# Automated structure solution with the Phenix suite.

Peter H. Zwart\<aug\>\<au\>\<fnm\> \</snm\>\</au\>,\<orf
id=\"^a^\"\>\<cor email=\"PDAdams@lbl.gov\"\>\</cor\> Pavel V.
Afonine,^a^ \<au\>\<fnm\> \<au\>\<fnm\>Ralf W.\</fnm\>
\<snm\>Grosse-Kunstleve\</snm\>\</au\>,\<orf id=\"^a^\"\>
\<au\>\<fnm\>Li-Wei\</fnm\> \<snm\>Hung\</snm\>\</au\>,\<orf
id=\"^B^\"\> \<au\>\<fnm\>Thomas R.\</fnm\>
\<snm\>Ioerger\</snm\>\</au\>,\<orf id=\"^c^\"\> \<au\>\<fnm\>Airlie J.
\</fnm\> \<snm\>McCoy\</snm\>\</au\>,\<orf id=\"^d^\"\> Erik McKee,^c^
\<au\>\<fnm\>Nigel W.\</fnm\> \<snm\>Moriarty\</snm\>\</au\>,\<orf
id=\"^a^\"\> \<au\>\<fnm\> \<au\>\<fnm\>Randy J.\</fnm\>
\<snm\>Read\</snm\>\</au\>,\<orf id=\"^d^\"\> David RICHARDSON, ^e^ JANE
RICHARDSON, ^E^ \<au\>\<fnm\>\"\> \<au\>\<fnm\>James C.\</fnm\>
\<snm\>Sacchettini\</snm\>\</au\>,\<orf id=\"^f^\"\>
\<au\>\<fnm\>Nicholas K.\</fnm\> \<snm\>Sauter\</snm\>\</au\>,\<orf
id=\"^a^\"\> \<au\>\<fnm\>Laurent C.\</fnm\>
\<snm\>Storoni,\</snm\>\</au\>\<orf id=\"^d^ \"\>\<au\>\<fnm\>Thomas
C.\</fnm\> \<snm\>Terwilliger\</snm\>\</au\>\<orf id=\"^g^\"\> and Paul
D.\</fnm\> \<snm\>Adams ^a\*^

\<aff\>\<oid id=\"^a^\"\>Lawrence Berkeley National Laboratory, One
Cyclotron Road, BLDG 64R0121, Berkeley, CA 94720,
\<cny\>USA\</cny\>\</aff\>, \<aff\>\<oid id=\"^b^\"\>Biophysics Group,
Mail Stop D454, Los Alamos National Laboratory, Los Alamos, NM 87545,
\<cny\>USA\</cny\>\</aff\>, \<aff\>\<oid id=\"^c^\"\>Department of
Computer Science, Texas A&M University, 301 H.R. Bright Building, 3112
TAMU, College Station, TX 77843, \<cny\>USA\</cny\>\</aff\>,
\<aff\>\<oid id=\"^d^\"\>Department of Haematology, University of
Cambridge, Cambridge Institute for Medical Research, Wellcome Trust/MRC
Building, Hills Road, Cambridge, CB2 2XY, \<cny\>UK\</cny\>\</aff\>,
\<aff\>\<oid id=\"^f^\"\>Department of Biochemistry and Biophysics,
Texas A&M University, 103 Biochemistry/Biophysics Building, 2128 TAMU,
College Station, TX 77843, \<cny\>USA\</cny\>\</aff\>, and \<aff\>\<oid
id=\"^g^\"\>Los Alamos National Laboratory, Mailstop M888, Los Alamos,
NM 87545, \<cny\>USA\</cny\>\</aff\>\</aug\>. \*E-mail: PDAdams@lbl.gov

### 1. Introduction 

The world-wide efforts of many structural genomics projects, in
particular the NIH Protein Structure Initiative have lead to many new
technological advances in robotized cloning, sample expression and
purification, screening of crystallization conditions (1), data
collection at synchrotron sources (2), and structure solution (see (3)).
These have made high-throughput structure determination possible, and
achievable for a number of the structures solved at structural genomics
centers. More recently these technologies have started to be adopted by
many \'more traditional\' structural biology laboratories, where they
are being applied to challenging systems such as large molecular
complexes, and membrane proteins.

