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Predicting changes in protein stability caused by mutation using sequence-and structure-based methods in a CAGI5

Alexey Strokach1, Carles Corbi-Verge2, Philip M Kim1,2,3

  • 1Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.

Human Mutation
|June 28, 2019
PubMed
Summary
This summary is machine-generated.

Predicting protein mutation effects is crucial. ELASPIC, FoldX, and Provean show varied accuracy, with ELASPIC being most accurate overall for predicting Gibbs free energy changes.

Keywords:
CAGIFXN genebenchmarkbioinformaticsmissense mutationmutationprotein designprotein stabilitystructural biologyvariant effect predictionΔΔG prediction

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Area of Science:

  • Computational biology
  • Protein structure and function

Background:

  • Accurate prediction of mutation impacts on protein stability is vital for understanding disease mechanisms.
  • The CAGI5 (Critical Assessment of Genome Interpretation) frataxin challenge provided a benchmark dataset for evaluating prediction tools.

Purpose of the Study:

  • To assess the accuracy of computational tools (Provean, FoldX, ELASPIC) in predicting changes in protein Gibbs free energy due to mutations.
  • To compare these methods against other established protocols like Rosetta and thermodynamic integration.

Main Methods:

  • Evaluation of Provean, FoldX, and ELASPIC using a small dataset of eight mutations from the CAGI5 frataxin challenge.
  • Comparison with predictions from Rosetta's ddg_monomer and cartesian_ddg protocols, and Amber's thermodynamic integration.

Main Results:

  • ELASPIC demonstrated the highest accuracy in predicting Gibbs free energy changes and offers a user-friendly web interface.
  • FoldX showed slightly lower accuracy but is easier for local execution.
  • Provean provided reasonable predictions with lower computational cost and no structure requirement.
  • Rosetta's cartesian_ddg protocol excelled at capturing the overall trend in the data.

Conclusions:

  • No single method is universally superior; each has distinct strengths and limitations.
  • ELASPIC is recommended for its high accuracy and accessibility.
  • FoldX and Provean offer valuable alternatives depending on specific computational and data availability constraints.