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Inferring repeat-protein energetics from evolutionary information.

Rocío Espada1, R Gonzalo Parra2, Thierry Mora3

  • 1Protein Physiology Lab, Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica. Buenos Aires, Argentina. / CONICET - Universidad de Buenos Aires. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN). Buenos Aires, Argentina.

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Summary
This summary is machine-generated.

Analyzing protein sequence variations reveals evolutionary history and energetic landscapes. This method predicts folding free energy changes and generates novel, natural-like protein sequences.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Protein Science

Background:

  • Natural protein sequences encode evolutionary history and structural constraints.
  • Protein families with similar structures can exhibit varying stabilization energies linked to physiological function.
  • Repeat proteins offer a simplified model for studying energetic variations due to inherent symmetry.

Purpose of the Study:

  • To develop a model describing energetic variations in repeat proteins based on sequence modifications.
  • To interpret the evolutionary field derived from sequence data with structural detail.
  • To predict folding free energy changes and generate novel protein sequences.

Main Methods:

  • Analyzing sequence covariations to infer native protein ensembles.
  • Developing an evolutionary field model accounting for single amino acid and pair-wise interactions.
  • Incorporating higher-order correlations into a single term.
  • Relating energetic variations to experimental characterization of natural proteins.

Main Results:

  • The proposed model provides an interpretable evolutionary field with structural detail.
  • Energetic variations in natural proteins were traced and linked to experimental data.
  • The model accurately predicts folding free energy changes for protein mutants.
  • Generated synthetic sequences are statistically indistinguishable from natural counterparts.

Conclusions:

  • The evolutionary field approach offers a powerful tool for understanding protein energy landscapes.
  • This method facilitates the prediction of mutation effects on protein stability.
  • The approach enables the design of novel protein sequences with natural characteristics.