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Using AlphaFold to predict the impact of single mutations on protein stability and function.

Marina A Pak1, Karina A Markhieva2, Mariia S Novikova3

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AlphaFold excels at protein structure prediction but struggles to predict mutation effects on stability and function. Current metrics show weak correlation, limiting its immediate application beyond initial structure determination.

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Area of Science:

  • Structural biology
  • Computational biology
  • Protein science

Background:

  • Deep learning models like AlphaFold have revolutionized protein structure prediction.
  • The protein folding problem encompasses more than just predicting static structures.
  • The applicability of AlphaFold to other protein folding-related challenges remains unexplored.

Purpose of the Study:

  • To evaluate AlphaFold's capability in predicting the impact of single amino acid mutations on protein stability and function.
  • To assess the correlation between AlphaFold's predicted metrics and experimentally determined changes in protein stability (ΔΔG) and function (fluorescence).

Main Methods:

  • Extracted pLDDT and metrics from AlphaFold predictions for proteins with and without single mutations.
  • Correlated predicted metric changes with experimentally measured ΔΔG values for protein stability.
  • Correlated predicted metric changes with experimentally measured fluorescence levels for protein function using a large dataset of Green Fluorescent Protein (GFP) mutations.

Main Results:

  • A very weak or no correlation was observed between AlphaFold output metrics and changes in protein stability.
  • Similarly, a weak or no correlation was found between AlphaFold metrics and changes in protein function (fluorescence).

Conclusions:

  • AlphaFold's current output metrics are not sufficient for accurately predicting the impact of single mutations on protein stability or function.
  • The AlphaFold revolution may not be immediately applicable to solving broader protein folding problems beyond initial structure prediction.