The demand for better software for crystallographic structure solution
is increasing as it becomes possible for researchers to study more
systems using high-throughput methods. This increased demand will need
to be at least partially met by automated software for structure
solution. Automation cannot rely on the availability of manual input
from a trained crystallographer; the software must be able to make many
complex decisions itself. Individual investigator research groups face a
related problem as more biologists and biochemists make use of
crystallography purely as a technique to better understand their
biological system. Often there is insufficient time to obtain a very
detailed expert crystallographic knowledge. A significant amount of this
knowledge must therefore be built into the software. Furthermore,
automated processes avoid possible subjective interpretation from manual
interpretation of complex numerical data that can lead to delays or even
inhibit reaching a high quality, final structure. Automated methods have
the potential to produce minimally biased models in an efficient manner.

Current software packages such as SOLVE (4), SHARP (5), ACrS (6)
SHELX-C/D/E (7) CRANK (8) Elves (9), Auto-Rickshaw (10) and BnP (11) are
capable of automatic structure solution using MAD, SAD, or other sources
of experimental phases. Molecular replacement can be carried out in an
automated fashion by software including Amore (12) Phaser (13), EPMR
(14), and MolRep (15). Model-building can be carried out automatically
by several algorithms including those in ARP/wARP (16), RESOLVE (17,
18), TEXTAL (19) and MAID (20). However, there still remain serious
computational bottlenecks in structure determination. Truly automated
structure solution is still limited to routine structures for which high
quality experimental data are available, typically at 2.5Å or better.

Manual model building programs such as O (21), XtalView (22), COOT (23)
and MAIN (24) have incorporated an increasing amount of tools that
automated complex tasks such as validation and model building. Current
shortcomings of automated algorithms are however unlikely to be overcome
by simply combining current software packages into automated
\"pipelines\". Rather, new algorithms must be developed and combined
with new approaches to decision-making. The PHENIX software (25, 26) has
been developed with the needs for automation and complex decision-making
in mind.

### 2. Architecture  {#architecture .unnumbered}

PHENIX builds upon Python, the Boost.Python Library, and C++ to provide
an environment for automation and scientific computing. Many of the
fundamental crystallographic building blocks, such as data objects and
tools for their manipulation are provided by the Computational
Crystallography Toolbox (*cctbx*; (27)). The computational tasks which
perform complex crystallographic calculations are then built on top of
this. Finally, there are a number of different user interface available
in order to use PHENIX. In order to facilitate automated operation there
is the Project Data Storage (PDS) that is used to store and track the
results of calculations.

### 3. User interfaces 

Different user interfaces are required depending on the needs of a
diverse user community. There are currently three different user
interfaces, each described below.

#### 3.1 Command line interface 

For a number of applications a command-line interface is most effective.
This is particularly the case when rapid results are required, such as
data quality assessment and twinning analysis, or substructure solution
at the synchrotron beam line. Tools that facilitate the ease of use at
the early stages of structure solution, such as data analyses
(phenix.xtriage), substructure solution (phenix.hyss) and reflection
file manipulations such as the generation of a test set, reindexing and
merging of data (iotbx.reflection_file_converter) are available via
simple command line interfaces. Another major application that is
controlled via the command line interface is phenix.refine.

To illustrate the command line interface, the command used to run the
program that carries out a data quality and twinning analyses is:

phenix.xtriage my_data.sca \[options\]

Further options can be given on the command line, or can be specified
via a parameter file:

phenix.xtriage my_parameters.def

A similar interface is used for macromolecular refinement:

phenix.refine my_model.pdb my_data.mtz

Although SCALEPACK and MTZ formats are indicated in the above example,
reflection file formats such as D\*TREK, CNS/XPLOR or SHELX can be used,
as the format is detected automatically.

