Dear Shenyuan Xu,
I dealt with a similar problem recently. In my particular case, a
combination of the correction with STARANISO and a subsequent molecular
replacement with the MoRDa pipeline helped me a lot. MoRDa was able to
suggest how to place individual protein domains separately in the unit
cell and which model templates to choose. Then I rebuilt carefully the
main chain(s).
I have heard several times that BUSTER is very smart in the refinement
of low-resolution structures. But I do not have personal experience.
By the way, regarding the reported completeness after the STARANISO
correction, is it spherical or ellipsoidal?
Good luck!
Martin
On 24. 10. 22 3:43, Xu, Shenyuan wrote:
Dear CCP4 community,
I have encountered a dataset, which I thought should be easy to solve.
The volume of the cell unit seems to be expanded after image 271,
which I think is caused by radiation damage. After removing the last
few images, the scaled statistics seem good with the resolution set at
3:07 A:
d_max d_min #obs #uniq mult. %comp <I> <I/sI> r_mrg
r_meas r_pim r_anom cc1/2 cc_ano
91.54 8.33 3086 1197 2.58 98.44 345.2 51.3 0.101
0.133 0.086 0.145 0.973* -0.148
8.33 6.61 3005 1225 2.45 99.51 256.5 27.3 0.160
0.212 0.138 0.260 0.923* -0.140
6.61 5.78 2883 1190 2.42 98.43 92.9 9.4 0.278
0.368 0.237 0.461 0.541* -0.165
5.78 5.25 3195 1217 2.63 99.10 75.6 8.1 0.283
0.369 0.233 0.436 0.618* -0.107
5.25 4.87 3192 1206 2.65 99.42 92.5 8.3 0.281
0.367 0.232 0.427 0.856* -0.187
4.87 4.59 3230 1213 2.66 99.02 118.4 10.3 0.288
0.378 0.240 0.456 0.870* -0.053
4.59 4.36 2797 1156 2.42 92.93 137.0 12.2 0.309
0.410 0.266 0.496 0.843* -0.239
4.36 4.17 2694 1118 2.41 93.17 280.2 16.2 0.419
0.575 0.392 0.847 0.491* -0.142
4.17 4.01 3102 1188 2.61 95.50 133.6 9.9 0.392
0.516 0.330 0.622 0.663* -0.268
4.01 3.87 3224 1205 2.68 98.69 120.4 7.3 0.406
0.539 0.349 0.687 0.791* -0.126
3.87 3.75 2939 1181 2.49 98.91 90.7 6.8 0.491
0.652 0.425 0.810 0.691* -0.042
3.75 3.64 1981 1021 1.94 82.34 87.3 5.0 0.557
0.756 0.506 0.885 0.540* -0.018
3.64 3.54 2374 1082 2.19 89.13 194.6 11.2 0.502
0.675 0.447 0.973 0.625* -0.237
3.54 3.46 2622 1122 2.34 92.35 310.5 10.1 0.432
0.585 0.392 0.851 0.603* -0.154
3.46 3.38 1739 945 1.84 76.58 48.2 3.6 0.930
1.247 0.822 1.542 0.444* -0.046
3.38 3.31 2847 1240 2.30 98.57 85.4 4.2 0.691
0.930 0.616 1.242 0.523* -0.060
3.31 3.24 2838 1153 2.46 97.88 70.8 3.3 0.693
0.934 0.620 1.341 0.362* 0.006
3.24 3.18 3097 1212 2.56 97.66 71.4 2.5 0.692
0.924 0.605 1.173 0.526* 0.044
3.18 3.12 3204 1216 2.63 99.10 79.6 3.7 0.668
0.886 0.576 1.338 0.440* -0.081
3.12 3.07 3059 1172 2.61 98.16 73.5 2.4 0.714
0.945 0.612 1.633 0.362* -0.177
91.50 3.07 57108 23259 2.46 95.22 138.4 10.8 0.383
0.513 0.336 0.671 0.656* -0.149
I used Mrbump to do the MR, most sequence identities of the starting
templates are more than 0.85, and some of them are structures
predicted from alpha fold 2. But after refinement (including
jelly-body, proSmart, TLC), the best R/Free R stuck at around
0.42/0.45. Inspecting the electron density map shows that the model
does not fit the electron density well. The space group is P1, Cell is
58.49 63.97 91.60 91.71 91.86 99.47, should be 5 or 6 molecules in the
asymmetric unit.
I checked the data quality, it said the data is highly anisotropy. Then
I searched the CCP4 forum and used theSTARANISO Server and UCLA
server, but still cannot improve the refinement. The data
statistics after drawing ellipsoidal resolution limits is good:
<pre>
resolution observed redundancy completenes rmerge
i/sigma
before/after before/after before/after before/after
before/after
7.90 4957 1185 3.5 3.3 97.3% 92.6% 13.0% 12.7%
11.9 7.2
5.58 8023 2397 3.0 3.6 98.6% 98.9% 30.3% 11.8%
5.5 7.5
4.56 11176 2624 3.3 3.1 99.1% 97.5% 57.1% 18.2%
3.7 6.3
3.95 11694 3213 2.9 3.1 94.0% 99.0% 53.1% 27.5%
4.2 4.3
3.53 12065 3524 2.6 3.0 92.8% 99.0% 49.8% 45.9%
3.2 2.8
3.22 12828 4098 2.5 3.2 93.8% 99.1% 69.3% 53.2%
2.1 2.7
2.98 17566 4628 3.2 3.3 98.6% 99.1% 98.1% 60.0%
1.6 2.7
2.79 19405 4631 3.3 3.1 98.1% 96.8% 203.0% 70.3%
0.8 2.7
2.63 20871 4001 3.3 2.5 98.1% 90.1% 760.1% 41.2%
0.3 3.4
2.50 20352 5406 3.0 3.2 97.8% 97.3% -99.9% 66.0%
0.0 2.6
2.38 17281 5689 2.5 3.2 93.8% 98.2% -99.9% 81.4%
0.0 1.8
2.28 13198 3874 1.8 2.1 68.6% 88.0% 546.3% 193.2%
0.1 0.6
2.19 10429 4961 1.4 2.6 52.5% 92.7% -99.9% 31.0%
0.0 4.8
2.11 7776 4275 1.0 2.1 41.2% 90.1% -99.9% 266.0%
0.0 0.6
2.04 5561 6084 0.7 2.9 30.9% 97.6% 531.4% 109.2%
0.1 1.1
1.97 3818 6700 0.4 3.1 23.4% 97.4% -99.9% 85.8%
0.0 1.6
1.91 2269 5906 0.3 2.7 17.0% 83.9% -99.9% 87.3%
0.0 1.5
1.86 1191 5250 0.1 2.3 10.4% 69.2% -99.9% 167.5%
0.0 0.9
1.81 433 3961 0.0 1.7 4.5% 51.0% -99.9% 225.7%
0.0 0.8
1.77 60 2168 0.0 0.9 0.6% 27.2% -99.9% 254.0%
0.0 0.7
total 200953 84575 1.6 2.6 54.7% 84.6% 78.6% 54.0%
1.4 2.4
</pre>
Any suggestions would help. I can provide the collected image dataset
and the sequence if anyone is interested.
Thanks,
Shenyuan Xu
Miami University
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