The evaluation of protein folding rate constant is improved by predicting the folding kinetic order with a SVM-based method

TL;DR

This paper introduces a support vector machine-based approach to predict protein folding kinetic order and rate, improving accuracy by classifying folding mechanisms and applying regression on specific protein groups.

Contribution

The study presents a novel SVM-based method for predicting protein folding kinetic order and rate, enhancing existing models with machine learning techniques.

Findings

Correctly classifies 78% of folding mechanisms

Improves rate prediction accuracy by using separate groups

Demonstrates the effectiveness of SVM in folding kinetics prediction

Abstract

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.