Automated search procedures

Stubbs, M. T.; Huber, R.

doi:10.1107/97809553602060000680

International
Tables for
Crystallography
Volume F
Crystallography of biological macromolecules
Edited by M. G. Rossmann and E. Arnold

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International Tables for Crystallography (2006). Vol. F. ch. 12.2, p. 259 | 1 | 2 |

Section 12.2.4.2. Automated search procedures

M. T. Stubbs^a ^* and R. Huber^b

^a Institut für Pharmazeutische Chemie der Philipps-Universität Marburg, Marbacher Weg 6, D-35032 Marburg, Germany, and ^bMax-Planck-Institut für Biochemie, 82152 Martinsried, Germany
Correspondence e-mail: stubbs@mailer.uni-marburg.de

12.2.4.2. Automated search procedures

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If the derivative shows a high degree of substitution, then the Harker sections become more difficult to interpret. Furthermore, Terwilliger et al. (1987) have shown that the intrinsic noise in the difference Patterson map increases with increasing heavy-atom substitution. It is at this stage that automated procedures are invaluable.

One such automated procedure is implemented in PROTEIN (Steigemann, 1991). The unit cell is scanned for possible heavy-atom sites; for each search point (x, y, z), all possible Harker vectors are calculated, and the difference-Patterson-map values at these points are summed or multiplied. As the origin peak dominates the Patterson function, this region is set to zero. The resulting correlation map should contain peaks at all possible heavy-atom positions. The peak list can then be used to find a set of consistent heavy-atom locations through a subsequent search for difference vectors (cross vectors) between putative sites. It should be possible to locate all major and minor heavy-atom sites through repetition of this procedure. A similar strategy is adopted in the program HEAVY (Terwilliger et al., 1987), but sets of heavy-atom sites are ranked according to the probability that the peaks are not random. The program SOLVE (Terwilliger & Berendzen, 1999) takes this process a stage further, where potential heavy-atom structures are solved and refined to generate an (interpretable) electron density in an automated fashion.

The search method can also be applied in reciprocal space, where the Fourier transform of the trial heavy-atom structure is calculated, and the resulting $[F_{{H}{\rm calc}}]$ is compared to the measured differences between derivative and native structure-factor amplitudes (Rossmann et al., 1986). In the programme XtalView (McRee, 1998), the correlation coefficient between $[|F_{H}|]$ and $[|F_{PH} - F_{P}|]$ is calculated, whilst a correlation between $[F_{H}^{2}]$ and $[(F_{PH} - F_{P})^{2}]$ is used by Badger & Athay (1998). Dumas (1994b,c ) calculates the correlation between $[|F_{{H}{\rm calc}}|^{2}]$ and $[|F_{{H}{\rm estimated}}|^{2}]$ , based on the estimated lack of isomorphism.

Vagin & Teplyakov (1998) have reported a heavy-atom search based on a reciprocal-space translation function. In this case, low-resolution peaks are not removed but weighted down using a Gaussian function. Potential solutions are ranked not only according to their translation-function height, but also through their phasing power, which appears to be a stronger selection criterion.

All these searches are based upon the sequential identification of heavy-atom sites and their incorporation in a heavy-atom partial structure. Problems arise when bogus sites influence the search for further heavy-atom positions. In an attempt to overcome this problem, the heavy-atom search has been reprogrammed using a genetic algorithm, with the Patterson minimum function as a selection criterion (Chang & Lewis, 1994). This approach has the potential to reveal all heavy-atom positions in one calculation, and tests on model data have shown it to be faster than traditional sequential searches.

References

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International Tables for Crystallography (2006). Vol. F. ch. 12.2, p. 259