Help for all command line applications can be obtained by use of the
flag \--help :

phenix.refine \--help

#### 3.2 Tasks and Strategies 

The PHENIX strategy interface provides a way to construct complex
networks of tasks to perform a higher-level function (Figure 1). For
example, the steps required to go from initial data to a first electron
density map in a SAD experiment can be broken down into well-defined
tasks (available from the task window), which can be reused in other
procedures. Instead of requiring the user to run these tasks in the
correct order they are connected together by the software developer, and
can thus be run in an automated way. However, because the connection
between tasks is dynamic they can be reconfigured or modified, and new
tasks introduced as necessary if problems occur. This provides the
flexibility of user input and control, while still permitting complete
automation when decision-making algorithms are incorporated into the
environment. The tasks and their connection into strategies rely on the
use of plain text task files written using the Python scripting
language. This enables the computational algorithms to be used easily in
a non-graphical environment. The PHENIX GUI permits strategies to be
visualized and manipulated. These manipulations include loading a
strategy distributed with PHENIX, customizing and saving it for future
recall.

Current tasks and strategies available include:

-   Density modification; Carries out a single run of resolve.

-   Substructure solution; Runs HySS.

-   Molecular replacement; Computes rotation and translation functions
    with PHASER.

-   Model building; Using TEXTAL or RESOLVE.

#### 3.3 Wizards 

The decision-making in strategies is local, with decisions being made at
the end of each task to determine the next route in the path. This is
analogous with how crystallographers typically make decisions during
structure solution; a program is run, the outputs manually inspected and
a decision made about the next step in the process. By contrast a wizard
provides a user interface that can make more global decisions, by
considering all of the available information at each step in the
process. Wizards are designed to lead the users through the process of
setting up a desired task, making automatic decisions when possible, but
prompting the user for additional information when necessary. The wizard
interface uses the same graphical environment as the strategies, but
consists of only a single input/output area (Figure 2).

Currently available wizards performs the following tasks:

-   Structure solution using experimental phasing approaches such as
    SAD/MAD and SIR

-   Structure solution via molecular replacement

-   Automated model building, structure completion and refinement of
    structures

-   Automated ligand building

### 4. Common crystallographic computations 

The following paragraphs are a brief description of a number of common
tasks that can be performed within the PHENIX framework.

#### 4.1. Automated structure solution using experimental phasing techniques 

Structure solution via SAD, MAD or SIR(AS) can be carried out with the
AutoSol wizard. The AutoSol wizard performs heavy atom location,
phasing, density modification and initial model building in an automated
manner.

The heavy atoms are located with substructure solution engine also used
in phenix.hyss (28), a dual space method similar to SHELXD (7) and Shake
and Bake (29). Phasing is carried out with PHASER for SAD cases and with
SOLVE for MAD and SIR(AS) cases. Subsequent density modification is
carried out with RESOLVE. The hand of the substructure is determined
automatically on the basis of the quality of the resulting electron
density map. It is noteworthy that the whole process is not necessarily
linear but that the wizard can decide to step back and (for instance)
try another set of heavy atoms if appropriate.

In the resulting electron density map, a structure is build. Further
model completion can be carried out via the AutoBuild wizard. The
Autobuild wizard iterates the model building and density modification
with refinement of the model in a scheme similar to ARP/wARP (16).

#### 4.2. Automated structure solution via molecular replacement 

Structure solution via molecular replacement is facilitated via the
AutoMR wizard. The AutoMR wizard guides the user through setting up all
necessary parameters to run a molecular replacement job by PHASER.

The molecular replacement carried out by PHASER uses likelihood based
scoring function (13, 30), improving the sensitivity of the procedure
and the ability to obtain reasonable solutions with search models that
have a relatively low sequence similarity to the crystal structure being
determined. Besides the use of likelihood based scoring functions,
structure solution is enhanced by an excellent bookkeeping of all search
possibilities when searching for more then a single copy in the
asymmetric unit or when there the choice of space group is ambiguous.

When a suitable molecular replacement solution is found, the AutoBuild
wizard is invoked and rebuilds the molecular replacement model given the
sequence of the model under investigation.

#### 4.3. Automated model building 

Automated model building given a starting model or a set of reasonable
phases can be carried out by the AutoBuild wizard. A typical AutoBuild
job combines density modification, model building, macromolecular
refinement and solvent model updates (\'water picking\') in an iterative
manner.

