Background: The prediction of protein folding rates is a necessary step towards understanding
the principles of protein folding. Prediction of protein folding rates from 3D structures is more
common and more accurate, but there are a few methods to accurately predict the folding rates from
sequences. Therefore, it is important to develop an accurate method of predicting protein folding rates
from sequences of proteins with unknown structures.
Objective: We proposed a highly accurate sequence-based prediction method to predict the rate of in-water
protein folding directly from its primary structure, which does not need any information of its 3D fold.
Method: It uses ANGLOR to predict real-value of protein backbone torsion angles from amino acid
sequences, and then calculate cumulative backbone torsion angles (CBTA). Our estimate is based on
the Pearson correlation coefficient between the folding rate and the natural logarithm of predicted
Results: The method achieves 79% correlation with experiment over all 100 “two-state” and “multistate”
proteins (including two artificial peptides) studied up to now. This is better than the results of existing
sequence-based prediction methods which include the effective length of the folding chain (Leff)
and the number of predicted long-range contacts (LROpred).
Conclusion: We found a new parameter of protein folding rates, i.e., cumulative backbone torsion angles,
and gave a highly accurate sequence-based method of predicting folding rates. On the one hand
the CBTA is a coarse-grained description for distribution of protein backbone torsion angles which determines
the basic topology structure of the protein, on the other hand, the CBTA is proportional to protein
length. Therefore, a strong correlation exists between the CBTA and folding rate. This is the reason
why we can successfully predict the folding rates from the amino acid sequence-predicted backbone