Predicting the secondary structure of proteins using machine learning algorithms

Authors:
Rui Camacho;Rita Ferreira;Natacha Rosa;Vânia Guimarães;Nuno A. Fonseca;Ví-tor Santos Costa;Miguel De Sousa;Alexandre Magalhães
Affiliations:
LIAAD&DEI-Faculdade de Engenharia da Universidade do Porto, Rua Dr Roberto Frias s/n, 4420-465 Porto, Portugal;Faculdade de Engenharia da Universidade do Porto, Rua Dr Roberto Frias s/n, 4420-465 Porto, Portugal;Faculdade de Engenharia da Universidade do Porto, Rua Dr Roberto Frias s/n, 4420-465 Porto, Portugal;Faculdade de Engenharia da Universidade do Porto, Rua Dr Roberto Frias s/n, 4420-465 Porto, Portugal;CRACS-INESC Porto L.A./FCUP, R. Campo Alegre 1021/1055, 4169-007 Porto, Portugal;CRACS-INESC Porto L.A./FCUP, R. Campo Alegre 1021/1055, 4169-007 Porto, Portugal;REQUIMTE/Universidade do Porto, R. Campo Alegre 687, 4169-007 Porto, Portugal;REQUIMTE/Universidade do Porto, R. Campo Alegre 687, 4169-007 Porto, Portugal
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2012

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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 3D- structure 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 ß-sheets, which are intermediate levels of the local structure.