Processing Small Molecule MicroED/3DED: Biotin

Author: Jessica Bruhn, NanoImaging Services

Warning

This tutorial was prepared using DIALS version 3.5.4, downloaded from this site. Results may differ with other versions of the software.

General Notes

  • Raw data for biotin can be downloaded from biotin

  • Data were collected with a ThermoFisher Glacios TEM and a CETA-D camera using Leginon, by the continuous rotation method.

  • Information about Leginon 1

  • Information about the sample and data collection 2

  • Final structure deposited in CCDC: BIOTIN16

  • Biotin: C10H16N2O3S, P212121, (5.1 Å, 10.4 Å, 20.8 Å, 90°, 90°, 90°)

1
Cheng A, Negro C, Bruhn JF, Rice WJ, Dallakyan S, Eng ET, Waterman DG, Potter CS, Carragher B.
Protein Sci 30, 136-150 (1 01 2021). [PMID:33030237] [PMC reprint: PMC7737759]
2
Bruhn JF, Scapin G, Cheng A, Mercado BQ, Waterman DG, Ganesh T, Dallakyan S, Read BN, Nieusma T, Lucier KW, Mayer ML, Chiang NJ, Poweleit N, McGilvray PT, Wilson TS, Mashore M, Hennessy C, Thomson S, Wang B, Potter CS, Carragher B.
Front Mol Biosci 8, 648603 (2021) [PMID:34327213] [PMC reprint: PMC8313502]

Note about pedestal and offset

  • Data collected with the CETA/CETA-D cameras includes some pixels for which the recorded intensity value is negative. Many of these pixels likely still contain valuable information and therefore should not be entirely excluded from the data.

  • SMV format, a common format for X-ray crystallography diffraction images, does not allow for signed data (i.e. negative values). Because of this, Leginon adds an offset value to all pixels when converting to SMV format to make all values positive. Leginon also excludes negative values beyond a reasonable threshold.

  • Some data processing programs, such as DIALS, allow for signed data. Therefore, it can be beneficial to subtract a reasonable pedestal value, essentially resetting the zero point.

  • For data collected with Leginon, both the offset value applied (LEGINON_OFFSET), and a suggested pedestal value (IMAGE_PEDESTAL) are listed in the image metadata.

Import images

Correct interpretation of these images requires a special format class, installed as a plug-in for the dxtbx library. Installation only has to be done once, using this command:

dxtbx.install_format -u https://raw.githubusercontent.com/dials/dxtbx_ED_formats/master/FormatSMVCetaD_TUI.py

With that in place, you can start data processing, beginning with dataset 801406_1.

cd 801406_1
mkdir DIALS
cd DIALS
dials.import ../*.img panel.pedestal=980

Note

The suggested pedestal can be found by opening one of the images with a text editor and finding the suggested pedestal value. This value is provided by Leginon: pedestal.

Find the beam centre

dials.image_viewer imported.expt
  • Opens the imported experiment in the image viewer

  • Turn off mark_beam_centre (note that the beam center from the image header is not accurate)

  • It can be helpful to find the beam center using an image with good diffraction spots. Try moving the slider at the top of the window to image 45

  • Change Zoom to 50%. For weaker data you can also increase the brightness value

  • Move the mouse to the center of the direct beam, not the center of the beamstop. It can be helpful to find Friedel pairs and draw lines between them. The beam center should be in the center of Friedel pairs. friedel_center

  • Make a note of the slow and fast beam center values at the bottom of the window (red box).

  • Close the viewer

Re-import with the correct beam center

dials.import ../*.img slow_fast_beam_centre=988,1059 panel.pedestal=980

Generate a mask for the beam center (optional)

dials.generate_mask imported.expt untrusted.circle=1059,988,100
  • The first two numbers are the beam center and the second is the diameter of the mask. Note that the beam center values are flipped compared to the import step.

Find spots

dials.find_spots imported.expt gain=0.1 d_min=1.2 mask=pixels.mask\
  min_spot_size=2 max_separation=15 max_spot=5000
  • This step can be very time consuming when working with a new detector/data collection parameters. You want to make sure you are detecting enough spots corresponding to the lattice of interest to index your data and should adjust parameters until this is the case.

  • Here I have set the gain to 0.1, which is not the true gain for this detector. Modifying the gain here allows for the detection of more spots, but this will not impact integration step, which will use the correct gain (>26).

