Nucleation-based prediction of the protein folding rate and its correlation with the folding nucleus size

Authors

  • Oxana V. Galzitskaya,

    Corresponding author
    1. Institute of Protein Research, Russian Academy of Sciences, Group of Bioinformatics, 4 Institutskaya Str., Pushchino, Moscow Region 142290, Russia
    • Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia
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  • Anna V. Glyakina

    1. Institute of Protein Research, Russian Academy of Sciences, Group of Bioinformatics, 4 Institutskaya Str., Pushchino, Moscow Region 142290, Russia
    2. Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Laboratory of Molecular Dynamics, 4 Institutskaya Str., Pushchino, Moscow Region 142290, Russia
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Abstract

The problem of protein self-organization is in the focus of current molecular biology studies. Although the general principles are understood, many details remain unclear. Specifically, protein folding rates are of interest because they dictate the rate of protein aggregation which underlies many human diseases. Here we offer predictions of protein folding rates and their correlation with folding nucleus sizes. We calculated free energies of the transition state and sizes of folding nuclei for 84 proteins and peptides whose other parameters were measured at the point of thermodynamic equilibrium between their unfolded and native states. We used the dynamic programming method where each residue was considered to be either as folded as in its native state or completely disordered. The calculated and measured folding rates showed a good correlation at the temperature mid-transition point (the correlation coefficient was 0.75). Also, we pioneered in demonstrating a moderate (-0.57) correlation coefficient between the calculated sizes of folding nuclei and the folding rates. Predictions made by different methods were compared. The established good correlation between the estimated free energy barrier and the experimentally found folding rate of each studied protein/peptide indicates that our model gives reliable results for the considered data set. Proteins 2012; © 2012 Wiley Periodicals, Inc.

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