title: Computational prediction of secondary and supersecondary structures.
abstract: The sequence-based prediction of the secondary and supersecondary structures enjoys strong interest and finds applications in numerous areas related to the characterization and prediction of protein structure and function. Substantial efforts in these areas over the last three decades resulted in the development of accurate predictors, which take advantage of modern machine learning models and availability of evolutionary information extracted from multiple sequence alignment. In this chapter, we first introduce and motivate both prediction areas and introduce basic concepts related to the annotation and prediction of the secondary and supersecondary structures, focusing on the β hairpin, coiled coil, and α-turn-α motifs. Next, we overview state-of-the-art prediction methods, and we provide details for 12 modern secondary structure predictors and 4 representative supersecondary structure predictors. Finally, we provide several practical notes for the users of these prediction tools.
authors.affiliation: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.
authors.forename: Ke
authors.initials: K
authors.lastname: Chen
authors.forename: Lukasz
authors.initials: L
authors.lastname: Kurgan
mesh_terms: D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D020407:Internet; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D012984:Software
chemical_list: D011506:Proteins