First Aid & Pathology Data quality assessment in PHENIX

1. First Aid & PathologyData 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, 2104-2113. • Bwil : 50 Å2; dmin: 2Å • Bwil : 9 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. Data is from DNA structure Likelihood based Wilson Scaling • Resolution dependent problems can be easily/automatically spotted • Ice rings • Empirical Wilson plots available for protein and DNA/RNA.

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. F(Qmax) Relative peak height Qmax Pseudo Translational Symmetry • 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.

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

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

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 • 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: 99.5 60.9 70.96 90 134.5 90 • 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 • Data does not appear to be twinned

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 • http://www.phenix-online.org/cctbx • 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, 83-95. • 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. Measurability 1/resolution2 Anomalous data 6 (partially occupied) Iodines in thaumatin at l=1.5Å. Raw SAD phases, straight after PHASER A A B B

27. Measurability 1/resolution2 Anomalous data 6 (partially occupied) Iodines in thaumatin at l=1.5Å. Density modified phases A 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 – 1524. Lucas van Leyden

34. Pathology/Autopsy Analyses at home The anatomical lesson of dr. Nicolaes Tulp - 1632. Rembrandt van Rijn.

35. Funding: • LBNL (DE-AC03-76SF00098) • NIH/NIGMS (P01GM063210) • PHENIX Industrial Consortium Ackowledgements 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

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