Poster #RP213
Using Neural Networks MLP to predict the Secundary Structure of Proteins
Luis Paulo Scott*, Jorge Chahine**, José R. Ruggiero***, Fausto Ferreira***
*UNIFEV, Votuporanga, Brasil; **Unesp, são José do Rio Preto, Brasil; ***UNESP, São José do Rio Preto, Brasil
The term 'protein' comes from the Greek (proteios) and it means 'the first magnitude'. The proteins are complex molecules that have a specific tertiary structure. These macromolecules realize tasks like chemical reactions catalysis, transport, recognition and transmission of signs. Therefore we need to know the 3D structure of these molecules, because the prediction of secondary structure of proteins can contribute to elucidate the protein folding problem. In order to predict these structures we used methods of Artificial Neural Networks (ANN) starting form the primary sequences of amino acids. The ANNs are good tools to classify and recognize the patterns. Therefore they are good tools in the 1D prediction. Our main objective was to develop a software to 1D prediction in the Web. In this present work we used ANNs in the prediction of the secondary structures of proteins, taking as patterns the structures in helix form, beta sheet and coil. The obtained data are compared with predictors described: PSA, PSIPRED and PHD in order to have an idea of the quality of the prediction.