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Bioinformatics Advance Access published online on November 30, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl610
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received September 22, 2006
Revised November 15, 2006
Accepted November 24, 2006

Applications note

K-Fold: a tool for the prediction of the protein folding kinetic order and rate

E. Capriotti 1 and R. Casadio 2 *

1 Biocomputing Group, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy; present address: Structural Genomics Unit, Department of Bioinformatic (CIPF), Autopista del Saler 16, 46013 Valencia, Spain
2 Biocomputing Group, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy

* To whom correspondence should be addressed.
R. Casadio, E-mail: emidio{at}biocomp.unibo.it


  Abstract

Summary: K-Fold is a tool for the automatic prediction of the protein folding kinetic order and rate. The tool is based on a support vector machine (SVM) that was trained on a data set of 63 proteins, whose 3D structure and folding mechanism are known from experiments already described in the literature. The method predicts whether a protein of known atomic structure folds according to a two-state or a multi-state kinetics and correctly classifies 81% of the folding mechanisms when tested over the training set of the 63 proteins. It also predicts as a further option the logarithm of the folding rate. To the best of our knowledge, the tool discriminates for the first time whether a protein is characterized by a two state or a multiple state kinetics, during the folding process, and concomitantly estimates also the value of the constant rate of the process. When used to predict the logarithm of the folding rate, K-Fold scores with a correlation value to the experimental data of 0.74 (with a standard error of 1.2).

Availability: http://gpcr.biocomp.unibo.it/cgi/predictors/K-Fold/K-Fold.cgi.

Supplementary information: http://gpcr.biocomp.unibo.it/~emidio/K-Fold/K-Fold_help.html.

Associate Editor: Anna Tramontano
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