title: MUPRED: a tool for bridging the gap between template based methods and sequence profile based methods for protein secondary structure prediction.
abstract: Predicting secondary structures from a protein sequence is an important step for characterizing the structural properties of a protein. Existing methods for protein secondary structure prediction can be broadly classified into template based or sequence profile based methods. We propose a novel framework that bridges the gap between the two fundamentally different approaches. Our framework integrates the information from the fuzzy k-nearest neighbor algorithm and position-specific scoring matrices using a neural network. It combines the strengths of the two methods and has a better potential to use the information in both the sequence and structure databases than existing methods. We implemented the framework into a software system MUPRED. MUPRED has achieved three-state prediction accuracy (Q3) ranging from 79.2 to 80.14%, depending on which benchmark dataset is used. A higher Q3 can be achieved if a query protein has a significant sequence identity (>25%) to a template in PDB. MUPRED also estimates the prediction accuracy at the individual residue level more quantitatively than existing methods. The MUPRED web server and executables are freely available at http://digbio.missouri.edu/mupred.
authors.affiliation: Digital Biology Laboratory, Department of Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, USA.
authors.forename: Rajkumar
authors.initials: R
authors.lastname: Bondugula
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D013698:Templates, Genetic
chemical_list: D011506:Proteins