Protein Engineering vol. 16 no. 11 pp. 785-789, 2003
© 2003 Oxford University Press
MANIFOLD: protein fold recognition based on secondary structure, sequence similarity and enzyme classification
1Computer Science V, University of Mannheim, B6, 26, D-68131 Mannheim, Germany, 2Center of Excellence in Bioinformatics, State University of New York at Buffalo, 901 Washington Street, Buffalo, NY 14203-1199, USA, 3CRIBI Biotechnology Centre, University of Padua, V.le G. Colombo 3, I-35121 Padova, Italy and 4Chair for Computer Vision, Graphics and Pattern Recognition, University of Mannheim, L13, 19, D-68131 Mannheim, Germany
5 To whom correspondence should be addressed, at the address in Italy. e-mail: silvio{at}cribi.unipd.it
We present a protein fold recognition method, MANIFOLD, which uses the similarity between target and template proteins in predicted secondary structure, sequence and enzyme code to predict the fold of the target protein. We developed a non-linear ranking scheme in order to combine the scores of the three different similarity measures used. For a difficult test set of proteins with very little sequence similarity, the program predicts the fold class correctly in 34% of cases. This is an over twofold increase in accuracy compared with sequence-based methods such as PSI-BLAST or GenTHREADER, which score 13–14% correct first hits for the same test set. The functional similarity term increases the prediction accuracy by up to 3% compared with using the combination of secondary structure similarity and PSI-BLAST alone. We argue that using functional and secondary structure information can increase the fold recognition beyond sequence similarity.
Received February 19, 2003; revised August 21, 2003; accepted September 12, 2003.
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