  • Note that I have adjusted the min and max spot sizes, as well as their separation.

  • I have also set the d_min to 1.2 Å to reduce the impact from high resolution spots not related to the lattice of interest (“zingers”).

In the log file (dials.find_spots.log), note the number of spots found on each image. In this case, there are very few spots found in the first 9 images:

Found 28 strong pixels on image 1
Found 8 strong pixels on image 2
Found 27 strong pixels on image 3
Found 1 strong pixels on image 4
Found 8 strong pixels on image 5
Found 160 strong pixels on image 6
Found 55 strong pixels on image 7
Found 67 strong pixels on image 8
Found 50 strong pixels on image 9
Found 276 strong pixels on image 10
Found 347 strong pixels on image 11
Found 598 strong pixels on image 12
Found 584 strong pixels on image 13
Found 483 strong pixels on image 14
Found 506 strong pixels on image 15
Found 327 strong pixels on image 16
Found 422 strong pixels on image 17
Found 286 strong pixels on image 18
Found 413 strong pixels on image 19
Found 483 strong pixels on image 20
Found 328 strong pixels on image 21
Found 142 strong pixels on image 22
Found 189 strong pixels on image 23
Found 140 strong pixels on image 24
Found 424 strong pixels on image 25
Found 1031 strong pixels on image 26
Found 735 strong pixels on image 27
Found 419 strong pixels on image 28
Found 374 strong pixels on image 29
Found 500 strong pixels on image 30
Found 670 strong pixels on image 31
Found 718 strong pixels on image 32

This is evident when opening the images with the image viewer:

dials.image_viewer imported.expt strong.refl
https://dials.github.io/images/Biotin_NIS/few_spots.png

For now, just make a mental note that there are very few spots on images 1-9.

Indexing

dials.index imported.expt strong.refl detector.fix=distance
  • Fixing the detector distance is essential for electron diffraction data, as this generally cannot be refined at the same time as the unit cell.

  • Make sure that the camera length (distance) is carefully calibrated for your microscope as this value will not be refined by DIALS.

In the log file (dials.index.log), note the final RMSD_X and RMSD_Y. The smaller the value the better. Generally, values lower than 3 are acceptable for electron diffraction data.

RMSDs by experiment:
+-------+--------+----------+----------+------------+
|   Exp |   Nref |   RMSD_X |   RMSD_Y |     RMSD_Z |
|    id |        |     (px) |     (px) |   (images) |
|-------+--------+----------+----------+------------|
|     0 |    988 |    1.205 |   1.8365 |    0.34229 |
+-------+--------+----------+----------+------------+

Also note the % of spots indexed. 79% is quite good for electron diffraction, but lower values (~30%) are still okay.

+------------+-------------+---------------+-------------+
|   Imageset |   # indexed |   # unindexed | % indexed   |
|------------+-------------+---------------+-------------|
|          0 |        1101 |           299 | 78.6%       |
+------------+-------------+---------------+-------------+

Find the Bravais lattice (optional)

dials.refine_bravais_settings indexed.refl indexed.expt detector.fix=distance
  • Potential lattices are listed.

  • Note the Metric fit and rmsd values, as well as the recommended solutions:

    Chiral space groups corresponding to each Bravais lattice:
    aP: P1
    mP: P2 P21
    oP: P222 P2221 P21212 P212121
    +------------+--------------+--------+--------------+----------+-----------+------------------------------------------+----------+----------+
    |   Solution |   Metric fit |   rmsd | min/max cc   |   #spots | lattice   | unit_cell                                |   volume | cb_op    |
    |------------+--------------+--------+--------------+----------+-----------+------------------------------------------+----------+----------|
    |   *      5 |       0.4889 |  0.08  | 0.763/0.873  |      990 | oP        | 5.16  10.37  20.80  90.00  90.00  90.00  |     1112 | a,b,c    |
    |   *      4 |       0.4856 |  0.079 | 0.798/0.798  |      979 | mP        | 5.17  10.36  20.81  90.00  90.26  90.00  |     1114 | a,b,c    |
    |   *      3 |       0.4889 |  0.074 | 0.763/0.763  |      996 | mP        | 5.17  20.80  10.37  90.00  90.38  90.00  |     1115 | -a,-c,-b |
    |   *      2 |       0.4718 |  0.068 | 0.873/0.873  |     1009 | mP        | 10.37   5.17  20.80  90.00  90.41  90.00 |     1115 | -b,-a,-c |
    |   *      1 |       0      |  0.062 | -/-          |      989 | aP        | 5.20  10.36  20.82  90.35  90.33  90.33  |     1121 | a,b,c    |
    +------------+--------------+--------+--------------+----------+-----------+------------------------------------------+----------+----------+
    * = recommended solution
    
