Tutorial: Solving a structure with MIR data


This tutorial will use some very good MIR data (Native and 5 derivatives from a rh-dehalogenase protein MIR dataset analyzed at 2.8 A) as an example of how to solve a MIR dataset with AutoSol. It is designed to be read all the way through, giving pointers for you along the way. Once you have read it all and run the example data and looked at the output files, you will be in a good position to run your own data through AutoSol.

Setting up to run PHENIX


echo $PHENIX

then you should get back something like this:


If instead you get:

PHENIX: undefined variable


source /xtal/phenix-1.3/phenix_env


Running the demo rh-dehalogenase data with AutoSol

To run AutoSol on the demo rh-dehalogenase data, make yourself a tutorials directory and cd into that directory:

mkdir tutorials
cd tutorials

Now type the phenix command:

phenix.run_example --help

to list the available examples. Choosing rh-dehalogenase-mir for this tutorial, you can now use the phenix command:

phenix.run_example rh-dehalogenase-mir

to solve the rh-dehalogenase structure with AutoSol. This command will copy the directory $PHENIX/examples/rh-dehalogenase-mir to your current directory (tutorials) and call it tutorials/rh-dehalogenase-mir/ . Then it will run AutoSol using the command file run.sh that is present in this tutorials/rh-dehalogenase-mir/ directory.

We are going to run this MIR dataset using a parameters file "rh-dehalogenase-mir.eff". As MIR datasets have a lot of different files and heavy-atom parameters to specify, it is easiest to run MIR by editing a simple file.

This command file run.sh is simple. It says:

echo "Running AutoSol on rhodococcus dehalogenase data..."
phenix.autosol rh-dehalogenase-mir.eff



The second line says to run the AutoSol Wizard, and use the contents of the file rh-dehalogenase-mir.eff as parameters.

Now let’s look at the rh-dehalogenase-mir.eff parameters file. Here is the entire file:

# parameters for autosol run with rh-dehalogenase-mir Native+5 derivs
autosol {
  seq_file = sequence.dat
  crystal_info {
    space_group = p21212
    unit_cell = 93.796  79.849  43.108  90.000  90.000  90.00
  native {
    data = rt_rd_1.sca
  deriv {
    data = auki_rd_1.sca
    atom_type = Au
    sites = 5
    inano = noinano *inano anoonly
    lambda = 1.5418
  deriv {
    data = hgki_rd_1.sca
    atom_type = Hg
    sites = 5
    inano = noinano *inano anoonly
    lambda = 1.5418
  deriv {
    data = ndac_rd_1.sca
    atom_type = Pt
    sites = 5
    inano = noinano *inano anoonly
    lambda = 1.5418
  deriv {
    data = hgi2_rd_1.sca
    atom_type = Hg
    sites = 5
    inano = noinano *inano anoonly
    lambda = 1.5418
  deriv {
    data = smac_1.sca
    atom_type = Sm
    sites = 5
    inano = noinano *inano anoonly
    lambda = 1.5418

Notice how the brackets ({ and }) work in this file. Everything in this file after the word "autosol" that is between the opening left-bracket ({) and and the closing right-bracket (}) is part of the autosol "scope". The AutoSol wizard looks for "autosol { lots of parameters }" and interprets everything inside these brackets. Everything outside the scope "autosol" is ignored.

Within the autosol scope there are some keywords like "atom_type = Sm", these are normally one per line.

There are also additional scopes, with keywords inside them. For example the space_group and unit_cell information are inside the scope "crystal_info".

The information about the native and each derivative is in a separate scope called "native" or "deriv". You can have one native for an MIR dataset and as many derivatives as you like.

The first part of the script, with the scope "crystal_info" tells AutoSol about the cell and space-group. These values override any values read from the input data files. Next the scope "native" gives the datafile name for the native data. Then a series of "deriv" scopes give information for each of 5 derivatives. Within this "deriv" scope you can define the datafile name, the heavy-atom name, the wavelength (lambda), f_prime and f_double_prime values for that wavelength. If you specify the heavy-atom and wavelength then the AutoSol Wizard will guess the f-prime and f-double-prime values at that wavelength. However if you know these values, then you should enter them.

