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Simulated-1g1c: Difference between revisions
→Why this is difficult to solve with SAD phasing
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so the strongest peak corresponds to the translation of molecules (0,0.5,0) but the origin peak is at 1/2 of that size, which appears significant. | so the strongest peak corresponds to the translation of molecules (0,0.5,0) but the origin peak is at 1/2 of that size, which appears significant. | ||
=== Finally solving the structure === | |||
After thinking about the most likely way that James Holton used to produce the simulated data, I hypothesized that within each frame, the radiation damage is most likely constant, and that there is a jump in radiation damage from frame 1 to 2. Unfortunately for this scenario, the scaling algorithm in CORRECT and XSCALE was changed for the version of Dec-2010, such that it produces best results when the changes are smooth. Therefore, I tried the penultimate version of XSCALE - and indeed that gives significantly better results: | |||
NOTE: Friedel pairs are treated as different reflections. | |||
SUBSET OF INTENSITY DATA WITH SIGNAL/NOISE >= -3.0 AS FUNCTION OF RESOLUTION | |||
RESOLUTION NUMBER OF REFLECTIONS COMPLETENESS R-FACTOR R-FACTOR COMPARED I/SIGMA R-meas Rmrgd-F Anomal SigAno Nano | |||
LIMIT OBSERVED UNIQUE POSSIBLE OF DATA observed expected Corr | |||
8.05 1922 467 476 98.1% 4.0% 5.8% 1888 22.37 4.5% 2.5% 84% 1.952 142 | |||
5.69 3494 864 882 98.0% 4.7% 6.0% 3429 20.85 5.4% 3.2% 77% 1.707 297 | |||
4.65 4480 1111 1136 97.8% 5.1% 5.9% 4395 21.13 5.8% 3.3% 68% 1.518 406 | |||
4.03 5197 1325 1357 97.6% 5.3% 6.0% 5101 20.57 6.1% 3.8% 48% 1.280 499 | |||
3.60 5915 1500 1533 97.8% 6.0% 6.3% 5803 19.99 6.9% 4.1% 41% 1.169 572 | |||
3.29 6601 1657 1694 97.8% 6.5% 6.5% 6476 19.42 7.5% 4.6% 27% 1.066 634 | |||
3.04 7080 1789 1830 97.8% 7.6% 7.2% 6948 17.50 8.7% 5.4% 23% 1.037 693 | |||
2.85 7682 1945 1979 98.3% 8.8% 9.0% 7528 14.75 10.1% 7.0% 15% 0.935 750 | |||
2.68 8099 2062 2100 98.2% 11.0% 11.1% 7933 12.81 12.7% 9.1% 13% 0.881 795 | |||
2.55 8351 2155 2201 97.9% 13.3% 13.7% 8178 11.16 15.4% 11.0% 12% 0.872 836 | |||
2.43 9195 2327 2376 97.9% 16.5% 17.2% 9003 9.49 19.0% 15.1% 8% 0.838 904 | |||
2.32 9495 2377 2428 97.9% 19.8% 20.3% 9304 8.62 22.7% 17.3% 4% 0.818 934 | |||
2.23 9936 2498 2551 97.9% 20.8% 21.7% 9751 8.30 23.9% 17.5% 4% 0.830 987 | |||
2.15 10217 2577 2622 98.3% 23.3% 24.0% 9990 7.74 26.7% 19.2% 4% 0.814 998 | |||
2.08 10710 2704 2766 97.8% 27.1% 28.6% 10506 6.82 31.1% 23.5% 5% 0.812 1071 | |||
2.01 10899 2777 2839 97.8% 28.1% 29.2% 10648 6.46 32.3% 25.0% 6% 0.813 1059 | |||
1.95 11361 2878 2937 98.0% 34.4% 35.5% 11134 5.55 39.5% 30.3% 3% 0.780 1136 | |||
1.90 11639 2941 3000 98.0% 40.5% 41.5% 11403 4.88 46.6% 35.9% 0% 0.787 1163 | |||
1.85 12020 3068 3123 98.2% 52.2% 55.1% 11752 3.79 60.0% 47.4% 6% 0.775 1195 | |||
1.80 11506 3003 3173 94.6% 60.8% 64.8% 11229 3.23 70.1% 58.8% 0% 0.765 1148 | |||
total 165799 42025 43003 97.7% 11.7% 12.3% 162399 10.07 13.5% 14.8% 17% 0.908 16219 | |||
Using these data (stored in [ftp://turn5.biologie.uni-konstanz.de/pub/xds-datared/1g1c/xscale.oldversion]), I was finally able to solve the structure (see screenshot below) - SHELXE traced 160 out of 198 residues. All files produced by SHELXE are in [ftp://turn5.biologie.uni-konstanz.de/pub/xds-datared/1g1c/shelx]. | |||
[[File:1g1c-shelxe.png]] | |||
It is worth mentioning that James Holton confirmed that my hypothesis is true; he also mentions that this approach is a good approximation for a multi-pass data collection. | |||
However, generally the smooth scaling gives better results than the previous method of assigning the same scale factor to all reflections of a frame; in particular, it correctly treats those reflections near the border of two frames. This example shows that it is important to | |||
* have the best data available if a structure is difficult to solve | |||
* know the options (programs, algorithms) | |||
* know as much as possible about the experiment | |||