A Statistical Method for Predicting Protein Unfolding Rates from Amino Acid Sequence

The prediction of protein unfolding rates from amino acid sequences is one of the most important challenges
in computational biology and chemistry. The analysis on the relationship between protein unfolding rates
and physical−chemical, energetic, and conformational properties of amino acid residues provides valuable
information to understand and predict the unfolding rates of two- and three-state proteins. We found that
the classification of proteins into different structural classes shows an excellent correlation between amino
acid properties and unfolding rates of two- and three-state proteins, indicating the importance of native-state topology in determining the protein unfolding rates. We have formulated three independent linear
regression equations to different structural classes of proteins for predicting their unfolding rates from amino
acid sequences and obtained an excellent agreement between predicted and experimentally observed unfolding
rates of proteins; the correlation coefficients are 0.999, 0.990, and 0.992, respectively, for all-α, all-β, and
mixed-class proteins. Further, we have derived a general equation applicable to all structural classes of
proteins, which can be used for predicting the unfolding rates for proteins of an unknown structural class.
We observed a correlation of 0.987 and 0.930, respectively, for back-check and jack-knife tests. These
accuracy levels are better than those of other methods in the literature.