Note the keyword line " inano = noinano *inano anoonly " for each derivative. This is an example of how choices are specified in a parameters file. The choice with a "*" next to it is the one that is chosen (in this case, "inano" which means include anomalous differences in phasing).

The AutoSol Wizard solves MIR datasets in several step, and in the first step, the individual derivatives are all solved separately (except using difference Fouriers to phase one derivative using a solution from another). Then when all are finished all the SIR or SIRAS datasets are phased all together with SOLVE Bayesian correlated phasing. This approach works well because a substructure determination is done separately for each derivative, and if any one of them works well, then all the derivatives can be solved.

Although the phenix.run_example rh-dehalogenase-mir command has just run AutoSol from a script (run.sh), you can run AutoSol yourself from this script with the same phenix.autosol rh-dehalogenase-mir.eff command. You can also run AutoSol from a GUI. All these possibilities are described in Using the PHENIX Wizards.

Where are my files?



What parameters did I use?


The autosol.eff file is good for more than just looking at the values of parameters, though. If you copy this file to a new one (for example autosol_lores.eff) and edit it to change the values of some of the parameters (resolution=3.0) then you can re-run AutoSol with the new values of your parameters like this:

phenix.autosol autosol_lores.eff

This command will do everything just the same as in your first run but use only the data to 3.0 A.

Reading the log files for your AutoSol run file




The AutoSol_run_1_1.log file is a running summary of what the AutoSol Wizard is doing. Here are a few of the key sections of the log files produced for the rh-dehalogenase MIR dataset.

Summary of the command-line arguments

Near the top of the log file you will find:

Starting AutoSol with the command:


Reading effective parameters from rh-dehalogenase-mir.eff
autosol {
  atom_type = None
  lambda = None
  f_prime = None
  f_double_prime = None
  wavelength_name = peak inf high low remote
  sites = None
  sites_file = None
  seq_file = "sequence.dat"

This is just a repeat of the parameters in your rh-dehalogenase-mir.eff parameters file, merged in with all the defaults for the AutoSol wizard.

Reading the datafiles.

The AutoSol Wizard will read in your datafiles and check their contents, printing out a summary for each one. This is done one dataset at a time (each native-derivative pair) until all have been read in. Here is the summary for the first derivative:

HKLIN ENTRY:  rt_rd_1.sca
FILE TYPE scalepack_no_merge_original_index
CONTENTS: ['rt_rd_1.sca', 'sca', 'unmerged', 'P 21 21 2', None, None, ['I', 'SIGI']]
Inverse hand of space group:  P 21 21 2
HKLIN ENTRY:  auki_rd_1.sca
FILE TYPE scalepack_no_merge_original_index
CONTENTS: ['auki_rd_1.sca', 'sca', 'unmerged', 'P 21 21 21', None, None, ['I', 'SIGI']]
Inverse hand of space group:  P 21 21 2
Converting the files ['rt_rd_1.sca', 'auki_rd_1.sca'] to sca format before proceeding


The input data files rt_rd_1.sca and auki_rd_1.sca are in unmerged Scalepack format. The AutoSol wizard converts everything to premerged Scalepack format before proceeding. Here is where the AutoSol Wizard identifies the format and then calls the ImportRawData Wizard:

Running import directly...
WIZARD:  ImportRawData

followed eventually by...

List of output files :
File 1: rt_rd_1_PHX.sca
File 2: auki_rd_1_PHX.sca

These output files are in premerged Scalepack format.

