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Out-of-Equilibrium Selection Pressure Enhances Inference from Protein Sequence Data.

Nicola Dietler1, Cyril Malbranke1, Anne-Florence Bitbol1

  • 1SIB Swiss Institute of Bioinformatics, École Polytechnique Fédérale de Lausanne (EPFL), Institute of Bioengineering, School of Life Sciences, CH-1015 Lausanne, Switzerland and , CH-1015 Lausanne, Switzerland.

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Summary
This summary is machine-generated.

Variations in natural selection, or noise, actually improve the accuracy of protein inference methods. This finding enhances our understanding of protein structure and function prediction using sequence data.

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Area of Science:

  • * Computational biology
  • * Molecular evolution
  • * Bioinformatics

Background:

  • * Homologous proteins share similar structures and functions, leading to coevolutionary correlations in their sequences.
  • * Coevolutionary methods, including Potts models and AlphaFold, leverage these correlations to predict protein structure and function.
  • * Understanding factors influencing inference accuracy is crucial for advancing protein science.

Purpose of the Study:

  • * To investigate the impact of fluctuating selection strength and new selection pressures on coevolution-based protein inference.
  • * To determine whether natural variations in selection dynamics enhance or impede the accuracy of protein sequence analysis.
  • * To extend findings to realistic datasets and the prediction of protein interaction partners.

Main Methods:

  • * Employed a minimal theoretical model to simulate protein sequence evolution under varying selection pressures.
  • * Analyzed the effect of fluctuating selection strength and the introduction of new selective constraints.
  • * Validated model predictions using realistic synthetic protein sequence data.

Main Results:

  • * Demonstrated that fluctuating selection strength significantly improves the inference of structural contacts from protein sequences.
  • * Showed that the emergence of new selection pressures also enhances coevolution-based inference accuracy.
  • * Confirmed these improvements extend to predicting protein interaction partners.

Conclusions:

  • * Out-of-equilibrium noise, driven by natural selection variations, is beneficial for coevolution-based protein inference.
  • * Contrary to expectations, dynamic selection pressures enhance, rather than hinder, the success of predicting protein structure and function.
  • * Findings provide new insights into the robustness and potential of sequence-based protein analysis methods.