The “Automated structure solution - Shelxcde” experimental phasing
task builds a model automatically from intensities or amplitudes from
single or multiple-wavelength anomalous diffraction (SAD/MAD) or
single isomorphous replacement (SIRAS) experiments. The user can
optionally start or end the automated pipeline at each of the steps
below:
Substructure detection with
SHELXD
using FA values obtained from
SHELXC
Density modification and poly-alanine tracing with
SHELXE
A pipeline can be started or stopped at any stage (1.1) For
example, if an anomalously scattering substructure has been
determined already, it can be input in PDB format by selecting to
start the pipeline at Density modification and poly-Ala tracing.
Inputting a protein sequence (1.2) is beneficial for model
building. If it is not available, click off the Input protein
sequence option and input the number of protein residues per monomer.
Input the substructure atom element symbol (i.e. Se for Selenium) and
the number of substructure atoms in the asymmetric unit (1.3).
The number of substructure atoms in the asymmetric unit is the number
of substructure atoms in your protein multiplied by the number of
protein molecules that are present in the asymmetric unit. For
sulfur-SAD phasing of structures containing disulfide bonds, the
sulfur atoms that make such bonds should be counted separately in the
number of substructure sites, and the minimum distance between sites
should be given as 1.5A in the “Advanced options” section (3.3). If
the anomalous resolution is worse than about 2.1A, the disulfides
will not be resolved, so the number of disulfides (i.e. “S-S pairs”)
should be specified and the minimum distance increased to 3.5A, but
the number of sites is still the total number of anomalously
scattering atoms.
Input the anomalous reflections data in a merged MTZ file containing
the Bijvoet/anomalous intensities or structure factor amplitudes
(1.5). Alternatively, unmerged or merged XDS (XDS_ASCII.HKL),
HKL2000 (.sca) or SHELX (.hkl) files can be input (1.4).
Inputting unmerged files is recommended if SHELXC is used since it
can make use of it for improved estimates of FA values and reporting
of statistics such as the anomalous correlation coefficient
(CCanom1/2). If non-MTZ reflection data is input, fields for crystal
cell dimensions and spacegroup appear and are automatically filled by
the ccp4i2 interface if the information exists in the input file. If
they have not been filled in, please input them. For MAD data, the
reflection data can be input for all the other wavelengths collected
(1.6).
If an isomorphic native data set is available, it should be input
(1.7) as it typically extends to higher resolution. The native
dataset is always used for building and refinement but it can be also
used for SIRAS phasing: If only a single anomalous dataset is input
together with the native data, an option to switch between the SIRAS
and SAD phasing can be selected under the “Important Paramaters” tab.
Finally, a previously defined cross-validation or “free” set can be
input (1.8) or the cross-validation can be turned off, otherwise
the default is to define a new set
This page (and the next one) offers options that can improve results
if default (automatically determined) values are not optimal. From
the number of protein residues per monomer, the interface will obtain
a guess for the number of molecules in the asymmetric unit and the
solvent content based on a Matthew’s coefficient analysis. These
values are only a guess and (re-)running the task with ‘correct’
values can significantly improve results.
The third pane offers advanced options, ones you will not normally
need to change for the first run. If you suspect that substructure
detection was not successful, consider running different resolution
cutoffs (3.1) and more trials (3.2) if the results indicate
that there is an anomalous signal. For sulfur-SAD phasing, as
mentioned above, if your anomalous resolution is greater than 2.1,
adjust the minimum distance between atoms (3.3) to 1.5. Another
useful option that can improve results is increasing the number of
cycles of model building in SHELXE(3.4) or adjusting the
solvent content and inputting the correct NCS copies in the
“Important options” section. If the substructure is correct, it is
faster to re-run SHELXE with different options from the “SHELX
phasing and building” pipeline starting at Density modification and
poly-Ala tracing. The partial model and initial phases can also be
input to the “CRANK2 phasing & building” pipeline and starting at
Substructure improvement. For all steps, the gui provides the ability
to input custom keywords for individual programs (i.e. (3.5),
(3.6)). For example, inputting -t5 to SHELXE for predominantly
beta sheet proteins can provide improved tracing, but also increase
run time. Please look at all the documentation for individual
programs (links shown above) to see supported keywords. Multiple
options can be inputted, separated by a comma.