After completing the ImportRawData step, the AutoSol Wizard goes back to the beginning, but uses the newly-converted files rt_rd_1_PHX.sca and auki_rd_1_PHX.sca:

HKLIN ENTRY:  AutoSol_run_1_/rt_rd_1_PHX.sca
FILE TYPE scalepack_merge
Unit cell: (93.796, 79.849, 43.108, 90, 90, 90)
Space group: P 21 21 2 (No. 18)
CONTENTS: ['AutoSol_run_1_/rt_rd_1_PHX.sca', 'sca', 'premerged', 'P 21 21 2',
[93.796000000000006, 79.849000000000004, 43.107999999999997, 90.0, 90.0, 90.0],
Inverse hand of space group:  P 21 21 2
Resolution  from  AutoSol_run_1_/rt_rd_1_PHX.sca  is  2.43
HKLIN ENTRY:  AutoSol_run_1_/auki_rd_1_PHX.sca
FILE TYPE scalepack_merge
Space group: P 21 21 2 (No. 18)
CONTENTS: ['AutoSol_run_1_/auki_rd_1_PHX.sca', 'sca', 'premerged', 'P 21 21 2',
Inverse hand of space group:  P 21 21 2
Total of 2 input data files

Guessing cell contents and scattering factors

The AutoSol Wizard uses the sequence information in your sequence file (sequence.dat) and the cell parameters and space group to guess the number of NCS copies and the solvent fraction. It will use any wavelength information you provide it to guess the values of scattering factors for the heavy-atoms. If you do not give any wavelength then a value of lambda=1.5418 (Cu K alpha) will be used.

AutoSol_guess_setup_for_scaling  AutoSol  Run 1 Thu Dec 18 13:34:29 2008

Setting default value of  0.5  for  solvent_fraction
Setting default value of  200  for  residues
Solvent fraction and resolution and ha types/scatt fact
Guessing setup for scaling dataset 1
SG P 21 21 2
cell [93.796000000000006, 79.849000000000004, 43.107999999999997, 90.0, 90.0, 90.0]
Number of residues in unique chains in seq file: 294
Space group: P 21 21 2 (No. 18)
CELL VOLUME :322858.090387
Total residues:294
Total Met:6
resolution estimate: 2.43
Guessing scattering factors for  AU  at  1.5418  A

Guesses of scattering factors for  Au
         Atom     Lambda          f'        f"    datafile
         Native                                     rt_rd_1_PHX.sca
           Au     1.5418        -5.09      7.30     auki_rd_1_PHX.sca

Running phenix.xtriage


No (pseudo)merohedral twin laws were found.

Patterson analyses
  - Largest peak height   : 6.470
   (corresponding p value : 0.60758)

The largest off-origin peak in the Patterson function is 6.47% of the
height of the origin peak. No significant pseudotranslation is detected.

The results of the L-test indicate that the intensity statistics
behave as expected. No twinning is suspected.

In this space group (P21 21 2) with the cell dimensions in this structure, there are no ways to create a twinned crystal, so you do not have to worry about twinning. There is also no large off-origin peak in the native Patterson, so there does not appear to be any translational pseudo-symmetry.

Testing for anisotropy in the data

After all the SIR datasets are read in, the AutoSol Wizard tests for anisotropy by determining the range of effective anisotropic B values along the principal lattice directions. If this range is large and the ratio of the largest to the smallest value is also large then the data are by default corrected to make the anisotropy small (see the AutoSol web page for more discussion of the anisotropy correction). In the rh-dehalogenase case, the range of anisotropic B values is small and no correction is made:

 Range of aniso B:  13.21 20.51
Not using aniso-corrected data files as the range of aniso b  is only  7.3  and 'correct_aniso' is not set

Note that if any one of the datafiles in a MIR dataset has a high anisotropy, then by default all of them will be corrected for anisotropy.

Scaling MIR data

The AutoSol Wizard uses SOLVE localscaling to scale MIR data. The procedure is basically to scale all the data to the native. During this process outliers that deviate from the reference values by more that ratio_out (default=10) standard deviations (using all data in the appropriate resolution shell to estimate the SD) are rejected.

