International Tables for Crystallography (2006). Vol. F, ch. 18.2, pp. 375-381   | 1 | 2 |
doi: 10.1107/97809553602060000694

Chapter 18.2. Enhanced macromolecular refinement by simulated annealing

Contents

  • 18.2. Enhanced macromolecular refinement by simulated annealing  (pp. 375-381) | html | pdf | chapter contents |
    • 18.2.1. Introduction  (p. 375) | html | pdf |
    • 18.2.2. Cross validation  (p. 375) | html | pdf |
    • 18.2.3. The target function  (pp. 375-377) | html | pdf |
      • 18.2.3.1. X-ray diffraction data versus model  (pp. 376-377) | html | pdf |
      • 18.2.3.2. A priori chemical information  (p. 377) | html | pdf |
    • 18.2.4. Searching conformational space  (pp. 377-379) | html | pdf |
      • 18.2.4.1. Molecular dynamics  (p. 378) | html | pdf |
      • 18.2.4.2. Temperature control  (p. 378) | html | pdf |
      • 18.2.4.3. Annealing schedules  (pp. 378-379) | html | pdf |
      • 18.2.4.4. An intuitive explanation of simulated annealing  (p. 379) | html | pdf |
    • 18.2.5. Examples  (pp. 379-380) | html | pdf |
    • 18.2.6. Multi-start refinement and structure-factor averaging  (p. 380) | html | pdf |
    • 18.2.7. Ensemble models  (p. 380) | html | pdf |
    • 18.2.8. Conclusions  (p. 381) | html | pdf |
    • References | html | pdf |
    • Figures
      • Fig. 18.2.2.1. Effect of resolution on coordinate-error estimates  (p. 376) | html | pdf |
      • Fig. 18.2.3.1. The Gaussian probability distribution forms the basis of maximum-likelihood targets in crystallographic refinement  (p. 376) | html | pdf |
      • Fig. 18.2.4.1. Illustration of simulated annealing for minimization of a one-dimensional function  (p. 378) | html | pdf |
      • Fig. 18.2.5.1. Simulated annealing produces better models than extensive conjugate-gradient minimization  (p. 379) | html | pdf |
      • Fig. 18.2.5.2. Maximum-likelihood targets significantly decrease model bias in simulated-annealing refinement  (p. 380) | html | pdf |