Progress of 1D protein structure prediction at last.
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.
Rost
B
B
EMBL, Heidelberg, Germany.
Sander
C
C
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
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