title: BETTY: prediction of beta-strand type from sequence.
abstract: Most secondary structure prediction programs do not distinguish between parallel and antiparallel beta-sheets. However, such knowledge would constrain the available topologies of a protein significantly, and therefore aid existing fold recognition algorithms. For this reason, we propose a technique which, in combination with existing secondary structure programs such as PSIPRED, allows one to distinguish between parallel and antiparallel beta-sheets. We propose the use of a support vector machine (SVM) procedure, BETTY, to predict parallel and antiparallel sheets from sequence. We found that there is a strong signal difference in the sequence profiles which SVMs can efficiently extract. With strand type assignment accuracies of 90.7% and 83.3% for antiparallel and parallel strands, respectively, our method adds considerably to existing information on current 3-class secondary structure predictions. BETTY has been implemented as an online service which academic researchers can access from our website http://www.fz-juelich.de/nic/cbb/service/service.php.
authors.affiliation: John v. Neumann Institute for Computing, FZ Jülich, 52425 Jülich, Germany. olav.zimmermann@fz-juelich.de
authors.forename: Olav
authors.initials: O
authors.lastname: Zimmermann
authors.forename: Longhui
authors.initials: L
authors.lastname: Wang
authors.forename: Ulrich H E
authors.initials: UH
authors.lastname: Hansmann
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D008958:Models, Molecular; D017510:Protein Folding; D017433:Protein Structure, Secondary