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Simulated annealing protein model

V Collura, I Higo, I Gamier. Modeling of protein loops by simulated annealing. Protein Sci 2 1502-1510, 1993. [Pg.306]

Figure 5 Optimization of the objective function in Modeller. Optimization of the objective function (curve) starts with a random or distorted model structure. The iteration number is indicated below each sample structure. The first approximately 2000 iterations coiTespond to the variable target function method [82] relying on the conjugate gradients technique. This approach first satisfies sequentially local restraints, then slowly introduces longer range restraints until the complete objective function IS optimized. In the remaining 4750 iterations, molecular dynamics with simulated annealing is used to refine the model [83]. CPU time needed to generate one model is about 2 mm for a 250 residue protein on a medium-sized workstation. Figure 5 Optimization of the objective function in Modeller. Optimization of the objective function (curve) starts with a random or distorted model structure. The iteration number is indicated below each sample structure. The first approximately 2000 iterations coiTespond to the variable target function method [82] relying on the conjugate gradients technique. This approach first satisfies sequentially local restraints, then slowly introduces longer range restraints until the complete objective function IS optimized. In the remaining 4750 iterations, molecular dynamics with simulated annealing is used to refine the model [83]. CPU time needed to generate one model is about 2 mm for a 250 residue protein on a medium-sized workstation.
Roitberg, A. Elber, R., Modeling side chains in peptides and proteins application of the locally enhanced sampling technique and the simulated annealing methods to find minimum energy conformations, J. Chem. Phys. 1991, 95, 9277-9287... [Pg.30]


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See also in sourсe #XX -- [ Pg.319 , Pg.320 ]




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