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 the STARANISO 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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