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Assessing the relation between protein phosphorylation, AlphaFold3 models, and conformational variability.

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Protein Science : a Publication of the Protein Society
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Summary
This summary is machine-generated.

Deep learning models like AlphaFold struggle to predict how phosphorylation, a key protein modification, changes protein structures. Current models capture dominant states but miss crucial phosphorylation-induced variations, limiting our understanding of protein function and disease.

Keywords:
AlphaFold3conformational diversityphosphorylationpost‐translational modifications (PTMs)protein structures

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Area of Science:

  • * Molecular Biology
  • * Structural Biology
  • * Computational Biology

Background:

  • * Post-translational modifications (PTMs) like phosphorylation regulate protein function and are implicated in diseases.
  • * Understanding PTM-driven structural changes is vital but limited by experimental data scarcity and protein dynamics.
  • * Deep learning, exemplified by AlphaFold, has advanced protein structure prediction.

Purpose of the Study:

  • * To evaluate AlphaFold models' ability to predict phosphorylation-induced protein structural diversity.
  • * To assess if phosphorylation-aware models capture PTM-specific conformational changes.
  • * To identify challenges in modeling PTM-driven protein structural landscapes.

Main Methods:

  • * Systematic evaluation of AlphaFold 2 (AF2), AlphaFold 3 non-phospho (AF3-non phospho), and AlphaFold 3 phospho (AF3-phospho) models.
  • * Analysis of experimentally derived protein conformational ensembles.
  • * Comparison of model predictions against dominant and PTM-specific structural states.

Main Results:

  • * All evaluated AlphaFold models predominantly predicted dominant protein structural states.
  • * Models failed to capture significant phosphorylation-induced conformational variations.
  • * AF3-phospho showed only marginal improvement over AF2 and AF3-non phospho in predicting these changes.

Conclusions:

  • * Current AlphaFold models have limitations in predicting PTM-driven structural dynamics, particularly phosphorylation.
  • * Capturing modification-induced conformational variability remains a significant challenge.
  • * Development of more adaptable protein structure prediction frameworks is needed.