Various modes of building a model are available. Depending on the
availability of a molecular model, model building can be carried by
locally rebuilding an existing model (*rebuild in place*) or by building
in the density without any information of an available model. The
rebuilding in place model building is a powerful building scheme that is
used by default for molecular replacement models that have a high
sequence similarity to the sequence of the structure that is to be
built.

A fundamental feature of the AutoBuild wizard is that it builds various
models, all from slightly different starting points. The dependency of
the outcome of the model building algorithm on initial starting
conditions provide a straightforward manner to obtain a variety of
plausible molecular models. It is not uncommon that certain sections of
a map have been built in one model, while it is not in another.
Combining these models allows the AutoBuild wizard to converge faster to
a more complete model, than when using a single model building attempt
for a given set of phases.

Dedicated loop fitting algorithms are available that can close gaps
between chain segments. This feature, together with the water picking
and side chain placement, typically results in highly complete models of
a high quality that need a minimum of manual intervention before they
are ready for deposition.

#### 4.4. Refinement  {#refinement .unnumbered}

The refinement engine used in the AutoBuild and AutoSol wizards can be
run from the command line via the command phenix.refine. The
phenix.refine program carries out likelihood based refinement and has
the possibility to refine positional parameters, individual or grouped
atomic displacement parameters, individual or grouped occupancies.
Functionality for the refinement of anisotropic displacement parameters
(individual or via a TLS parameterization) is present as well.
Positional parameters can be optimized using either traditional
gradient-only based optimization methods, or via simulated annealing
protocols (31, 32).

The command line interface allows the user to specify which part of the
model should be refined in what manner. It is in principle possible to
refine half of the molecule as a rigid group with grouped B values,
whereas the other half of the molecule has a TLS parameterization. The
flexibility of specifying the level of parameterization of the model is
especially important for the refinement of low resolution data or when
starting with severely incomplete atomic models. Another advantage of
this flexibility in refinement strategy is that one could perform a
complex refinement protocol that carries out simulated annealing,
isotropic B refinement and water picking in \'one go\'.

Another main feature of phenix.refine is the way in which the relative
weights for the geometric and ADP restraints with respect to the X-ray
target are determined. Considerable effort has been put into devising a
good set of defaults and weight determination schemes that results in a
good choice of parameters for the data set under investigation. Defaults
can of course be overwritten if the user chooses to.

Besides being able to handle the refinement against X-ray data,
phenix.refine can refine against neutron data or against X-ray and
neutron data simultaneously.

#### 4.5. Ligands 

Automated fitting of ligands into the electron density is facilitated
via the LigandFit wizard. The ligand building is performed by finding an
initial fit for the largest rigid domain of the ligand and extends the
remaining part of the ligand from this initial \'seed\'. Besides being
able to fit a known ligand into a difference map, the LigandFit wizard
is capable to identify ligands on the basis of the difference density
only. In the latter scheme, density characteristics for ligands
occurring frequently in the PDB (33, 34) are used to provide the user
with a range of plausible ligands.

Stereo chemical dictionaries of ligands whose chemical description is
not available in the supplied monomer library (35) for the use in
restrained macromolecular refinement can be generated with the
*electronic ligand builder and optimization workbench* (eLBOW). eLBOW
generates a 3D geometry from a number of chemical input formats
including MOL2 or PDB files and SMILES strings (36). SMILES is a
compact, chemically dense description of a molecule that contains all
element and bonding information and optionally other stereo information
such as chirality. To generate a 3D geometry from an input format that
contains no 3D geometry information, eLBOW uses a Z-Matrix formalism in
conjunction with a table of bond lengths calculated using the
Hartree-Fock method with a 6-31G(d,p) basis set to obtain a cartesian
coordinate set. The geometry is then optionally optimized using the
semi-empirical quantum chemistry method AM1. The AM1 optimization
provides chemically meaningful and accurate geometries for the class of
molecule typically complexed with proteins. eLBOW outputs the optimized
geometry and a standard CIF restraint file that can be read in by
phenix.refine and can also be used for real space refinement during
manual model building sessions in the program *COOT* (23). An interface
is also available to use eLBOW within COOT.

