Protein structure prediction system based on artificial neural networks.
Methods based on the neural network techniques are among the most accurate in the secondary structure prediction of globular proteins. Here the same principles have been used for the tertiary structure prediction problem. The map of dihedral phi and psi angles is divided into 10 by 10 squares each spanning 36 by 36 degrees. By predicting the classification of each residue in the protein chain in this map a rough tertiary structure can be deduced. A complete prediction system running on a cluster of workstations and a graphical user interface was developed.
Vanhala
J
J
Tampere University of Technology/Microelectronics Laboratory, Finland.
Kaski
K
K
D000595:Amino Acid Sequence; D000818:Animals; D007611:Aprotinin / Q000737:chemistry; D002417:Cattle; D005544:Forecasting; D006801:Humans; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009113:Muramidase / Q000737:chemistry; D016571:Neural Networks, Computer; D010191:Pancreatic Polypeptide / Q000737:chemistry; D017434:Protein Structure, Tertiary; D014584:User-Computer Interface
C009725:pancreatic polypeptide, avian; D010191:Pancreatic Polypeptide; D007611:Aprotinin; D009113:Muramidase