Running HYSS to find the heavy-atom substructure

The HYSS (hybrid substructure search) procedure for heavy-atom searching uses a combination of a Patterson search for 2-site solutions with direct methods recycling. The search ends when the same solution is found beginning with several different starting points. The HYSS log files are named after the datafile that they are based on and the type of differences (ano, iso) that are being used. In this rh-dehalogenase MIR dataset, the HYSS logfile for the HgKI derivative is hgki_rd_1_PHX.sca_iso_2.sca_hyss.log. The key part of this HYSS log file is:

Entering search loop:

p = peaklist index in Patterson map
f = peaklist index in two-site translation function
cc = correlation coefficient after extrapolation scan
r = number of dual-space recycling cycles
cc = final correlation coefficient

p=000 f=000 cc=0.186 r=015 cc=0.245 [ best cc: 0.245 ]
p=000 f=001 cc=0.198 r=015 cc=0.240 [ best cc: 0.245 0.240 ]
Number of matching sites of top 2 structures: 3
p=000 f=002 cc=0.174 r=015 cc=0.215 [ best cc: 0.245 0.240 ]
p=001 f=000 cc=0.212 r=015 cc=0.254 [ best cc: 0.254 0.245 0.240 ]
Number of matching sites of top 2 structures: 7
Number of matching sites of top 3 structures: 3
p=001 f=001 cc=0.219 r=015 cc=0.254 [ best cc: 0.254 0.254 0.245 0.240 ]
Number of matching sites of top 2 structures: 8
Number of matching sites of top 3 structures: 7
Number of matching sites of top 4 structures: 3
p=001 f=002 cc=0.163 r=015 cc=0.261 [ best cc: 0.261 0.254 0.254 0.245 ]
Number of matching sites of top 2 structures: 2
Number of matching sites of top 3 structures: 2
Number of matching sites of top 4 structures: 2

p=013 f=000 cc=0.184 r=015 cc=0.290 [ best cc: 0.299 0.291 0.290 0.290 ]
Number of matching sites of top 2 structures: 6
Number of matching sites of top 3 structures: 6
Number of matching sites of top 4 structures: 6

Here a correlation coefficient of 0.5 is very good (0.1 is hopeless, 0.2 is possible, 0.3 is good) and 8 sites were found that matched in the first two tries. The program continues until 4 structures all have 6 matching sites, then ends and prints out the final correlations, after taking the top 5 sites.

Finding the hand and scoring heavy-atom solutions


The AutoSol Wizard scores heavy-atom solutions based on two criteria. The first criterion is the skew of the electron density in the map (SKEW). Good values for the skew are anything greater than 0.1. In a MIR structure determination, the heavy-atom solution with the correct hand may have a more positive skew than the one with the inverse hand. The second criterion is the correlation of local RMS density (CORR_RMS). This is a measure of how contiguous the solvent and non-solvent regions are in the map. (If the local rms is low at one point and also low at neighboring points, then the solvent region must be relatively contiguous, and not split up into small regions.) For MIR datasets, SOLVE is used for calculating phases. For a MIR dataset, a figure of merit of 0.5 is acceptable, 0.6 is fine and anything above 0.7 is very good. The scores are listed in the AutoSol log file. Here is the scoring for solution 4 (the best initial map):

AutoSol_run_1_/TEMP0/resolve.scores SKEW 0.2369411
AutoSol_run_1_/TEMP0/resolve.scores CORR_RMS 0.9131303

CC-EST (BAYES-CC) SKEW : 54.1 +/- 19.5
CC-EST (BAYES-CC) CORR_RMS : 61.5 +/- 30.6
ESTIMATED MAP CC x 100:  58.1 +/- 14.0

This is a good solution, with a high (and positive) skew (0.24), and a high correlation of local rms density (0.91)

The ESTIMATED MAP CC x 100 is an estimate of the quality of the experimental electron density map (not the density-modified one). A set of real structures was used to calibrate the range of values of each score that were obtained for phases with varying quality. The resulting probability distributions are used above to estimate the correlation between the experimental map and an ideal map for this structure. Then all the estimates are combined to yield an overall Bayesian estimate of the map quality. These are reported as CC x 100 +/- 2SD. These estimated map CC values are usually fairly close, so if the estimate is 58.1 +/- 14.0 then you can be confident that your structure is solved and that the density-modified map will be quite good.