#### 4.6. Twinned data 

The presence of twinning can severely delay structure solution, model
completion and refinement if not taken explicitly taken into account.
Detection of twinning on the basis of intensity statistics only is
facilitated via the program phenix.xtriage. This command line driven
program analyses an experimental data set and provides diagnostics that
aid in the detection of other common idiosyncrasies such as the presence
of pseudo translational symmetry or certain data processing problems.
Other sanity checks, such as an a Wilson plot sanity check (37) and an
algorithm that tries to detect the presence of ice rings from the merged
data are performed as well.

If twin laws are present for the given unit cell and space group, a
Britton plot (38) is computed, an H-test (39, 40) is performed and a
likelihood based method is used to provide an estimate of the twin
fraction. Twin laws are deduced from first principles for each data set,
avoiding the danger of over-looking twin laws by incomplete lookup
tables. If a model is available, more efficient twin detection tools are
available. The RvsR statistic (41) is particulary useful in the
detection of twinning in combination with pseudo rotational symmetry.
This statistic is computed by phenix.xtriage if calculated data is
supplied together with the observed data. A more direct test for the
presence of twinning is by refinement of the twin fraction given an
atomic model. The command line utility phenix.twin_map_utils provides a
straightforward way to refine a twin fraction given an atomic model and
a X-ray data set and also produces \'detwinned\' 2FO-FC and gradient
maps. The implementation of least-squares targets for refinement of
twinned data will be available in phenix.refine.

The routines in Xtriage can detect the presence of higher intensity
symmetry than specified by the space group of the data. If a higher
intensity symmetry is detected, the user is advised to consider
reprocessing the data.

### 5. Examples

### **A few examples will highlight some points mentioned in the previous sections. The results shown here have been obtained with PHENIX version 1.26b-d2 (December 2006).**

#### 5.1. Structure solution via S-SAD Phasing

An X-ray data set of insulin measured at a wavelength of 1.54 A, was
given to the AutoSol wizard for substructure solution and phasing. After
setting up how many sites to find, the Wizard finds 7 Sulfur sites, out
of which 3 are refined to a low occupancy. The sites are refined by the
Wizard with PHASER, for both choices of the hand. The quality of the
electron density is used to determine the correct hand. The solution
that produces the best map is used for further density modification and
model building.

Initial phasing of the sites produces a map with a mean figure of merit
equal to 0.38. The experimental phases are of such a quality, that large
aromatic side chains such as tyrosine can be recognized in the map
before any density modification is applied (Figure 3). The AutoSol
wizard failed to build 5 N-terminal residues in weak density. Subsequent
building of these residues in COOT and automated placement of waters and
additional refinement by phenix.refine resulted in R-values (work/free)
of 18%/20%.

#### 5.2. Molecular replacement  {#molecular-replacement .unnumbered}

To illustrate a typical structure solution with molecular replacement,
the structure of Epsin (42) was solved using the structure of 1XGW as a
search template. The X-ray data extend to 1.84 Å. A Matthews analyses
suggests 1 molecule per asymmetric unit with an approximate solvent
content of 44%.

The sequence identity of the search template is 54% over the length of
the alignment.

The rotation function reveals two significant peaks with Z-scores of 5.7
and 6.2. A subsequent translational search results two solutions with
Z-scores of 10 and 13. After refinement a single unique solution is
available. This solution can be used as a starting point in automated
model building.

The space group of this particular data set is P3121. The AutoMR wizard
can be instructed to search in space groups with the same point groups
as well. in this particular case, the translation function only gives a
statisfactory result in space group P3~1~21. Possible solutions in
P3~2~21 and in P321 have low Z-scores and multiple clashes with symmetry
related copies.

#### 5.3. Ligand Building 

The ligand building capabilities of the LigandFit wizard are illustrated
by fitting NADH and cholic acid into the structure of SS_LADH (43). The
X-ray data extends to 1.54 A, and the difference density is rather
clear.

Atomic models for NADH and Cholic acid were constructed from smiles
strings using eLBOW. The command used to get the Cholic acid model is

elbow.builder
\--smiles="CC(CCC(O)=O)C1CCC2C3C(O)CC4CC(O)CCC4(C)C3CC(O)C12C" \--opt

SMILES strings can be constructed with molecular editors such as JME
(http://www.molinspiration.com/jme/) or can be obtained directly from
MSDChem (44).

