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Rationalization of the effects of mutations on peptide andprotein aggregation rates

Author

Listed:
  • Fabrizio Chiti

    (University of Cambridge
    Università degli Studi di Firenze)

  • Massimo Stefani

    (Università degli Studi di Firenze)

  • Niccolò Taddei

    (Università degli Studi di Firenze)

  • Giampietro Ramponi

    (Università degli Studi di Firenze)

  • Christopher M. Dobson

    (University of Cambridge)

Abstract

In order for any biological system to function effectively, it is essential to avoid the inherent tendency of proteins to aggregate and form potentially harmful deposits1,2,3,4. In each of the various pathological conditions associated with protein deposition, such as Alzheimer's and Parkinson's diseases, a specific peptide or protein that is normally soluble is deposited as insoluble aggregates generally referred to as amyloid2,3. It is clear that the aggregation process is generally initiated from partially or completely unfolded forms of the peptides and proteins associated with each disease. Here we show that the intrinsic effects of specific mutations on the rates of aggregation of unfolded polypeptide chains can be correlated to a remarkable extent with changes in simple physicochemical properties such as hydrophobicity, secondary structure propensity and charge. This approach allows the pathogenic effects of mutations associated with known familial forms of protein deposition diseases to be rationalized, and more generally enables prediction of the effects of mutations on the aggregation propensity of any polypeptide chain.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:nature:v:424:y:2003:i:6950:d:10.1038_nature01891
    DOI: 10.1038/nature01891
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    Cited by:

    1. Espinoza Ortiz, J.S. & Dias, Cristiano L., 2018. "Cooperative fibril model: Native, amyloid-like fibril and unfolded states of proteins," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 154-165.
    2. Qi Wang & Joshua L Johnson & Nathalie YR Agar & Jeffrey N Agar, 2008. "Protein Aggregation and Protein Instability Govern Familial Amyotrophic Lateral Sclerosis Patient Survival," PLOS Biology, Public Library of Science, vol. 6(7), pages 1-19, July.
    3. Chyn Liaw & Chun-Wei Tung & Shinn-Ying Ho, 2013. "Prediction and Analysis of Antibody Amyloidogenesis from Sequences," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-15, January.
    4. Taniya Bhardwaj & Kundlik Gadhave & Shivani K. Kapuganti & Prateek Kumar & Zacharias Faidon Brotzakis & Kumar Udit Saumya & Namyashree Nayak & Ankur Kumar & Richa Joshi & Bodhidipra Mukherjee & Aparna, 2023. "Amyloidogenic proteins in the SARS-CoV and SARS-CoV-2 proteomes," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    5. Phillips, J.C., 2016. "Autoantibody recognition mechanisms of p53 epitopes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 162-170.
    6. Carlos Família & Sarah R Dennison & Alexandre Quintas & David A Phoenix, 2015. "Prediction of Peptide and Protein Propensity for Amyloid Formation," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-16, August.
    7. Elodie Monsellier & Matteo Ramazzotti & Niccolò Taddei & Fabrizio Chiti, 2008. "Aggregation Propensity of the Human Proteome," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-9, October.
    8. 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.
    9. Allen W Bryan Jr. & Matthew Menke & Lenore J Cowen & Susan L Lindquist & Bonnie Berger, 2009. "BETASCAN: Probable β-amyloids Identified by Pairwise Probabilistic Analysis," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-11, March.

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