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Forecasting protein evolution by integrating birth-death population models with structurally constrained substitution

David Ferreiro1,2, Luis Daniel González-Vázquez1,2, Ana Prado-Comesaña1

  • 1CINBIO, Universidade de Vigo, Vigo, Spain.

Elife
|September 24, 2025
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Summary

Forecasting protein evolution is now feasible. This new method integrates population and protein evolution models to predict future changes, showing promise for evolutionary studies.

Keywords:
birth-death processevolutionary biologyforecasting evolutionmolecular evolutionphylogeneticsprotein folding stabilitysubstitution modelviruses

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Biophysics

Background:

  • Traditional evolutionary studies focus on past events.
  • Emerging trend: predicting future evolutionary trajectories for applications.
  • Protein evolution modeling often separates molecular evolution from history.

Purpose of the Study:

  • Introduce a novel method for forecasting protein evolution.
  • Combine birth-death population models with substitution models.
  • Incorporate selection on protein folding stability.

Main Methods:

  • Integrated modeling of forward-in-time birth-death trajectories and protein evolution.
  • Utilized structurally constrained substitution models.
  • Implemented in a freely available computer framework.

Main Results:

  • Method outperformed traditional empirical substitution models.
  • Predicted protein folding stability with acceptable errors for viral proteins.
  • Sequence prediction errors were larger than stability prediction errors.

Conclusions:

  • Forecasting protein evolution is feasible under specific evolutionary scenarios.
  • Accuracy can be enhanced by improving underlying evolutionary models.
  • The developed framework offers a new tool for evolutionary predictions.