Predicting the secondary structure of proteins using Machine Learning algorithms. (14th November 2012)

• Record Type: Journal Article
• Authors: Camacho, Rui; Ferreira, Rita; Rosa, Natacha; Guimarães, Vânia; Fonseca, Nuno A.; Costa, Vítor Santos; de Sousa, Miguel; Magalhães, Alexandre
• Is Part Of: International journal of data mining and bioinformatics. Volume 6:Number 6(2012)
• Page Start: 571
• Page End: 584
• Publication Date: 2012-11-14
• Subjects: data mining -- machine learning -- classification -- decision trees -- rule induction -- instance-based learning -- Bayesian algorithms -- WEKA -- bioinformatics -- protein folding -- predicting secondary structure conformations; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3Dstructure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely α-helices and β-sheets, which are intermediate levels of the local structure.