Exploring alternative knowledge representations for protein secondary-structure prediction.
Methods for 3-class secondary-structure prediction are thought to be reaching the highest achievable accuracy. Their accuracy on beta-sheet residue class is considerably lower than for the other two classes. We analysed the relevance of 315 individual input attributes for a predictor with the usual framework of using sequence-profile based data with an input window of fixed size. We propose two alternative knowledge representations with significantly smaller sets of input attributes. We also investigated the possibility of exploiting the prediction of connected pairs of beta-sheet residues and the prediction of residue contact maps for the improvement of accuracy of secondary-structure prediction.
Midic
Uros
U
Center for Information Science and Technology, Temple University, 1805 N. Broad St., 303 Wachman Hall, Philadelphia, PA 19129, USA. uros@ist.temple.edu
Dunker
A Keith
AK
Obradovic
Zoran
Z
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000032:analysis / Q000737:chemistry / Q000648:ultrastructure; D040901:Proteomics; D012680:Sensitivity and Specificity
D011506:Proteins