  • Lattice choice is generally less straightforward for electron diffraction compared to X-ray data

  • When in doubt, process the data in P1 and determine the true symmetry after processing many datasets or after phasing the data (ADDSYM in Platon is great for doing this)

  • In this case, we know that the biotin crystal should be P212121, Solution #5 (primitive orthorhombic), but let’s just process in P1 to start with

Refine the geometry

dials.refine indexed.refl indexed.expt scan_varying=False detector.fix=distance
  • We start with a round of scan-static refinement. Although refinement is done during indexing, it is good practice to run a separate round to optimise the outlier rejection.

  • After that, we follow with scan-varying refinement

dials.refine refined.refl refined.expt scan_varying=True\
  detector.fix=all parameterisation.block_width=0.25\
  beam.fix="all in_spindle_plane out_spindle_plane *wavelength"\
  beam.force_static=False beam.smoother.absolute_num_intervals=1
  • We fix the detector position and orientation with detector.fix=all, but now we are allowing the crystal unit cell, crystal orientation, and beam direction parameters to vary on a frame-by-frame basis with scan_varying=True.

Now RMSD_X and RMSD_Y have decreased significantly:

RMSDs by experiment:
+-------+--------+----------+----------+------------+
|   Exp |   Nref |   RMSD_X |   RMSD_Y |     RMSD_Z |
|    id |        |     (px) |     (px) |   (images) |
|-------+--------+----------+----------+------------|
|     0 |    890 |  0.79025 |   1.1428 |    0.24032 |
+-------+--------+----------+----------+------------+

This looks like a good model for the experiment, so we will continue on to integration.

Integration

dials.integrate refined.expt refined.refl d_min=0.8
  • We have set the high-resolution limit (d_min) to 0.8 Å. Applying a resolution cutoff at integration speeds up later steps, especially scaling multiple datasets together. You want to set this to a smaller d-spacing limit than you expect for your dataset.

Scaling

dials.scale integrated.expt integrated.refl d_min=0.8

Though we will not directly use the output from scaling individual datasets, performing this step at this stage is helpful to assess the quality of the individual dataset.

Find the file dials.scale.html and open it in a web browser.

  • Note the useful statistics by resolution shells.

  • Keep in mind that merging statistics from incomplete and low multiplicity data are less reliable. It is generally best to wait to assess the final resolution cutoff until data from multiple crystals have been combined.

  • Scroll down the page a little and click analysis_by_image_number. This brings up two graphs. Let’s focus on the “Scale and Rmerge vs batch” plot:

https://dials.github.io/images/Biotin_NIS/scale_plot_801406_1.png
  • This plots the scale factor and Rmerge on a per frame (N) basis. Let’s focus on the orange Rmerge line (right axis).

  • Note that there is an uptick in Rmerge at the beginning and the end of the dataset. The higher Rmerge values at the start are likely due to the low number of spots that were found on those images, due to suboptimal centering. The uptick at the end is more likely to be due to radiation damage.

  • We will remove these high Rmerge frames after combining data from all four crystals.

Other datasets

Repeat this process for the other three datasets.

Hint, here are the import commands I used for each dataset:

801406_1

dials.import ../*.img slow_fast_beam_centre=988,1059 panel.pedestal=980

801574_1

dials.import ../*.img slow_fast_beam_centre=992,1022 panel.pedestal=831

802003_1

dials.import ../*.img slow_fast_beam_centre=986,1026 panel.pedestal=791

810542_1

dials.import ../*.img slow_fast_beam_centre=998,1024 panel.pedestal=1619

Multi-dataset symmetry determination

We will run dials.cosym in a new directory alongside the dataset directories

mkdir cosym
cd cosym
dials.cosym ../801406_1/DIALS/integrated.{expt,refl}\
  ../801574_1/DIALS/integrated.{expt,refl}\
  ../802003_1/DIALS/integrated.{expt,refl}\
  ../810542_1/DIALS/integrated.{expt,refl}

Towards the end of the log we see:

Scoring all possible sub-groups
+-------------------+----+--------------+----------+--------+--------+---------+--------------------+
| Patterson group   |    |   Likelihood |   NetZcc |   Zcc+ |   Zcc- |   delta | Reindex operator   |
|-------------------+----+--------------+----------+--------+--------+---------+--------------------|
| P 1 2/m 1         | ** |        0.715 |     3.19 |   9.43 |   6.24 |     0.3 | -a,-c,-b           |
| P m m m           |    |        0.149 |     7.46 |   7.46 |   0    |     0.3 | -a,-b,c            |
| P -1              |    |        0.071 |    -7.46 |   0    |   7.46 |     0   | -a,-b,c            |
| P 1 2/m 1         |    |        0.033 |    -1.75 |   6.25 |   7.99 |     0.3 | -b,-a,-c           |
| P 1 2/m 1         |    |        0.032 |    -1.77 |   6.23 |   8    |     0.2 | -a,-b,c            |
+-------------------+----+--------------+----------+--------+--------+---------+--------------------+
Best solution: P 1 2/m 1
Unit cell: (5.19032, 20.8498, 10.2916, 90, 90.2178, 90)
Reindex operator: -a,-c,-b
Laue group probability: 0.715
Laue group confidence: 0.636
Reindexing operators:
x,y,z: [0, 1, 2, 3]
  • Note that the suggested Patterson group is P 1 2/m 1, not P m m m as we expect for biotin.

  • This is likely due to the data being fed into dials.cosym being a little suboptimal.

  • We could force dials.cosym to choose P212121 by adding space_group=P212121 to the above command and move on if we wanted, or we could improve the input files.

Excluding bad frames

Let’s improve the input files by going back and reprocess all datasets to exclude bad frames.

  • Remember the higher Rmerge we saw for certain frames in dials.scale.html for the first dataset? Let’s re-process each dataset and remove these “bad” frames.

  • Start at the import step and exclude some of the bad frames:

801406_1

dials.import ../*.img slow_fast_beam_centre=988,1059 panel.pedestal=980 image_range=6,129

https://dials.github.io/images/Biotin_NIS/scale_plot_exclude_frames_801406_1.png
801574_1

dials.import ../*.img slow_fast_beam_centre=992,1022 panel.pedestal=831 image_range=1,101

https://dials.github.io/images/Biotin_NIS/scale_plot_exclude_frames_801574_1.png
802003_1

dials.import ../*.img slow_fast_beam_centre=986,1026 panel.pedestal=791 image_range=1,126

https://dials.github.io/images/Biotin_NIS/scale_plot_exclude_frames_802003_1.png
810542_1

dials.import ../*.img slow_fast_beam_centre=998,1024 panel.pedestal=1619 image_range=3,128

https://dials.github.io/images/Biotin_NIS/scale_plot_exclude_frames_810542_1.png

Process as before and re-run dials.cosym with the trimmed data:

+-------------------+-----+--------------+----------+--------+--------+---------+--------------------+
| Patterson group   |     |   Likelihood |   NetZcc |   Zcc+ |   Zcc- |   delta | Reindex operator   |
|-------------------+-----+--------------+----------+--------+--------+---------+--------------------|
| P m m m           | *** |        0.975 |     9.51 |   9.51 |   0    |     0.5 | a,b,c              |
| P 1 2/m 1         |     |        0.011 |     0.36 |   9.75 |   9.39 |     0.5 | -a,-c,-b           |
| P 1 2/m 1         |     |        0.007 |    -0.17 |   9.4  |   9.56 |     0.3 | -b,-a,-c           |
| P 1 2/m 1         |     |        0.007 |    -0.19 |   9.38 |   9.57 |     0.5 | a,b,c              |
| P -1              |     |        0.001 |    -9.51 |   0    |   9.51 |     0   | a,b,c              |
+-------------------+-----+--------------+----------+--------+--------+---------+--------------------+
Best solution: P m m m
Unit cell: (5.19159, 10.2937, 20.8531, 90, 90, 90)

Now the P m m m Patterson group is the most likely, as expected.

Scale the data together

Starting from the output of dials.cosym:

dials.scale symmetrized.expt symmetrized.refl nproc=8 d_min=0.8
  • The d_min=0.8 is not actually necessary because we only integrated to 0.8 Å.

  • Open dials.scale.html

https://dials.github.io/images/Biotin_NIS/scale_plot_combined.png
  • Note the increase in Rmerge part way through collection of dataset #1 (801574_1).

