1. First Aid & Pathology Data quality assessment in PHENIX
Peter Zwart

2. Introduction
Structure solution can be enhanced by the knowledge of the quality and idiosyncrasies of the merged data
Anomalous signal?
Twinning
Pseudo centering
Data characterization should extend beyond standard quantities as Rmerge and nominal resolution
A full characterization of a data set might provide expert systems, such as wizards, useful information on how to most optimally solve a structure

3. Introduction
Xtriage is a program that aims to characterize a merged X-ray dataset
Probabilistic unit cell content analyses
Likelihood based Wilson scaling
Analyses of mean intensity
Ice ring detection
Outlier analyses
Twinning / pseudo centering
Anomalous signal

4. Likelihood based Wilson Scaling
Both Wilson B and nominal resolution determine the ‘looks’ of the map
Zwart & Lamzin (2003). Acta Cryst. D50,
Bwil : 9 Å2; dmin: 2Å
Bwil : 50 Å2; dmin: 2Å

5. Likelihood based Wilson Scaling
Data can be anisotropic
Traditional ‘straight-line fitting’ not reliable at low resolution
Solution: Likelihood based Wilson scaling
Results in estimate of anisotropic overall B value.
Zwart, Grosse-Kunstleve & Adams, CCP4 newletter, 2005.

6. Likelihood based Wilson Scaling
Likelihood based scaling not extremely sensitive to resolution cut-off, whereas classic straight line fitting is.

7. Likelihood based Wilson Scaling
Anisotropy is easily detected and can be ‘corrected’ for.
Useful for molecular replacement and possibly for substructure solution
Anisotropy correction cleans up your N(Z) plots

8. Likelihood based Wilson Scaling
For the ML Wilson scaling an ‘expected Wilson plot’ is needed
Obtained from over 2000 high quality experimental datasets
‘Expected intensity’ and its standard deviation can be obtained

9. Likelihood based Wilson Scaling
Resolution dependent problems can be easily/automatically spotted
Ice rings
Empirical Wilson plots available for protein and DNA/RNA.
Data is from DNA structure

10. Outlier analyses
Assume amplitudes are distributed according to Wilson distribution
For a dataset of a given size, the cumulative distribution function of the largest |E| values in the dataset can be used to detect outliers

11. Pseudo Translational Symmetry
Can cause problems in refinement and MR
Incorrect likelihood function due to effects of extra translational symmetry on intensity
Can be helpful during MR
Effective ASU is smaller is T-NCS info is used.
The presence of pseudo centering can be detected from an analyses of the Patterson map.
A Fobs Patterson with truncated resolution should reveal a significant off-origin peak.

12. Pseudo Translational Symmetry
Relative peak height Qmax
F(Qmax)
A database analyses reveal that the height of the largest off-origin peaks in truncated X-ray data set are distributed according to:

13. Pseudo Translational Symmetry
1-F(Qmax): The probability that the largest off origin peak in your Patterson map is not due to translational NCS; This is a so-called p value
If a significance level of 0.01 is set, all off origin Patterson vectors larger than 20% of the height of the origin are suspected T-NCS vectors.
PDBID
Height (%)
P-value
(%)
1sct 77
9*10-6
1ihr 45
1*10-3
1c8u 20 1
1ee2 10 5

14. Twinning
Merohedral twinning can occur when the lattice has a higher symmetry than the intensities.
When twinning does occur, the recorded intensities are the sum of two independent intensities.
Normal Wilson statistics break down
Detect twinning using intensity statistics

15. Twinning
Cumulative intensity distribution can be used to identify twinning
(acentric data)
Pseudo centering
Normal
Perfect twin Z
N(Z)

16. Twinning Pseudo centering + twinning = N(Z) looks normal
Anisotropy in diffraction data produces similar trend to Pseudo centering
Anisotropy can however be removed
How to detect twinning in presence of T-NCS?
Partition miller indices on basis of detected T-NCS vectors
Intensities of subgroups follow normal Wilson statistics (approximately)
Use L-test for twin detection
Not very sensitive to T-NCS if partitioning of miller indices is done properly.
No need to know twin laws: not sensitive to pseudo symmetry or certain data processing problems.

17. Twinning - + 2 - + 2
+; /N
<L>

18. Twinning
A data base analyses on high quality, untwinned datasets reveals that the values of the first and second moment of L follow a narrow distribution
This distribution can be used to determine a multivariate Z-score
Large values indicate twinning

19. Twinning Determination of twin laws Determination of twin fraction
From first principles
No twin law will be overlooked
PDB analyses: 36% of structures has at least 1 possible twin law
50.9% merohedral; 48.2% pseudo merohedral;0.9% both
27% of cases with twin laws has intensity statistics that warrant further investigation on whether or not the data is twinned
10% of whole PDB(!)
Determination of twin fraction
Fully automated Britton and H analyses as well as ML estimate of twin fraction of basis of L statistic.

