title: MANIFOLD: protein fold recognition based on secondary structure, sequence similarity and enzyme classification.
abstract: 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.
authors: Computer Science V, University of Mannheim B6 26, D-68131 Mannheim, Germany.
authors: Eckart
authors: E
authors: Bindewald
authors: Alessandro
authors: A
authors: Cestaro
authors: Jürgen
authors: J
authors: Hesser
authors: Matthias
authors: M
authors: Heiler
authors: Silvio C E
authors: SC
authors: Tosatto
mesh_terms: D000465:Algorithms; D004798:Enzymes / Q000737:chemistry* / Q000145:classification*; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017510:Protein Folding; D017433:Protein Structure, Secondary; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D012984:Software; D040681:Structural Homology, Protein
chemical_list: D004798:Enzymes