  • Let’s remove some of those images and see how that changes things: dials.scale symmetrized.expt symmetrized.refl nproc=8 d_min=0.8 exclude_images="1:49:101"

  • Here we have removed images 49-101 from dataset #1 as these had a fairly high Rmerge

                ----------Merging statistics by resolution bin----------
    
     d_max  d_min   #obs  #uniq   mult.  %comp       <I>  <I/sI>    r_mrg   r_meas    r_pim   r_anom   cc1/2   cc_ano
     20.86   2.17    941     92   10.23  97.87      19.6    29.0    0.104    0.109    0.032    0.085   0.994*   0.001
      2.17   1.72    943     69   13.67  98.57       8.8    22.8    0.158    0.164    0.044    0.074   0.989*  -0.263
      1.72   1.51    935     79   11.84 100.00       8.3    17.9    0.161    0.169    0.046    0.114   0.983*  -0.011
      1.51   1.37   1013     71   14.27 100.00       4.2    11.7    0.256    0.265    0.067    0.160   0.966*  -0.523
      1.37   1.27    865     63   13.73  96.92       3.5     9.4    0.277    0.288    0.076    0.225   0.942*   0.051
      1.27   1.20    970     76   12.76 100.00       3.2     8.3    0.307    0.322    0.091    0.213   0.816*  -0.612
      1.20   1.14    917     65   14.11 100.00       2.9     7.7    0.310    0.323    0.085    0.259   0.881*   0.306
      1.14   1.09    993     68   14.60 100.00       2.0     5.6    0.393    0.408    0.104    0.272   0.965*   0.167
      1.09   1.04    920     58   15.86 100.00       2.0     5.7    0.428    0.442    0.110    0.276   0.915*  -0.078
      1.04   1.01   1036     77   13.45  96.25       1.3     3.3    0.584    0.607    0.155    0.374   0.842*  -0.259
      1.01   0.98    856     61   14.03 100.00       1.1     2.4    0.622    0.647    0.166    0.520   0.735*   0.219
      0.98   0.95    931     69   13.49 100.00       0.6     1.5    0.873    0.910    0.246    0.628   0.495*  -0.306
      0.95   0.92    876     70   12.51 100.00       0.5     1.2    0.964    1.004    0.268    0.696   0.333*  -0.261
      0.92   0.90    751     62   12.11 100.00       0.3     0.9    1.146    1.194    0.326    0.805   0.491*  -0.324
      0.90   0.88    631     60   10.52 100.00       0.3     0.5    1.556    1.648    0.505    1.224   0.554*   0.219
      0.88   0.86    572     58    9.86  96.67       0.2     0.4    1.422    1.503    0.459    1.251   0.260  -0.050
      0.86   0.84    513     76    6.75 100.00       0.2     0.3    1.791    1.939    0.673    1.371   0.322*   0.051
      0.84   0.83    427     65    6.57  98.48       0.2     0.3    1.808    1.947    0.666    1.225   0.389*  -0.109
      0.83   0.81    423     62    6.82  95.38       0.2     0.2    2.817    3.054    1.078    1.996   0.099   0.173
      0.81   0.80    285     60    4.75  88.24       0.1     0.2    2.769    3.123    1.312    1.477  -0.003  -0.432
     20.85   0.80  15798   1361   11.61  98.27       3.4     7.1    0.246    0.257    0.071    0.205   0.988*  -0.111
    
    
    Resolution limit suggested from CC½ fit (limit CC½=0.3): 0.83
    
  • Note that the completeness in the lower resolution shells have decreased a small amount. Let’s try adding back in some frames to boost the completeness back to 100% in the low-resolution shells: dials.scale symmetrized.expt symmetrized.refl nproc=8 d_min=0.8 exclude_images="1:60:101"