In this case the datasets used to find heavy-atom substructures were the isomorphous differences for each derivative. For each dataset one solution was found, and that solution and its inverse were scored. The scores were (skipping extra text below):

SCORING SOLUTION 1: Solution 1 using HYSS on
Dataset #1 SG="P 21 21 2", with 5 sites
ESTIMATED MAP CC x 100:  46.8 +/- 20.9

SCORING SOLUTION 2: Solution  2 using HYSS on
AutoSol_run_1_/auki_rd_1_PHX.sca_iso_1.sca and taking inverse.
ESTIMATED MAP CC x 100:  32.0 +/- 32.1

SCORING SOLUTION 3: Solution 3 using HYSS on
Dataset #2 SG="P 21 21 2", with 5 sites
ESTIMATED MAP CC x 100:  33.5 +/- 37.0

SCORING SOLUTION 4: Solution  4 using HYSS on
AutoSol_run_1_/hgki_rd_1_PHX.sca_iso_2.sca and taking inverse.
ESTIMATED MAP CC x 100:  58.1 +/- 14.0

In this case the best score was solution 4 (as shown above), based on the HGKI derivative and taking the inverse of the heavy-atom sites, with a ESTIMATED MAP CC x 100: 58.1 +/- 14.0 The score from the opposite hand was just 33.5+/- 37.0 and so the hand was clear.

Finding origin shifts between heavy-atom solutions for different derivatives and combining phases

Depending on the space group, there may be a few or infinitely many totally equivalent heavy-atom substructures for a particular native-derivative pair. These are related to each other by translations that can be thought of as offsets of the origins for the two substructures. The AutoSol Wizard identifies the allowed offsets for the space group. Then it aligns the solutions from different derivatives by finding the origin offset that maximizes the correlation of electron density in the native Fouriers for the two. Then it combines the phases from the two using addition of Hendrickson-Lattman coefficients. These combined phases are then used to score the phasing obtained by combining the two derivatives. The best combinations are iteratively combined until all available derivatives are considered and combined in an optimal fashion. Once an optimal set of derivatives and sites is found, SOLVE Bayesian correlated phasing is used to calculate a final set of native phases from the native and all the derivatives at once. Here is the best pair of derivatives from this first cycle:

Getting origin shift for 1 mapped on to 4
Keeping order of datasets for merge 2.4307589843 2.4307589843
Phases from solution 4:solve_4.mtz
Phases from solution 1:solve_1.mtz
Merged ha files in ha_4_1.pdb
Merged files in merged_4_1.mtz
FOM solution 4: 0.486    FOM solution 1: 0.415    Correlation of maps: 0.247    Ideal map correlation: 0.20169

RESULT: FOM solution 4: 0.486    FOM solution 1: 0.415    Correlation of maps: 0.247    Ideal map correlation: 0.20169
 Origin offset of solution 1: [-0.5, 0.0, 0.0]

Here solutions 1 and 4 have a map correlation of 0.25, just about the same as expected (0.20) based on the FOM of the two solutions (0.49 and .44) and assuming random errors. The two solutions differ by an origin shift of 0.5 along x.

The two solutions are then phased as a group to use as the basis for density modification:

Merging a set of solutions and phasing the group with SOLVE
 PHASED SOLUTION: Solution 9 based on MIR phasing starting from solutions 4
(dataset #2)  and 1 (dataset #1)
AutoSol_run_1_/TEMP0/resolve.scores SKEW 0.1159246
AutoSol_run_1_/TEMP0/resolve.scores CORR_RMS 0.8839763

CC-EST (BAYES-CC) SKEW : 33.5 +/- 32.8
CC-EST (BAYES-CC) CORR_RMS : 57.2 +/- 34.9
ESTIMATED MAP CC x 100:  45.4 +/- 22.8

Though worse than the HGKI solution by itself, this is reasonably good solution, with a moderate a positive skew (0.12), and a good correlation of local rms density (0.88).

