  Protein folding is a problem of large interest since it concerns the
mechanism by which the genetic information is translated into proteins with
well defined three-dimensional (3D) structures and functions. Recently
theoretical models have been developed to predict the protein folding rate
considering the relationships of the process with tolopological parameters
derived from the native (atomic-solved) protein structures. Previous works
classified proteins in two different groups exhibiting either a
single-exponential or a multi-exponential folding kinetics. It is well known
that these two classes of proteins are related to different protein structural
features. The increasing number of available experimental kinetic data allows
the application to the problem of a machine learning approach, in order to
predict the kinetic order of the folding process starting from the experimental
data so far collected. This information can be used to improve the prediction
of the folding rate. In this work first we describe a support vector
machine-based method (SVM-KO) to predict for a given protein the kinetic order
of the folding process. Using this method we can classify correctly 78% of the
folding mechanisms over a set of 63 experimental data. Secondly we focus on the
prediction of the logarithm of the folding rate. This value can be obtained as
a linear regression task with a SVM-based method. In this paper we show that
linear correlation of the predicted with experimental data can improve when the
regression task is computed over two different sets, instead of one, each of
them composed by the proteins with a correctly predicted two state or
multistate kinetic order.