The automated ligand building procedure uses a protein model (without
ligands) and the X-ray data to compute a difference map in which the
ligand is built. Two copies of NADH and two copies of Cholic acid were
built. The quality of the model is shown in figure 4.

#### 5.4. Refinement 

A typical refinement with phenix.refine is initiated with the following
command:

phenix.refine my_data.mtz my_model.pdb

The refinement program will try to determine which columns in the mtz
file to use for refinement and which column contains the test flags for
cross validation purposes.

An example of the application of TLS refinement and its effects on the
R-values is illustrated by refining the structure of synaptotagmin
(45).The available X-ray data extend to 3.2 Å and is over 97% complete.
Standard refinement (positional parameters and individual atomic
displacement parameters (ADPs)) results in R-values of 24.6% and 27.7%
for the work and test set respectively. At this resolution, ADPs are
often refined in groups by applying constraints the values ADPs in
selected atoms within a residue. The refinement of ADPs in this manner,
resulted in R-values of 24.7% and 28.9% for the work and test set
respectively. The application of a TLS model to the atomic displacement
parameters that models the displacement of rigid groups within a crystal
reduces the R-values to 22.7% and 25.9% for the work and test
respectively. An ortep diagram of the anisotropic ADPs is shown in
Figure 5.

If only a TLS parameterization is used to model the ADPs, local
variations in ADPs due to increased or decrease flexibility are
effectively ignored. A more complete ADP model includes both a TLS and
individual parameterization. The refinement both the TLS parameters and
the individual ADPs result in R-values of 20.7% and 24.4% for the work
and test respectively.

The command that is needed to perform this last refinement is relatively
straightforward:

phenix.refine scale.hkl synaptotagmin.pdb tls.param

Note that besides the experimental data and the atomic model and extra
parameter file is specified. This parameter file has the following
content:

refinement.refine {

strategy = \*individual_sites \*individual_adp \*tls

adp {

tls = \"(chain A and resid :421)\"

tls = \"(chain A and resid 422:430)\"

tls = \"(chain A and resid 431:)\"

}

}

The line containing the keyword *strategy* specifies that the postional
parameters for individual sites should be refined, as well as a TLS
model and individual ADPs. The TLS groups are defined by the *adp*
scope. In this case, 3 TLS domains are specified within chain A.

### 5.5. Twinning

The deposited X-ray dataset of PDBID 1GH7 was analyzed by phenix.xtriage
for the presence of twinning. A single twin law was found (-h-k, k ,-l).
Analyses of the intensity statistics indicates that that data is
twinned:

Statistics independent of twin laws

\- \<I\^2\>/\<I\>\^2 : 1.795

\- \<F\>\^2/\<F\^2\> : 0.843

\- \<\|E\^2-1\|\> : 0.658

\- \<\|L\|\>, \<L\^2\>: 0.396, 0.219

Multivariate Z score L-test: 8.104

The multivariate Z score is a quality measure of the given

spread in intensities. Good to reasonable data is expected

to have a Z score lower than 3.5.

Large values can indicate twinning, but small values do not

necessarily exclude it.

The results of the L-test indicate that the intensity statistics

are significantly different then is expected from good to reasonable,

untwinned data.

As there are twin laws possible given the crystal symmetry, twinning
could

be the reason for the departure of the intensity statistics from
normality.

It might be worthwhile carrying out refinement with a twin specific
target function.

An H-test (39, 40) and Britton analyses (38) indicate a twin fraction of
approximately 7%.

Refinement of the twin fraction and bulk solvent and scaling parameters
reveals that the data is 16% twinned, a fact overseen during the
original structure solution (46).

### 6. Notes

*The effect of the data quality on the ability to solve the
substructure*

The quality of the anomalous signal has a large impact on the ability to
solve the substructure.

The AutoSol wizard analyses the anomalous signal in a dataset by
computing either a correlation coefficient between the anomalous
differences or a signal to noise ratio for SAD data. On the basis of
these statistics, resolution limits for substructure solution are
chosen.

