title: FOLD-RATE: prediction of protein folding rates from amino acid sequence.
abstract: We have developed a web server, FOLD-RATE, for predicting the folding rates of proteins from their amino acid sequences. The relationship between amino acid properties and protein folding rates has been systematically analyzed and a statistical method based on linear regression technique has been proposed for predicting the folding rate of proteins. We 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. Consequently, different regression equations have been developed for proteins belonging to all-alpha, all-beta and mixed class. We observed an excellent agreement between predicted and experimentally observed folding rates of proteins; the correlation coefficients are, 0.99, 0.97 and 0.90, respectively, for all-alpha, all-beta and mixed class proteins. The prediction server is freely available at http://psfs.cbrc.jp/fold-rate/.
authors.affiliation: Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. michael-gromiha@aist.go.jp
authors.forename: M Michael
authors.initials: MM
authors.lastname: Gromiha
authors.forename: A Mary
authors.initials: AM
authors.lastname: Thangakani
authors.forename: S
authors.initials: S
authors.lastname: Selvaraj
mesh_terms: D020407:Internet; D007700:Kinetics; D016014:Linear Models; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry / Q000145:classification; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins