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Predicting protein folding rate change upon point mutation using residue-level coevolutionary information.

Saurav Mallik1,2, Smita Das1, Sudip Kundu1,2

  • 1Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, Kolkata, 700009, India.

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|November 15, 2015
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Summary

Residue-level coevolutionary networks predict protein folding rate changes upon point mutation. Network parameters like relative coevolution order (rCEO) and network density (ND) show strong linear correlations with experimental folding rate changes.

Keywords:
characteristic path lengthcoevolution ordernetwork densitypoint mutationprotein folding

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Area of Science:

  • Biophysics
  • Computational Biology
  • Protein Science

Background:

  • Protein folding kinetics are critical for understanding biological processes like misfolding and aggregation.
  • Point mutations can significantly alter protein folding rates, impacting protein function and stability.

Purpose of the Study:

  • To investigate if residue-level coevolutionary information in globular proteins can predict folding rate changes caused by point mutations.
  • To explore the relationship between network properties and alterations in protein folding kinetics.

Main Methods:

  • Generation of residue-level coevolutionary networks for globular proteins.
  • Analysis of network parameters: relative coevolution order (rCEO), network density (ND), and characteristic path length (CPL).
  • Modeling point mutations as node deletions within these coevolutionary networks.

Main Results:

  • Percentage changes in rCEO, ND, and CPL were found to be linearly correlated with experimental folding rate changes (correlation coefficients of 0.84, 0.73, and -0.61, respectively).
  • The analyzed network parameters predicted folding rate changes with low standard errors (0.031 for rCEO, 0.045 for ND, and 0.059 for CPL).

Conclusions:

  • Coevolutionary network properties offer valuable insights into the impact of point mutations on protein folding rates.
  • Network-based parameters can serve as predictive tools for assessing changes in protein folding kinetics due to mutations.