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First Aid & Pathology Data quality assessment in PHENIX

First Aid & Pathology Data quality assessment in PHENIX. Peter Zwart. Introduction. PHENIX: Software for bio-molecular crystallography Molecular replacement ( PHASER ) Substructure solution ( SOLVE , HYSS ) Phasing ( SOLVE ; PHASER ) Model building ( RESOLVE; TEXTAL )

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First Aid & Pathology Data quality assessment in PHENIX

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  1. First Aid & PathologyData quality assessment in PHENIX Peter Zwart

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

  3. Introduction • Command line tools • Convert reflection files to and from any format • Get basic statistics of reflection file • Solve your substructure • Compare substructures (site comparison) • Characterize you data set • etc etc etc • With a bit more effort and some knowledge of python • Find Harker sections for a given space group • Explore symmetry a la SGINFO • Compute structure factors • Write your own direct methods program

  4. Introduction • PHENIX Strategy • Each box represent a simple task • read in reflection file • Read in molecular replacement model • Do rotation search • Do translation search • Show solution • This allows users to quickly build there own interface for specific strategies

  5. Introduction • PHENIX Wizard • ‘Grey box’ • Ask question you are supposed to answer • Needs only most basic information and figures things out itself afterwards • Most control parameters can be set by user though • Customizability of underlying parameters for those who want/need it

  6. Introduction • Structure solution can be enhanced by the knowledge of the quality of the merged data • Presence of absence of anomalous signal • SAD/MAD or MR? • Resolution dependent completeness • Shall I recollect my low resolution? • Twinning • Which refinement target to use? • Anisotropy • Some regions of reciprocal space might be weak • Pseudo centering • Can I use this in Molecular Replacement; Do I expect possible problems in refinement? • Wilson plot • Is this protein?

  7. Introduction • To answer these kind of question, data sets need to be characterized beyond the standard quantities as Rmerge and nominal resolution • The to-be-presented characterization is implemented in the program mmtbx.xtriage of the PHENIX suite (version > 1.2) • Easy to use command-line driven program

  8. 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

  9. 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.

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

  11. 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

  12. 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 CrystD60, 220-226. • Obtained from over 2000 high quality experimental datasets • ‘Expected intensity’ and its standard deviation obtained

  13. Data is from DNA structure Likelihood based Wilson Scaling • Resolution dependent problems can be easily/automatically spotted Morris et al., (2004). J. Synch. Rad. 11, 56-59. • Ice rings • Missing overloads • Empirical Wilson plots available for protein and DNA/RNA.

  14. 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.

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

  16. 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.

  17. 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

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

  19. 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

  20. - 2 + - 2 + +; /N <L> Twinning

  21. 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

  22. Twinning • Determination of twin laws • From first principles Grosse-Kunstleve et al., 2005. IUCr Computing Commision newletter. • No (pseudo)merohedral 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.

  23. 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

  24. 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 • Twin law H,-K,-H-L is a symmetry axis, and the space group is too low • It should be F222 (C2 + H,-K,-H-L = F222) • Need images to make decision

  25. Anomalous data • Structure solution via experimental methods (especially SAD) is on the rise. • How to identify the presence of anomalous signal? • <DI/I> ; <DF/F> • VERY sensitive to noise • <DI/sDI>; <DF/sDF> • 2? • 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

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

  27. <FOM> SnB success rate Measurability Redundancy Weiss, (2000). J. App. Cryst, 34, 130-135. Anomalous data • The quality of the data determines the success of structure solution Obtained via numerical methods

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

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

  30. Anomalous data • SAD phasing with PHASER • Very sensitive residual maps • 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

  31. Anomalous data • SAD phasing with PHASER • Very sensitive residual maps • Lysozyme soaked with solution containing (NH4)2(OsCl6) • Wilson B: 13.7Å2; dmin=1.7Å • Data collected at Os L-III edge (f”>10) • Measurability at 3.0Å is 67% • Anomalous signal is strong • Partial structure is large • Zheavy2/(Zheavy2+Zprotein2)=35% Raw PHASER SAD phases

  32. 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)

  33. 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).

  34. 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

  35. 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

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

  37. Ackowledgements 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 Paul Adams Ralf Grosse-Kunstleve Pavel Afonine Nigel Moriarty Nick Sauter Michael Hohn

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