While the task is running, graphs are updated to indicate the
performance for each step. After and typically also while each step
is being performed, a summary with statistics indicating the
performance is provided. For many graphs, a shaded gray area is shown
to indicate a region with poor statistics. For some steps, the user
may click on a button on the bottom right panel of the Results
section to stop the current step. If the button is pressed, the step
will be stopped after finishing the current iteration and the next
step of the pipeline will follow. This can be used to save time if
the user is happy with the results of the current step as indicated
by the Results report.
Figure 4: Anomalous difference to noise versus resolution¶
Besides generating the FA values, this step may also provide useful
information about the input datasets. Figure 4 shows the graph of
Dano/Sigdano - a measure of the anomalous signal strength - as a
function of resolution. The larger the number, the greater the signal
and a value of about 0.8 indicates zero signal. If unmerged data was
input, the graph of CCAnom1/2 - the anomalous difference correlation
within a dataset - versus resolution (Figure 5)
Figure 5: Anomalous difference correlation within dataset¶
is shown. Again the larger the number, the greater the signal and
values over approximately 30% indicate a significant anomalous
signal. Evaluating these graphs is useful if the substructure was not
correctly identified and the user should re-run the task by manually
inputting a resolution cutoff, or even several jobs varying the
cutoff for difficult cases with small anomalous signals. Similarly,
in the case of a MAD experiment, the signed anomalous correlation
between data sets is shown (Figure 6)
Figure 6: Anomalous difference correlation between MAD datasets¶
If there is a significant anomalous signal for different
wavelengths, a high anomalous difference correlation for them will be
present (above 30%).
A good indication that the substructure has been correctly determined
is if the threshold (i.e. CFOM in the case of SHELXD) has been
reached before the maximum number of cycles has been performed: the
report summary for the Substructure detection step will state this. A
division of trials in the Distribution of CFOM (Figure 7) is also an
encouraging indicator: the graph shows the separation of incorrect
(lower CFOM) trials from correct (higher CFOM) solutions. This
division can also be observed as clustering in a graph of CC against
CCweak where the trials (shown in circles) in the the top right
indicate correct solutions with higher values of CC and CCweak.
However, some datasets, especially those with small anomalous
signals, show little discrimination between the CFOM of solutions and
of wrong substructures. The graph of occupancies versus atom number
details the number of significant peaks that have been found and can
be used cautiously to indicate the number of substructure atoms and
protein molecules in the asymmetric unit.
Figure 9: Summary and correlation coefficient of enantiomorphs¶
The results and summary of running density modification and initial
model building for both substructure enantiomorphs is presented. A
significant difference in between both hands for the correlation
coefficient in model building (Figure 9),
chain length (Figure 12) are promising sign. The most important
number output by shelxe is the CC for the traces against the native
data. If this is greater than 25%, the structure is probably solved.
This is still true even if a totally incorrect protein sequence has
been provided!
Figure 13: Summary and graphs of Residues, FOM and Rfactors¶
The model building summary (Figure 13) will indicate whether a
majority of the model was correctly built. In the graphs, an R-factor
under 40% and a large number of residue built in a small number of
fragments are good indication of success.
Finally, the model refinement summmary (Figure 14) quotes the
R-factors versus the cycle number: lower R-factors (underneath 40%)
are a good indication and a small difference between the working and
free R-factors is positive. In general, the R-values should plateau -
if this hasn’t occurred, and the R-factors are still going down, more
refinement steps can further improve the model.
