title: Protein secondary structure prediction.
abstract: While the prediction of a native protein structure from sequence continues to remain a challenging problem, over the past decades computational methods have become quite successful in exploiting the mechanisms behind secondary structure formation. The great effort expended in this area has resulted in the development of a vast number of secondary structure prediction methods. Especially the combination of well-optimized/sensitive machine-learning algorithms and inclusion of homologous sequence information has led to increased prediction accuracies of up to 80%. In this chapter, we will first introduce some basic notions and provide a brief history of secondary structure prediction advances. Then a comprehensive overview of state-of-the-art prediction methods will be given. Finally, we will discuss open questions and challenges in this field and provide some practical recommendations for the user.
authors.affiliation: Centre for Integrative Bioinformatics VU, VU University, Amsterdam, The Netherlands.
authors.forename: Walter
authors.initials: W
authors.lastname: Pirovano
authors.forename: Jaap
authors.initials: J
authors.lastname: Heringa
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology; D057225:Data Mining; D030562:Databases, Protein; D006801:Humans; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D017385:Sequence Homology; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins