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.