The quality of the anomalous data can be checked manually with
iotbx.reflection_statistics. It computes correlation coefficients
between anomalous differences and a statistic known as the measurability
(47) for SAD data sets. Correlation coefficients larger then 30%
indicate significant anomalous signal in a MAD data set. For SAD
datasets, measurabilities larger than 6% indicate the presence of
significant anomalous signal.

Although the AutoSol wizard analyses the signal to noise level of the
anomalous data and makes appropriate resolution cut offs, it can be
worthwhile running phenix.hyss with various resolution cutoff if the
AutoSol wizard fails to find the substructure.

*Difficult molecular replacement*

Not all structures can be solved by molecular replacement. Certain
strategies can however be adopted to push its capabilities to the
boundaries of what is possible. Careful editing of the input model by
removing non conserved, flexible loops can make a big difference.
Breaking a flexible model down into multiple rigid domains that can be
used in a multi-copy search can be a vital ingredient for a successful
structure solution. Other suggestions are available from the program
documentation.

*Bias removal in molecular replacement maps*

The presence of bias in molecular replacement phases can make the
interpretation of the electron density difficult. This bias can be
removed by computing a *Full Omit* map in the AutoBuild wizard. The Full
Omit procedure is reminiscent of the composite omit maps of CNS (48).

*Definition of NCS restraints in refinement*

In order to increase the data to parameter ratio during refinement,
multiple copies of the protein within the asymmetric unit can be
restrained to have a similar confirmation. These so called NCS
restraints can be set up automatically by phenix.refine.

*Interpreting the result of a TLS refinement*

By default, the ADPs written by phenix.refine are the total ADPs rather
than the so called residual ADPs, which can be negative.

*Weight optimization in restrained macromolecular refinement*

The weight that determines the relative contribution of the X-ray target
with respect to the restraint terms is determined automatically. The
procedure works well in most cases, but a manual optimization of this
weight can be required. Changing the weight manually can be done via the
following command:

phenix.refine my_data.mtz my_model.pdb wxc_scale=5

Rerunning the refinement job with various values for the weight
*wxc_scale* and a careful monitoring of the Free R value will give an
indication of a suitable value for the weight.

### 7. Availability

PHENIX can be downloaded from http://www.phenix.online.org, and is
freely available for academics. The open source crystallographic
libraries (the CCTBX) is available from http://cctbx.sf.net.

The development of PHENIX is funded by XXXXX ???? XXXXX

### 8. References

1\. Page, R., Grzechnik, S. K., Canaves, J. M., Spraggon, G., Kreusch,
A., Kuhn, P., Stevens, R. C., and Lesley, S. A. (2003) *Acta
Crystallographica Section D* **59,** 1028-37.

2\. Snell, G., Cork, C., Nordmeyer, R., Cornell, E., Meigs, G., Yegian,
D., Jaklevic, J., Jin, J., Stevens, R. C., and Earnest, T. (2004)
*Structure* **12,** 537-45.

3\. Adams, P. D., Grosse-Kunstleve, R.W., and Brunger, A.T. (2003)
*Methods Biochem. Anal.* **44,** 75-87.

4\. Terwilliger, T. C., and Berendzen, J. (1999) *Acta Crystallographica
Section D* **55,** 849-61.

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![](media/image1.png){width="5.998611111111111in"
height="4.8902777777777775in"}

Figure 1: Example of the PHENIX strategy interface, showing a
substructure and phasing strategy for MAD data.

![](media/image2.png){width="5.992361111111111in"
height="5.072916666666667in"}

Figure 2: Example of the PHENIX wizard interface, with the model
building part of the AutoMR wizard shown.

![](media/image3.png){width="5.996527777777778in"
height="4.298611111111111in"}

Figure 3a: Experimental S-SAD phases from PHASER before any density
modification. The density is of a quality that it can be readily
interpreted.

![](media/image4.png){width="5.996527777777778in"
height="4.298611111111111in"}

Figure 3b: The electron density map corresponding to the refined model.

![](media/image5.png){width="5.996527777777778in"
height="5.497916666666667in"}

Figure 4: The difference density and the automatically build model of
NADH.

![](media/image6.png){width="3.7222222222222223in"
height="5.126388888888889in"}

Figure 5: An ORTEP diagram with anisotropic displacement parameters
indicating the mobility of certain parts of the Synaptotagmin molecule.