As the original HGKI solution was the best, it is used for density modification and finding additional sites:

Solution  4 using HYSS on AutoSol_run_1_/hgki_rd_1_PHX.sca_iso_2.sca and taking inverse. Dataset #2 SG="P 21 21 2"
HKLIN: solve_4.mtz
Testing density modification with mask_type = histograms
RFACTOR:  0.2655
Best mask type so far is  histograms

Finding additional sites by density modification and heavy-atom difference Fouriers

Heavy-atom sites are found for derivatives that are not yet solved by phasing using the current model, carrying out density modification to improve the phases, and using the improved phases along with isomorphous differences and the phase difference between the heavy atoms and the non-heavy atoms to calculate Fourier maps showing the positions of the heavy atoms. The top peaks in these maps are used as trial heavy-atom sites (if they are not already part of the heavy-atom model.

In this example solution 4 from derivative 2 is used for this phasing/density modification/Fourier procedure. Sites are are found for all the derivatives and new solutions are created and scored using the top sites for each derivative. The combinations are then tested as above, and the highest-scoring ones are kept again. The best solution found is #96:

 PHASED SOLUTION: Solution 96 based on MIR phasing starting from solutions 4 (dataset #2)  and 14 (dataset #1)
AutoSol_run_1_/TEMP0/resolve.scores SKEW 0.4449184
AutoSol_run_1_/TEMP0/resolve.scores CORR_RMS 0.9306632

CC-EST (BAYES-CC) SKEW : 71.3 +/- 11.3
CC-EST (BAYES-CC) CORR_RMS : 63.3 +/- 28.2
ESTIMATED MAP CC x 100:  71.9 +/- 10.4

This is quite a good solution, with high skew (0.44) and correlation of local rms density (0.93). This solution is the best overall and is used for final phasing and density modification. Notice that it only contains two of the five derivatives. The merging procedure identifies which combinations of derivatives give the best phasing, and all the other derivatives are ignored.

Final phasing with SOLVE

Once the best heavy-atom solution or solutions are chosen based on Z-scores, these are used in a final round of phasing with SOLVE (for MIR phasing). In this case several nearly-equally-good solutions are available, and all are used in phasing, density modification and initial model-building, with the R-factor in density modification and the model-map correlation in model-building being used to identify the best solutions. The log file from phasing for solution 96 is in solve_96.prt. The heavy-atom model is refined and phases are calculated with Bayesian correlated MIR phasing. An important part of this phasing method is a statistical method of taking into account the correlation of non-isomorphism among derivatives. The extent of this correlation is listed in the solve_96.prt summary file:


DERIVATIVE:            1
DMIN:            ALL      8.91   5.58   4.35   3.68   3.25   2.94   2.71   2.52
RMS errors correlated and uncorrelated with others in group:
     Correlated:   54.6   67.6   58.3   57.0   65.3   58.5   34.7   38.1   37.5
   Uncorrelated:   49.6   64.3   62.8   50.1   46.7   41.4   50.8   32.9   29.2

Correlation of errors with other derivs:
DERIV 2:           0.56   0.60   0.51   0.52   0.61   0.63   0.38   0.49   0.58

Here the centric reflections in derivative 1 have non-isomorphism errors related to those in derivative 2, with a correlation coefficient overall of 0.58. another way to look at this is that the RMS correlated error is 54.6 and the RMS uncorrelated (random) error is 49.6. That means that a big part of the errors are correlated, and should be treated as such. The final occupancies and coordinates are listed at the end:

                   SITE  ATOM       OCCUP     X       Y       Z         B
CURRENT VALUES:      1    Hg       0.3744  0.2772  0.2197  0.4194    6.9985
CURRENT VALUES:      2    Hg       0.4444  0.8110  0.3415  0.4388   24.1644
CURRENT VALUES:      3    Hg       0.3327  0.2629  0.2488  0.4174   21.4129
CURRENT VALUES:      4    Hg       0.0684  0.2568  0.1753  0.3437   11.1209
CURRENT VALUES:      5    Hg       0.0918  0.3076  0.2496  0.4639   39.3362

