title: Benchmarking secondary structure prediction for fold recognition.
abstract: If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the "usefulness" and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.
authors.affiliation: Bioinformatics Unit, Department of Computer Science, University College London, London, United Kingdom. l.mcguffin@cs.ucl.ac.uk
authors.forename: Liam J
authors.initials: LJ
authors.lastname: McGuffin
authors.forename: David T
authors.initials: DT
authors.lastname: Jones
mesh_terms: D008958:Models, Molecular; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry; D015203:Reproducibility of Results
chemical_list: D011506:Proteins