                ----------Merging statistics by resolution bin----------
    
     d_max  d_min   #obs  #uniq   mult.  %comp       <I>  <I/sI>    r_mrg   r_meas    r_pim   r_anom   cc1/2   cc_ano
     20.86   2.17    968     94   10.30 100.00      20.3    27.6    0.110    0.116    0.034    0.079   0.995*  -0.080
      2.17   1.72    961     71   13.54 100.00       9.1    13.3    0.164    0.170    0.045    0.075   0.989*  -0.492
      1.72   1.51    966     79   12.23 100.00       8.5     9.7    0.178    0.185    0.051    0.126   0.968*   0.074
      1.51   1.37   1034     71   14.56 100.00       4.1     5.2    0.282    0.292    0.074    0.178   0.960*  -0.365
      1.37   1.27    891     65   13.71 100.00       3.1     3.6    0.332    0.345    0.090    0.288   0.906*  -0.148
      1.27   1.20   1003     76   13.20 100.00       2.8     3.0    0.364    0.379    0.103    0.267   0.708*  -0.277
      1.20   1.14    932     65   14.34 100.00       2.3     2.5    0.361    0.374    0.096    0.296   0.905*   0.020
      1.14   1.09   1013     68   14.90 100.00       1.6     1.8    0.439    0.455    0.116    0.302   0.947*   0.242
      1.09   1.04    937     58   16.16 100.00       1.5     1.7    0.477    0.493    0.121    0.361   0.812*  -0.213
      1.04   1.01   1081     80   13.51 100.00       0.9     1.0    0.672    0.699    0.180    0.393   0.739*   0.044
      1.01   0.98    879     61   14.41 100.00       0.8     0.7    0.710    0.736    0.184    0.554   0.523*  -0.264
      0.98   0.95    951     69   13.78 100.00       0.5     0.4    0.917    0.953    0.249    0.481   0.571*  -0.105
      0.95   0.92    897     70   12.81 100.00       0.4     0.4    1.082    1.124    0.294    0.796   0.386*  -0.125
      0.92   0.90    760     62   12.26 100.00       0.2     0.2    1.439    1.500    0.409    0.920   0.346*  -0.262
      0.90   0.88    642     60   10.70 100.00       0.2     0.2    1.777    1.866    0.544    1.208   0.514*   0.190
      0.88   0.86    593     61    9.72 100.00       0.2     0.1    1.706    1.827    0.605    1.294  -0.290  -0.089
      0.86   0.84    538     76    7.08 100.00       0.1     0.1    2.044    2.195    0.744    1.534   0.289*  -0.067
      0.84   0.83    436     65    6.71  98.48       0.1     0.1    2.146    2.314    0.799    1.353   0.363*   0.005
      0.83   0.81    432     62    6.97  95.38       0.2     0.1    3.472    3.752    1.306    1.794   0.137   0.052
      0.81   0.80    291     60    4.85  88.24       0.1     0.1    4.041    4.519    1.851    1.350   0.128  -0.490
     20.85   0.80  16205   1373   11.80  99.13       3.3     4.2    0.255    0.266    0.072    0.201   0.988*  -0.127
    
    
    Resolution limit suggested from CC½ fit (limit CC½=0.3): 0.85
    
  • This looks a lot better in terms of completeness.

  • Looking at dials.scale.html we can probably improve this a little by removing some images from the end of dataset #2

https://dials.github.io/images/Biotin_NIS/scale_plot_combined_exclude_1.png
  • So, we run dials.scale symmetrized.expt symmetrized.refl nproc=8 d_min=0.8 exclude_images="1:60:101" exclude_images="2:121:126"

