title: Progress of 1D protein structure prediction at last.
abstract: Accuracy of predicting protein secondary structure and solvent accessibility from sequence information has been improved significantly by using information contained in multiple sequence alignments as input to a neural network system. For the Asilomar meeting, predictions for 13 proteins were generated automatically using the publicly available prediction method PHD. The results confirm the estimate of 72% three-state prediction accuracy. The fairly accurate predictions of secondary structure segments made the tool useful as a starting point for modeling of higher dimensional aspects of protein structure.
authors.affiliation: EMBL, Heidelberg, Germany.
authors.forename: B
authors.initials: B
authors.lastname: Rost
authors.forename: C
authors.initials: C
authors.lastname: Sander
mesh_terms: D000595:Amino Acid Sequence; D001426:Bacterial Proteins; D003195:Computer Communication Networks; D004268:DNA-Binding Proteins / Q000737:chemistry; D029721:Drosophila Proteins; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D016601:RNA-Binding Proteins / Q000737:chemistry; D016415:Sequence Alignment; D013381:Subtilisins / Q000737:chemistry
chemical_list: D001426:Bacterial Proteins; D004268:DNA-Binding Proteins; D029721:Drosophila Proteins; D016601:RNA-Binding Proteins; C063112:rtP protein, Bacillus subtilis; C069927:stau protein, Drosophila; D013381:Subtilisins