Predicting the secondary structure of proteins using machine learning algorithms.
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 alpha-helices and beta-sheets, which are intermediate levels of the local structure.
LIAAD & DEI-Faculdade de Engenharia da Universidade do Porto, Rua Dr Roberto Frias s/n, 4420-465 Porto, Portugal. rcamacho@fe.up.pt
Rui
R
Camacho
Rita
R
Ferreira
Natacha
N
Rosa
Vânia
V
Guimarães
Nuno A
NA
Fonseca
Vítor Santos
VS
Costa
Miguel
M
de Sousa
Alexandre
A
Magalhães
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D016208:Databases, Factual; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
D000596:Amino Acids; D011506:Proteins