Capriotti E, Casadio R
Biocomputing Group, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy.
Bioinformatics. 2007 Feb 1;23(3):385-6. doi: 10.1093/bioinformatics/btl610. Epub 2006 Nov 30.
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 SE of 1.2).
http://gpcr.biocomp.unibo.it/cgi/predictors/K-Fold/K-Fold.cgi.
http://gpcr.biocomp.unibo.it/~emidio/K-Fold/K-Fold_help.html.
K折法是一种用于自动预测蛋白质折叠动力学顺序和速率的工具。该工具基于支持向量机（SVM），它在一个包含63种蛋白质的数据集上进行训练，这些蛋白质的三维结构和折叠机制已在文献中描述的实验中得知。该方法可预测已知原子结构的蛋白质是按照两态还是多态动力学进行折叠，并且在对这63种蛋白质的训练集进行测试时，能正确分类81%的折叠机制。作为进一步的选项，它还可预测折叠速率的对数。据我们所知，该工具首次在蛋白质折叠过程中区分其是具有两态还是多态动力学特征，并同时估计该过程的恒定速率值。当用于预测折叠速率的对数时，K折法与实验数据的相关值为0.74（标准误差为1.2）。
http://gpcr.biocomp.unibo.it/cgi/predictors/K-Fold/K-Fold.cgi。
http://gpcr.biocomp.unibo.it/~emidio/K-Fold/K-Fold_help.html。
