title: Quantification of secondary structure prediction improvement using multiple alignments.
abstract: The use of multiple sequence alignments for secondary structure predictions is analysed. Seven different protein families, containing only sequences of known structure, were considered to provide a range of alignment and prediction conditions. Using alignments obtained by spatial superposition of main chain atoms in known tertiary protein structures allowed a mean of 8% in secondary structure prediction accuracy, when compared to those obtained from the individual sequences. Substitution of these alignments by those determined directly from an automated sequence alignment algorithm showed variations in the prediction accuracy which correlated with the quality of the multiple alignments and distance of the primary sequence. Secondary structure predictions can be reliably improved using alignments from an automatic alignment procedure with a mean increase of 6.8%, giving an overall prediction accuracy of 68.5%, if there is a minimum of 25% sequence identity between all sequences in a family.
authors.affiliation: Unité d'Ingénierie des Protéines, Biotechnologies, INRA, Jouy-en-Josas, France.
authors.forename: J M
authors.initials: JM
authors.lastname: Levin
authors.forename: S
authors.initials: S
authors.lastname: Pascarella
authors.forename: P
authors.initials: P
authors.lastname: Argos
authors.forename: J
authors.initials: J
authors.lastname: Garnier
mesh_terms: D000595:Amino Acid Sequence; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary; D011506:Proteins / Q000145:classification; D015203:Reproducibility of Results; D016415:Sequence Alignment / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins