First Aid Pathology Data quality assessment in PHENIX

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First Aid & Pathology Data quality assessment in PHENIX Peter Zwart Computational Crystallography Initiative

First Aid & Pathology Data quality assessment in PHENIX Peter Zwart Computational Crystallography Initiative Physical Biosciences Division

Introduction • PHENIX: – Software for bio-molecular crystallography • • • Molecular replacement (PHASER)

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 Computational Crystallography Initiative Physical Biosciences Division

Introduction • Command line tools – – – Convert reflection files to and from

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 – 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 Computational Crystallography Initiative Physical Biosciences Division

Introduction • PHENIX Strategy – Each box represent a simple task • read in

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 Computational Crystallography Initiative Physical Biosciences Division

Introduction • PHENIX Wizard – ‘Grey box’ • Ask question you are supposed to

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 Computational Crystallography Initiative Physical Biosciences Division

Introduction • Structure solution can be enhanced by the knowledge of the quality of

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? Computational Crystallography Initiative Physical Biosciences Division

Introduction • To answer these kind of question, data sets need to be characterized

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 Computational Crystallography Initiative Physical Biosciences Division

Likelihood based Wilson Scaling • Both Wilson B and nominal resolution determine the ‘looks’

Likelihood based Wilson Scaling • Both Wilson B and nominal resolution determine the ‘looks’ of the map Zwart & Lamzin (2003). Acta Cryst. D 50, 2104 -2113. Bwil : 9 Å2; dmin: 2Å Computational Crystallography Initiative Bwil : 50 Å2; dmin: 2Å Physical Biosciences Division

Likelihood based Wilson Scaling • Data can be anisotropic • