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AlphaFold and the amyloid landscape.

Francisca Pinheiro1, Jaime Santos1, Salvador Ventura1

  • 1Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain; Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain.

Journal of Molecular Biology
|May 23, 2021
PubMed
Summary

AlphaFold, a protein structure predictor, shows promise for designing soluble proteins but faces challenges in predicting complex amyloid structures due to polymorphism and lack of evolutionary data. Functional amyloids may offer a path forward.

Keywords:
aggregation intermediatesfunctional amyloidspolymorphismprotein aggregationsequence covariation

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • Protein aggregation is a significant biological process with both detrimental (diseases) and beneficial (functional amyloids) roles.
  • Understanding the structure of aggregated proteins is crucial but remains challenging due to their complex nature and heterogeneity.
  • Functional amyloids, found across all life forms, perform essential physiological functions.

Purpose of the Study:

  • To explore the applicability of AlphaFold, a state-of-the-art protein structure prediction tool, to the study of protein aggregation.
  • To identify potential applications and limitations of AlphaFold in predicting structures of both soluble proteins and aggregated species.

Main Methods:

  • Utilizing AlphaFold, a deep learning-based protein structure prediction algorithm.
  • Analyzing the challenges posed by amyloid polymorphism and aggregation intermediates for sequence-based structure prediction.
  • Considering the role of evolutionary information in AlphaFold's predictive capabilities.

Main Results:

  • AlphaFold is envisioned as a valuable tool for designing globular proteins with enhanced solubility for biotechnological applications.
  • Predicting the structure of aggregated protein species, particularly amyloids, presents significant hurdles for AlphaFold.
  • The inherent heterogeneity and polymorphism of amyloids challenge the "one sequence, one structure" principle that underlies sequence-based prediction methods.

Conclusions:

  • While AlphaFold offers straightforward applications in protein design, its direct use for predicting amyloid structures is complex.
  • The lack of positive selective pressure in aberrant aggregation limits the utility of evolutionary approaches central to AlphaFold.
  • Functional amyloids, with their defined structure-activity relationships, represent a potential starting point for applying AlphaFold to the broader field of amyloid structures.