title: Predicting protein secondary structure and solvent accessibility with an improved multiple linear regression method.
abstract: We have improved the multiple linear regression (MLR) algorithm for protein secondary structure prediction by combining it with the evolutionary information provided by multiple sequence alignment of PSI-BLAST. On the CB513 dataset, the three states average overall per-residue accuracy, Q(3), reached 76.4%, while segment overlap accuracy, SOV99, reached 73.2%, using a rigorous jackknife procedure and the strictest reduction of eight states DSSP definition to three states. This represents an improvement of approximately 5% on overall per-residue accuracy compared with previous work. The relative solvent accessibility prediction also benefited from this combination of methods. The system achieved 77.7% average jackknifed accuracy for two states prediction based on a 25% relative solvent accessibility mode, with a Mathews' correlation coefficient of 0.548. The improved MLR secondary structure and relative solvent accessibility prediction server is available at http://spg.biosci.tsinghua.edu.cn/.
authors.affiliation: National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, People's Republic of China.
authors.forename: Sanbo
authors.initials: S
authors.lastname: Qin
authors.forename: Yun
authors.initials: Y
authors.lastname: He
authors.forename: Xian-Ming
authors.initials: XM
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016014:Linear Models; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012997:Solvents / Q000737:chemistry*
chemical_list: D011506:Proteins; D012997:Solvents