20. Conflicting information
PDBID: 1???
Unit cell:
Space group : C 2
Twin laws and estimated twin fractions:
H,-K,-H-L : 0.44
H+2L,-K,-L : 0.01
-H-2L, K, H+L : 0.01
<I2>/<I>2 = 2.10 (theory for untwinned data : 2.0);
Data does not appear to be twinned
<L> = 0.49 (theory for untwinned data : 0.5);
Multivariate Z-score of L test: 0.963

21. Conflicting information
What is going on?
Estimated twin fraction is large, but data does not seem to be twinned:
Twin law H,-K,-H-L is parallel to an existing NCS axis or
Twin law H,-K,-H-L is a symmetry axis, and the space group is too low
It should be : C2 + H,-K,-H-L = F222
Need images to make decision

22. Conflicting information
A DNA example:
Space group: P65;
1 twin law
Resolution: 1.87A
Native Patterson analyses indicates several significant off-origin peaks
Intensity statistics indicate pseudo translation symmetry:
<I^2>/<I>^2 :4.243
N(Z) plot not very informative

23. Conflicting information
However
L test: <L>=0.46;
Data might be twinned.
Partitioned data might not follow Wilson statistics however.
Britton and H analyses estimate of twin fraction is about 40%
Wrong spacegroup?
Monomer would not fit in ASU
Twinning, pseudo symmetry, or both?
Not clear from experimental data only, use deposited coordinates
Rwork=28%; Rfree=34%
Twin fractions via Britton plot
From Fcalc: 11% (due to pseudo symmetry only)
From Fobs: 41% (pseudo symmetry + twinning)
See Lebedev, Vagin, Murshudov (2006) Acta D62,
Data likely to be twinned.
Difficult to spot due to TPS and RPS effects on intensity statistics

24. Anomalous data
Structure solution via experimental methods (especially SAD) is on the rise.
Presence of anomalous signal indicated by a quantity called Measurability:
Fraction of Bijvoet differences for which
DI/sDI>3 and (I+/sI(+) and I(-)/sI(-) > 3)
Easy to interpret
At 3 Angstrom 6% of Bijvoet pairs are significantly larger than zero

25. Anomalous data Measurability and <DI/sDI> are closely related
Measurability more directly translates to the number of ‘useful’ Bijvoet differences in substructure solution/phasing

26. Anomalous data 6 (partially occupied) Iodines in thaumatin at l=1.5Å.
Raw SAD phases, straight after PHASER A
Measurability
1/resolution2 A B B

27. Anomalous data 6 (partially occupied) Iodines in thaumatin at l=1.5Å.
Density modified phases A
Measurability
1/resolution2 A B B

28. Anomalous data SAD phasing with PHASER
Very sensitive residual maps
Residual map indicates where a certain type of anomalous scatterers need to be placed to improve fit between observed and expected F(+) and F(-)
Lysozyme soaked with solution containing (NH4)2(OsCl6)
Wilson B: 13.7; dmin=1.7
Data collected at Os L-III edge (f”>10)
Measurability at 3.0 is 67%
Anomalous signal is strong
Partial structure is large
Zheavy2/(Zheavy2+Zprotein2)=35%
PHASER residual map indicating location of main chain atoms

29. Anomalous data SAD phasing with PHASER
Very sensitive residual maps
Residual map indicates where a certain type of anomalous scatterers need to be placed to improve fit between observed and expected F(+) and F(-)
Lysozyme soaked with solution containing (NH4)2(OsCl6)
Wilson B: 13.7; dmin=1.7
Data collected at Os L-III edge (f”>10)
Measurability at 3.0 is 67%
Anomalous signal is strong
Partial structure is large
Zheavy2/(Zheavy2+Zprotein2)=35%
Raw PHASER SAD phases

30. Anomalous data Another extreme 2 Fe4S4 clusters in 60 residues
Wilson B: 6.5Å2; dmin=1.2Å
Measurability at 3.0Å: 6%
Data not terribly strong
ZFe2/(ZFe2+ZS2+Zprotein2)=17%
Fe f ”=1.25 e; S f ”=0.35 e
PHASER residual map from Fe SAD phases clearly show S positions
SAD on Fe, residual maps indicate S positions (green balls)

31. Anomalous data Inclusion of Sulfurs improves phasing
(ZFe2+ZS2)/(ZFe2+ZS2+Zprotein2)=32%
<FOM>=0.67 (was 0.53)
Residual maps show almost all non-hydrogen atoms
Inclusion of non hydrogen atoms results in <FOM>=0.98.
SAD on Fe, S. Residual maps (purple) and FOM weighted Fobs map (blue).

32. Discussion & Conclusions
Software tools are available to point out specific problems
mmtbx.xtriage <input_reflection_file> [params]
Log file are not just numbers, but also contains an extensive interpretation of the statistics
Knowing the idiosyncrasies of your X-ray data might avoid falling in certain pitfalls.
Undetected twinning for instance

33. First Aid Analyses at the beamline
If problem are detected while at the beam line, possible problems could be solved by recollecting data or adapting the data collection strategy.
The Surgeon and the Peasant – Lucas van Leyden

34. Pathology/Autopsy Analyses at home
The anatomical lesson of dr. Nicolaes Tulp Rembrandt van Rijn.

35. Ackowledgements Paul Adams Ralf Grosse-Kunstleve Pavel Afonine
Cambridge
Randy Read
Airlie McCoy
Laurent Storoni
Los Alamos
Tom Terwilliger
Li Wei Hung
Thirumugan Rhadakanan
Texas A&M Univeristy
Jim Sacchettini
Tom Ioerger
Eric McKee
Paul Adams
Ralf Grosse-Kunstleve
Pavel Afonine
Nigel Moriarty
Nick Sauter
Michael Hohn
Funding:
LBNL (DE-AC03-76SF00098)
NIH/NIGMS (P01GM063210)
PHENIX Industrial Consortium

36. W W W Phenix Xtriage tutorials CCTBX www.phenix-online.org
CCTBX
cctbx.sf.net