Predicting the secondary structure of proteins using Machine Learning algorithms

• Author(s): Rui Camacho ; Rita Ferreira ; Natacha Rosa ; Vânia Guimarães ; Nuno A. Fonseca ; Vítor Santos Costa ; Miguel de Sousa ; Alexandre Magalhães
• Date: 2012
• Persistent ID: http://hdl.handle.net/10216/67119
• Origin: Repositório Aberto da Universidade do Porto
• Subject(s): Ciências Naturais; Ciências biológicas
• Document Type: Article
• Language: English

Description

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.