title: Motif-based fold assignment.
abstract: Conventional fold recognition techniques rely mainly on the analysis of the entire sequence of a protein. We present an MBA method to improve performance of any conventional sequence-based fold assignment. The method uses sequence motifs, such as those defined in the Prosite database, and the SwissProt annotation of the fold library. When combined with a simple SDP method, the coverage of MBA is comparable to the results obtained with PSI-BLAST. However, the set of the MBA predictions is significantly different from that of PSI-BLAST, leading to a 40% increase of the coverage for the combined MBA/PSI-BLAST method. The MBA approach can be easily adopted to include the results of sequence-independent function prediction methods and alternative motif and annotation databases. The method is available through the web server localized at http://www.doe-mbi.ucla.edu/mba.
authors.affiliation: Department of Chemistry, UCLA-DOE Laboratory of Structural Biology and Molecular Medicine, UCLA, Los Angeles, California 90095-1570, USA.
authors.forename: L
authors.initials: L
authors.lastname: Salwinski
authors.forename: D
authors.initials: D
authors.lastname: Eisenberg
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000818:Animals; D019992:Databases as Topic; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D012984:Software