                   SITE  ATOM       OCCUP     X       Y       Z         B
CURRENT VALUES:      1    Au       0.3856  0.7926  0.3138  0.4669   19.0809
CURRENT VALUES:      2    Au       0.4300  0.2877  0.2163  0.4266   19.5977
CURRENT VALUES:      3    Au       0.3315  0.6380  0.1629  0.4836   15.1735
CURRENT VALUES:      4    Au       0.1238  0.8116  0.3356  0.4366    1.0000
CURRENT VALUES:      5    Au       0.2690  0.2873  0.2161  0.4832    7.4303

In this case the occupancies of the top sites are about 1/3, which is fine for MIR (particularly with such heavy atoms as Hg and Au).

Statistical density modification with RESOLVE

After MIR phases are calculated with SOLVE, the AutoSol Wizard uses RESOLVE density modification to improve the quality of the electron density map. The statistical density modification in RESOLVE takes advantage of the flatness of the solvent region and the expected distribution of electron density in the region containing the macromolecule, as well as any NCS that can be found from the heavy-atom substructure. The weighted structure factors and phases (FP, PHIB) from SOLVE are used to calculate the starting map for RESOLVE, and the experimental structure factor amplitudes (FP) and MIR Hendrickson-Lattman coefficients from SOLVE are used in the density modification process. The output from RESOLVE for solution 107 can be found in resolve_96.log. Here are key sections of this output.


region, where the model distribution is given by,
and then convoluted with a gaussian with width of sigma

                             .                   .                            .
                             .                   .                            .
                             .               xxxxx                            .
                             .              xo   oxx                          .
                             .             x     . xo                         .
                             .            x      .  xx                        .
               p(rho)        .           x       .    xx                      .
                             .          x        .     xxo                    .
                             .         xx        .       xoo                  .
                             .        ox         .        xxxo                .
                             .       ox          .           xx               .
                             .      ox           .            oxxx            .
                             .    oxx            .                xxx         .
                             .  xxx              .                  oxxxxx    .

                            -2        -1         0         1         2        3

                                 normalized rho (0 = mean of solvent region)

After density modification, the curve is more ideal, with a very strong positive skew:

              .                   .                            .
              .                   .                            .
              .           xxxxxx  .                            .
              .          x    oxx .                            .
              .         x        xx                            .
              .        x          xx                           .
p(rho)        .       x           .oxx                         .
              .      xx           .  xxx                       .
              .     xx            .   oxxx                     .
              .    x              .      xxxxx                 .
              .   xx              .          xxxxxxx o         .
              .  xo               .                oxxxxxxoo   .
              .xxo                .                      xxxxxoo
              xoo                 .                           xo

             -2        -1         0         1         2        3

                  normalized rho (0 = mean of solvent region)

The key statistic from this RESOLVE density modification is the R-factor for comparison of observed structure factor amplitudes (FP) with those calculated from the density modification procedure (FC). In this rh-dehalogenase MIR phasing the R-factor is very low:

Overall R-factor for FC vs FP: 0.253 for      12293 reflections


Generation of FreeR flags

The AutoSol Wizard will create a set of free R flags indicating which reflections are not to be used in refinement. By default 5% of reflections, (up to a maximum of 2000) are reserved for this test set. If you want to supply a reflection file hires.mtz that has higher resolution than the data used to solve the structure, or has a test set already marked, then you can do this with the keyword input_refinement_file=hires.mtz. The log file tells what file is created:

Adding FreeR_flag to  AutoSol_run_1_/TEMP0/solve_96.mtz

Label for column with FP is 'FP' for
the file AutoSol_run_1_/TEMP0/solve_96.mtz
Done with adding free R set

