G.F. & Phillips, J.C., 2018. "Why human milk is more nutritious than cow milk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 302-309.
    13. Phillips, J.C., 2016. "Bioinformatic scaling of allosteric interactions in biomedical isozymes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 289-294.

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