title: A statistical model for predicting protein folding rates from amino acid sequence with structural class information.
abstract: 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-alpha, all-beta 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.
authors.affiliation: Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Aomi Frontier Building 17F, 2-43 Aomi, Koto-ku, Tokyo 135-0064, Japan. michael-gromiha@aist.go.jp
authors.forename: M Michael
authors.initials: MM
authors.lastname: Gromiha
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000032:analysis* / Q000737:chemistry*; D015233:Models, Statistical; D009154:Mutation / Q000235:genetics; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000235:genetics / Q000378:metabolism*; D040681:Structural Homology, Protein
chemical_list: D000596:Amino Acids; D011506:Proteins