FreeR_flag added to  solve_96.mtz

New file:  TEMP0.mtz

Copying  TEMP0.mtz  to  exptl_fobs_phases_freeR_flags_96.mtz

Checking for HL coeffs in  exptl_fobs_phases_freeR_flags_96.mtz True

Refinement file with freeR flags is in  AutoSol_run_1_/exptl_fobs_phases_freeR_flags_96.mtz

The files to be used for model-building are listed in the AutoSol log file:

THE FILE AutoSol_run_1_/resolve_96.mtz will be used for model-building
THE FILE exptl_fobs_phases_freeR_flags_96.mtz will be used for refinement

Model-building with RESOLVE

The AutoSol Wizard by default uses a very quick method to build just the secondary structure of your macromolecule, and then will try to extend that model with standard model-building. This process is controlled by the keywords helices_strands_start=True. and helices_strands_only=False . The Wizard will guess from your sequence file whether the structure is protein or RNA or DNA (but you can tell it if you want with (chain_type=PROTEIN).


In this case the secondary-structure-only model-building using solution #96 produces an initial model with 179 residues built and side chains assigned to 130, and which has a model-map correlation of 0.53:

Secondary-structure model:  AutoSol_run_1_/TEMP0/Build_1.pdb
Log file:  Build_1.log  copied to  Build_1.log
Models to combine and extend:  ['Build_1.pdb']
Using CC to score in combine_extend
Model 2: Residues built=179  placed=130  Chains=8  Model-map CC=0.53 (Build_combine_extend_2.pdb)
This is new best model with cc =  0.53
Refining model:  Build_combine_extend_2.pdb
Model: AutoSol_run_1_/TEMP0/refine_2.pdb  R/Rfree=0.41/0.45

This is quite an adequate preliminary model. It is then extended in several cycles and quite a good model is produced:

Current overall_best model and map  Thu Dec 18 16:21:17 2008
Working directory: /net/sunbird/scratch1/terwill/run_121808a/rh-dehalogenase-mir
Model (overall_best.pdb) from: refine_8.pdb
R and R-free: 0.20 0.23
Map-model CC: 0.82
Model-building logfile (overall_best.log) from: model_with_loops_9.log
Model evaluation (overall_best.log_eval) from: refine_8.pdb.log_eval
Map coeffs used for build (overall_best_denmod_map_coeffs.mtz)
from: map_coeffs.mtz
SigmaA map coeffs (overall_best_refine_map_coeffs.mtz)
from: refine_map_coeffs_8.mtz


The AutoSol_summary.dat summary file




Solution # 96  BAYES-CC: 71.9 +/- 10.4 Dataset #0   FOM: 0.6

This solution is a composite of solutions:  4 14 (Already used for Phasing at resol of 2.44)      Refined Sites: 5
NCS information  in: AutoSol_96.ncs_spec
Experimental phases in: solve_96.mtz
Experimental phases plus FreeR_flags for refinement in: exptl_fobs_phases_freeR_flags_96.mtz
Density-modified phases in: resolve_96.mtz
HA sites (PDB format) in: ha_96.pdb_formatted.pdb
Sequence file in: sequence.dat
Model in: refine_8.pdb
  Residues built: 283
  Side-chains built: 283
  Chains: 0
  Overall model-map correlation: 0.82
  R/R-free: 0.2/0.23
Phasing logfile in: solve_96.prt
Density modification logfile in: resolve_96.log (R=0.25)
Build logfile in: model_with_loops_9.log

 Score type:       SKEW    CORR_RMS
Raw scores:        0.44      0.93
100x EST OF CC:   71.32     63.28

Refined heavy atom sites (fractional):
deriv 1
xyz       0.277      0.220      0.419
xyz       0.811      0.342      0.439
xyz       0.263      0.249      0.417
xyz       0.257      0.175      0.344
xyz       0.308      0.250      0.464
deriv 2
xyz       0.793      0.314      0.467
xyz       0.288      0.216      0.427
xyz       0.638      0.163      0.484
xyz       0.812      0.336      0.437
xyz       0.287      0.216      0.483

How do I know if I have a good solution?


What to do next



Additional information
