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Protein Engineering, Vol. 16, No. 2, 103-107, February 2003
© 2003 Oxford University Press

A pentapeptide-based method for protein secondary structure prediction

A. Figureau1, M.A. Soto2,3 and J. Tohá2

1 Institut de Physique Nucléaire de Lyon, Université Claude Bernard, 69622 Villeurbanne Cedex, France and 2 Departamento de Física, Fac. Ciencias Físicas y Matemáticas, Universidad de Chile, Casilla 487-3, Santiago, Chile

We present a new method for protein secondary structure prediction, based on the recognition of well-defined pentapeptides, in a large databank. Using a databank of 635 protein chains, we obtained a success rate of 68.6%. We show that progress is achieved when the databank is enlarged, when the 20 amino acids are adequately grouped in 10 sets and when more pentapeptides are attributed one of the defined conformations, {alpha}-helices or ß-strands. The analysis of the model indicates that the essential variable is the number of pentapeptides of well-defined structure in the database. Our model is simple, does not rely on arbitrary parameters and allows the analysis in detail of the results of each chosen hypothesis.


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