1. Computational Crystallography InitiativePhysical Biosciences Division First Aid & Pathology Data quality assessment in PHENIX Peter Zwart

2. Computational Crystallography InitiativePhysical Biosciences Division Introduction PHENIX: –Software for bio-molecular crystallography Molecular replacement (PHASER) Substructure solution (SOLVE, HYSS) Phasing (SOLVE; PHASER) Model building (RESOLVE) Refinement (phenix.refine) Ligand building (ELBOW and RESOLVE) http://www.phenix-online.org

3. Computational Crystallography InitiativePhysical Biosciences Division Introduction GUI shapshots

4. Computational Crystallography InitiativePhysical Biosciences Division Introduction Structure solution can be enhanced by the knowledge of the quality of the merged data –Presence of absence of anomalous signal –Completeness –Twinning –Anisotropy –Pseudo centering –… Adapt data solution/refinement strategy or even recollect data

5. Computational Crystallography InitiativePhysical Biosciences Division Likelihood based Wilson Scaling Both Wilson B and nominal resolution determine the ‘looks’ of the map Zwart & Lamzin (2003). Acta Cryst. D50, 2104-2113. B wil : 50 Å 2 ; d min : 2Å B wil : 9 Å 2 ; d min : 2Å

6. Computational Crystallography InitiativePhysical Biosciences Division Likelihood based Wilson Scaling Data can be anisotropic Traditional ‘straight line fitting’ not reliable at low resolution Solution: Likelihood based Wilson scaling –Similar to maximum likelihood refinement, but with absence of knowledge of positional parameters –Results in estimate of anisotropic overall B value. Zwart, Grosse-Kunstleve & Adams, CCP4 newletter, 2005.

7. Computational Crystallography InitiativePhysical Biosciences Division Likelihood based Wilson Scaling Likelihood based scaling not extremely sensitive to resolution cut-off, whereas classic straight line fitting is.

8. Computational Crystallography InitiativePhysical Biosciences Division 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

9. Computational Crystallography InitiativePhysical Biosciences Division Likelihood based Wilson Scaling Useful by products –For the ML Wilson scaling an ‘expected Wilson plot’ is needed Using correction term formalism Zwart & Lamzin (2004) Acta Cryst D60, 220-226. –Obtained from over 2000 high quality experimental datasets –‘Expected intensity’ and its standard deviation obtained

10. Computational Crystallography InitiativePhysical Biosciences Division Likelihood based Wilson Scaling Resolution dependent problems can be easily/automatical ly spotted –Ice rings Empirical Wilson plots available for protein and DNA/RNA. Data is from DNA structure

11. Computational Crystallography InitiativePhysical Biosciences Division Pseudo Translational Symmetry Can cause problems in refinement and MR –Incorrect likelihood function due to effects of extra translational symmetry on intensity Can cause problems or 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 F obs Patterson with truncated resolution should reveal a significant off-origin peak.

12. Computational Crystallography InitiativePhysical Biosciences Division 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: Relative peak height Q max F(Q max )

13. Computational Crystallography InitiativePhysical Biosciences Division Pseudo Translational Symmetry 1-F(Q max ): 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. PDBIDHeight (%) P-value (%) 1sct779*10 -6 1ihr451*10 -3 1c8u201 1ee2105

14. Computational Crystallography InitiativePhysical Biosciences Division 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. Computational Crystallography InitiativePhysical Biosciences Division Twinning Cumulative intensity distribution can be used to identify twinning (acentric data) Pseudo centering Normal Perfect twin Z N(Z)

16. Computational Crystallography InitiativePhysical Biosciences Division 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)

17. Computational Crystallography InitiativePhysical Biosciences Division Twinning - + 2 - + 2 +; /N

18. Computational Crystallography InitiativePhysical Biosciences Division Twinning A data base analyses on highly 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. Computational Crystallography InitiativePhysical Biosciences Division 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 is suspected to be 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. Computational Crystallography InitiativePhysical Biosciences Division Twinning Conflicting information –Twin law is present lattice has higher symmetry than assumed symmetry of intensities –Estimated twin fraction is close to 0.5 ‘twin’ related intensities are very similar – test does not indicate twinning Very strong NCS Space group too low

21. Computational Crystallography InitiativePhysical Biosciences Division Twinning Maybe an example of a too low symmetry?

22. Computational Crystallography InitiativePhysical Biosciences Division Anomalous data Structure solution via experimental methods (especially SAD) is on the rise. How to identify the presence of anomalous signal? – ; VERY sensitive to noise – ; 2? –Measurability Fraction of Bijvoet differences for which –  I/   I >3 and (I + /  I(+) and I(-)/  I(-) > 3) Easy to interpret –At 3 Angstrom 6% of Bijvoet pairs are significantly larger than zero

23. Computational Crystallography InitiativePhysical Biosciences Division Anomalous data Measurability and are closely related of course Measurability more directly translates to the number of ‘useful’ Bijvoet differences in substructure solution/phasing

24. Computational Crystallography InitiativePhysical Biosciences Division Anomalous data The quality of the data determines the success of structure solution Redundancy SnB success rate Weiss, (2000). J. App. Cryst, 34, 130-135. Measurability Obtained via numerical methods

25. Computational Crystallography InitiativePhysical Biosciences Division Anomalous data The quality of the data determines the success of structure solution Measurability 1/resolution 2 6 (partially occupied) Iodines in thaumatin at =1.5Å. Raw SAD phases, straight after PHASER A B

26. Computational Crystallography InitiativePhysical Biosciences Division Anomalous data The quality of the data determines the success of structure solution Measurability 1/resolution 2 6 (partially occupied) Iodines in thaumatin at =1.5Å. Density modified phases A B

27. Computational Crystallography InitiativePhysical Biosciences Division Anomalous data LysOs PHASER maps Ferrodoxin PHASER maps

28. Computational Crystallography InitiativePhysical Biosciences Division Discussion & Conclusions Software tools are available to point out specific problems –mmtbx.xtriage [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

29. Computational Crystallography InitiativePhysical Biosciences Division Discussion & Conclusions mmtbx.xtriage at the beamline If problem are detected while at the beamline, possible problems could be solved by recollecting data or adpating the data collection strategy. The Surgeon and the Peasant – 1524. Lucas van Leyden

30. Computational Crystallography InitiativePhysical Biosciences Division Discussion & Conclusions mmtbx.xtriage at home The anatomical lesson of dr. Nicolaes Tulp - 1632. Rembrandt van Rijn.

31. Computational Crystallography InitiativePhysical Biosciences Division Ackowledgements Paul Adams Ralf Grosse-Kunstleve Pavel Afonine Nigel Moriarty Nick Sauter Michael Hohn Cambridge Randy Read Airlie McCoy Laurent Storonoy Los Alamos Tom Terwilliger Li Wei Hung Thirumugan Rhadakanan Texas A&M Univeristy Jim Sachetini Tom Ioerger Eric McKee