A Statistical Model for Predicting Protein Folding Rates from Amino Acid Sequence with Structural Class Information

Prediction of protein folding rates from amino acid sequences is one of the most important challenges in
molecular biology. In this work, I have related the protein folding rates with physical-chemical, energetic
and conformational properties of amino acid residues. I found that the classification of proteins into different
structural classes shows an excellent correlation between amino acid properties and folding rates of two-
and three-state proteins, indicating the importance of native state topology in determining the protein folding
rates. I have formulated a simple linear regression model for predicting the protein folding rates from amino
acid sequences along with structural class information and obtained an excellent agreement between predicted
and experimentally observed folding rates of proteins; the correlation coefficients are 0.99, 0.96 and 0.95,
respectively, for all-α, all-β and mixed class proteins. This is the first available method, which is capable
of predicting the protein folding rates just from the amino acid sequence with the aid of generic amino acid
properties and structural class information.