Side-chain prediction by neural networks and simulated annealing optimization.
The prediction of the side-chain positions of proteins of known tertiary backbone structure was accomplished by a combination of neural networks and a simulated annealing method. Neural networks were used to generate distributions of side-chain dihedral angles. By eliminating network outputs with low activities, we were able to generate a reduced conformational space in which Monte Carlo-simulated annealing was carried out to optimize side-chain positions. In this study of 12 proteins, the average fractions of correct chi 1, chi 2 and combined chi 1 and chi 2 (to within 40 degrees of actual structure) were 82, 72 and 68% respectively.
Hwang
J K
JK
Institute of Life Sciences, National Tsing-Hua University, Taiwan, China.
Liao
W F
WF
D000596:Amino Acids / Q000737:chemistry; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D006860:Hydrogen Bonding; D008433:Mathematics; D008958:Models, Molecular; D009010:Monte Carlo Method; D009113:Muramidase / Q000737:chemistry; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
D000596:Amino Acids; D011506:Proteins; D009113:Muramidase