A New Version of DADAS (Distance Analysis in Dihedral Angle Space) and Its Performance

  • Shigeru Endo
  • Hiroshi Wako
  • Kuniaki Nagayama
  • Nobuhiro Gō
Part of the NATO ASI Series book series (NSSA, volume 225)


Among the computational algorithms to determine solution structures of proteins, the minimization of a variable target function in the dihedral angle space with the first derivative has been widely applied with its success at generating unbiased structures. The program called DADAS (Distance Analysis in Dihedral Angle Space, sometimes called DISMAN) has been extended to include additional tools, besides the rapid first derivative calculation and the variable target function: a rapid second derivative calculation, effective Metropolis Monte Carlo sampling and simulated annealing based on the Monte Carlo simulation. The new version of DADAS, called DADAS90, has some advantages, as compared with the older one, of flexibility to design new hybrid algorithms consisting of the mentioned tools and the possibility to manipulate the target function minimization from short range to long range information. The performance of each of the tools and their combination has been tested with respect to the convergence to the exact structure with the use of simulated data sets of distance constraints. The points on how to apply the program to an actual system with a limited amount of NMR data on distance and angle constraints have been also studied.


Simulated Annealing Dihedral Angle Monte Carlo Target Function Reference Structure 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    W. Braun, C. Bosch, L. R. Brown, N. Gö, and K. Wütlirich, Biochim. Biophys. Acta. 667, 377–396 (1981).PubMedCrossRefGoogle Scholar
  2. 2.
    G. M. Crippen, J. Comp. Chem. 10, 896–902 (1989).CrossRefGoogle Scholar
  3. 3.
    W. Braun, and N. Gö, J. Mol. Biol. 186, 611–626 (1985).PubMedCrossRefGoogle Scholar
  4. 4.
    T. Noguti, and N. Gö, J. Phys. Soc. Japan 52, 3685–3690 (1983).CrossRefGoogle Scholar
  5. 5.
    H. Abe, W. Braun, T. Noguti, and N. Gö, Comp. Chem. 8, 239–247 (1984).CrossRefGoogle Scholar
  6. 6.
    R. Kaptein, E. R. P. Zuiderweg, R. M. Scheek, R. Boelens, and W. F. van Gunsteren J. Mol Biol 182, 179–182 (1985).PubMedCrossRefGoogle Scholar
  7. 7.
    A. T. Brünger, G. M. Clore, A. M. Gronenborn, and M. Karplus, Proc. Natl. Acad. Sci. USA 83, 3801–3805 (1986).PubMedCrossRefGoogle Scholar
  8. 8.
    M. Nilges, G. M. Clore, and A. M. Gronenborn, FEBS Lett. 229, 317–324 (1988).PubMedCrossRefGoogle Scholar
  9. 9.
    T. F. Havel and K. Wüthrich, Bull. Muth. Biol. 46, 673–698 (1984).Google Scholar
  10. 10.
    M. F. Summers, T. L. South, B. Kim, and D. R. Hare, Biochemistry 29, 329–340 (1990).PubMedCrossRefGoogle Scholar
  11. 11.
    T. Noguti, and N. Gö, Biopolymers 24, 527–546 (1985).PubMedCrossRefGoogle Scholar
  12. 12.
    F. A. Momany, R. F. McGuire, A. W. Burgess, and H. A. Scheraga, J. Phys. Chem. 79, 2361–2381 (1975).CrossRefGoogle Scholar
  13. 13.
    G. Némethy, M. S. Pottle, and H. A. Scheraga, J. Phys. Chem. 87, 1883–1887 (1983).CrossRefGoogle Scholar
  14. 14.
    H. Wako, and N. Gö, J. Comp. Chem. 8, 625–635 (1987).CrossRefGoogle Scholar
  15. 15.
    G. Wagner, W. Braun, T. F. Havel, T. Schaumann, N. Gö, and K. Wüthrich, J. Mol Biol. 196, 611–639 (1987).PubMedCrossRefGoogle Scholar
  16. 16.
    K. Wüthrich, M. Billeter, and W. Braun, J. Mol Biol. 169, 949–961 (1983).PubMedCrossRefGoogle Scholar
  17. 17.
    G. M. Crippen and T. F. Havel, Acta Cryst. A34, 282–284 (1978).Google Scholar

Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • Shigeru Endo
    • 1
  • Hiroshi Wako
    • 2
  • Kuniaki Nagayama
    • 1
  • Nobuhiro Gō
    • 3
  1. 1.Biometrology LabJEOL Ltd.Musashino, Akishima Tokyo 196Japan
  2. 2.Waseda UniversityNishiwaseda, Shinjuku-ku, Tokyo 169Japan
  3. 3.Department of ChemistryKyoto UniversityKitashirakawa, Sakyo-ku, Kyoto 606Japan

Personalised recommendations