title: Multiple linear regression for protein secondary structure prediction.
abstract: In the present work, a novel method was proposed for prediction of secondary structure. Over a database of 396 proteins (CB396) with a three-state-defining secondary structure, this method with jackknife procedure achieved an accuracy of 68.8% and SOV score of 71.4% using single sequence and an accuracy of 73.7% and SOV score of 77.3% using multiple sequence alignments. Combination of this method with DSC, PHD, PREDATOR, and NNSSP gives Q3 = 76.2% and SOV = 79.8%.
authors: National Laboratory of Biomacromolecules, Institute of Biophysics, Academia Sinica, Beijing, China. xmpan@sun5.ibp.ac.com
authors: X M
authors: XM
authors: Pan
mesh_terms: D016208:Databases, Factual; D016014:Linear Models; D017433:Protein Structure, Secondary; D016415:Sequence Alignment