title: Computational Prediction of Protein Secondary Structure from Sequence.
abstract: Secondary structure of proteins refers to local and repetitive conformations, such as α-helices and β-strands, which occur in protein structures. Computational prediction of secondary structure from protein sequences has a long history with three generations of predictive methods. This unit summarizes several recent third-generation predictors. We discuss their inputs and outputs, availability, and predictive performance and explain how to perform and interpret their predictions. We cover methods for the prediction of the 3-class secondary structure states (helix, strand, and coil) as well as the 8-class secondary structure states. Recent empirical assessments and our small-scale analysis reveal that these predictions are characterized by high levels of accuracy, between 70% and 80%. We emphasize that modern predictors are available to end users in the form of convenient-to-use Web servers and stand-alone software. © 2016 by John Wiley & Sons, Inc.
authors: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
authors: Fanchi
authors: F
authors: Meng
authors: Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia.
authors: Lukasz
authors: L
authors: Kurgan
mesh_terms: D000595:Amino Acid Sequence; D003198:Computer Simulation; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012984:Software
chemical_list: D011506:Proteins
keywords: DSSP; coil; helix; prediction; secondary structure of proteins; strand