Welcome to FOLD-RATE:
Prediction of Protein Folding Rates from Amino Acid Sequence

Protein folding rate is a measure of slow/fast folding of proteins from the unfolded state to native three-dimensional structure. Prediction of protein folding rates from amino acid sequence is a challenging problem. Several methods have been proposed for prdicting the folding rates of two- and three state proteins.

We have developed a statistical method based on multiple regression technique for predicting protein folding rates using amino acid composition and properties. Different regression equations have been set up for proteins belonging to different structural classes, all-alpha, all-beta and mixed class proteins. Further, we have derived a general equation applicable for all structural classes of proteins, which may be used for predicting the folding rates for proteins of unknown structural class.

FOLD-RATE predicts the folding rate of two and three-state proteins with/without structural class information. To predict the folding rate enter your sequence in a single letter code in the following box and select the structural class.

Structural class    all-alpha all-beta mixed Unknown

Databases/programs for Structural Class Information/Prediction
1. SCOP: Structural classification of proteins
2. CATH: Protein structure classification
3. PSA: Protein structure prediction server
4. SSCP: Secondary structural Content Prediction

Sample sequence (all-alpha):

For the list of Protein Data Bank codes along with their amino acid sequences, please click here.

M. Michael Gromiha and S. Selvaraj (2001). Comparison between Long-range Interactions and Contact Order in Determining the Folding Rate of Two-state Proteins: Application of Long Range Order to Folding Rate Prediction. J. Mol. Biol. 310, 27-32.

M. Michael Gromiha (2003). Importance of Native-state Topology for Determining the Folding Rate of Two-state Proteins. J. Chem. Inf. Comp. Sci. 43, 1481-1485.

M. Michael Gromiha (2005). A Statistical Model for Predicting Protein Folding Rates from Amino Acid Sequence with Structural Class Information. J. Chem. Inf. Model. 45, 494-501 .

Contributors for the Analysis and Prediction:
1. M. Michael Gromiha, Computational Biology Research Center, AIST, Japan.
2. S. Selvaraj, Bharathidasan University, India
3. A. Mary Thangakani, ATA, Japan
4. Makiko Suwa, Computational Biology Research Center, AIST, Japan.