               ----------Merging statistics by resolution bin----------
    
    d_max  d_min   #obs  #uniq   mult.  %comp       <I>  <I/sI>    r_mrg   r_meas    r_pim   r_anom   cc1/2   cc_ano
    20.86   2.17    954     94   10.15 100.00      20.4    27.4    0.105    0.110    0.033    0.077   0.993*  -0.028
     2.17   1.72    943     71   13.28 100.00       9.1    13.5    0.161    0.168    0.044    0.075   0.991*  -0.079
     1.72   1.51    957     79   12.11 100.00       8.4     9.9    0.178    0.186    0.051    0.126   0.971*   0.053
     1.51   1.37   1017     71   14.32 100.00       4.1     5.3    0.272    0.282    0.073    0.177   0.956*  -0.203
     1.37   1.27    885     65   13.62 100.00       3.1     3.7    0.334    0.347    0.090    0.288   0.908*  -0.017
     1.27   1.20    988     76   13.00 100.00       2.7     3.1    0.359    0.375    0.102    0.265   0.722*  -0.331
     1.20   1.14    914     65   14.06 100.00       2.3     2.6    0.352    0.366    0.096    0.295   0.900*   0.192
     1.14   1.09    994     68   14.62 100.00       1.6     1.8    0.436    0.452    0.116    0.304   0.870*   0.185
     1.09   1.04    920     58   15.86 100.00       1.4     1.8    0.475    0.490    0.122    0.364   0.761*  -0.397
     1.04   1.01   1072     80   13.40 100.00       0.9     1.0    0.637    0.663    0.173    0.394   0.775*  -0.137
     1.01   0.98    869     61   14.25 100.00       0.8     0.8    0.714    0.741    0.186    0.560   0.608*  -0.175
     0.98   0.95    942     69   13.65 100.00       0.4     0.5    0.921    0.957    0.253    0.486   0.755*  -0.151
     0.95   0.92    888     70   12.69 100.00       0.4     0.4    1.059    1.101    0.290    0.798   0.289*  -0.155
     0.92   0.90    746     62   12.03 100.00       0.2     0.2    1.471    1.534    0.421    0.911   0.101  -0.218
     0.90   0.88    629     60   10.48 100.00       0.2     0.2    1.635    1.719    0.507    1.189   0.550*   0.023
     0.88   0.86    584     61    9.57 100.00       0.2     0.1    1.679    1.800    0.602    1.315  -0.212  -0.147
     0.86   0.84    529     76    6.96 100.00       0.1     0.1    2.107    2.272    0.790    1.577   0.223  -0.078
     0.84   0.83    429     65    6.60  98.48       0.1     0.1    2.101    2.274    0.802    1.318   0.569*  -0.058
     0.83   0.81    426     62    6.87  95.38       0.2     0.1    2.047    2.232    0.808    1.842   0.220   0.046
     0.81   0.80    287     60    4.78  88.24       0.1     0.1    4.537    5.104    2.145    1.340   0.100  -0.650
    20.85   0.80  15973   1373   11.63  99.13       3.3     4.2    0.248    0.259    0.071    0.199   0.991*  -0.148
    
https://dials.github.io/images/Biotin_NIS/scale_plot_combined_exclude_2.png
  • This looks reasonably good

Export the data

We export the scaled, unmerged dataset to MTZ format:

dials.export scaled.refl scaled.expt mtz.hklout=biotin_P222.mtz
  • It can be helpful to open the biotin_P222.mtz file in CCP4’s ViewHKL program

  • You want to see a nice decay of intensities, with more intense spots in the middle and lower intensity spots towards the edges. You also want to make sure that you see some variation in your reflection intensities. They should not all be the same value as can happen when scaling goes poorly.

https://dials.github.io/images/Biotin_NIS/viewhkl.png

Now we want to convert to SHELX format for structure solution

xia2.to_shelx biotin_P222.mtz biotin_P222 CHNOS

Solve the structure

Prepare an .ins file for SHELXT or SHELXD by either running XPREP or manually editing the .ins file as shown below:

TITL biotin_P212121 in P2(1)2(1)2(1)
CELL 0.02501   5.19177  10.29400  20.84910  90.0000  90.0000  90.0000
ZERR    4.00   0.00104   0.00206   0.00417   0.0000   0.0000   0.0000
LATT -1
SYMM 0.5-X, -Y, 0.5+Z
SYMM -X, 0.5+Y, 0.5-Z
SYMM 0.5+X, 0.5-Y, -Z
SFAC C H N O S
UNIT 40 64 8 12 4
FIND    10
PLOP    14    17    19
MIND 1.0 -0.1
NTRY 1000
HKLF 4
END

Now phase the data:

shelxt biotin_P212121

If you are processing a more challenging organic small molecule dataset you could try this:

shelxt biotin_P212121 -y -m1000

If this fails (it should not fail for biotin as this is really high-quality data), you could try SHELXD, which has been more successful in phasing challenging datasets here at NIS:

shelxd biotin_P212121
  • Note that you have to have the space group correct for SHELXD to work.

  • When you are having difficulties, try solving this in P1 and figuring out the proper space group once you have a solution with ADDSYM in PLATON.

  • It can help to increase the NTRY. Try 50000 for challenging cases.

If SHELXD fails, I usually go back and remove more datasets/bad images and try again.

If that fails, you can try molecular replacement with PHASER.

  • Note that you will need merged data and an R-free set. I recommend using dials.merge and then freerflag in CCP4.

  • The model used needs to be very accurate in terms of RMSD with the final structure.

  • When defining the contents of the ASU, try setting this to 2% solvent content.

If you are still struggling, I recommend going back and collecting more data or growing better crystals. Sometimes one crystal will diffract to much higher resolution than the others. For challenging cases, we have collected data from ~200 crystals just to find ~5 good ones to combine.

Good luck!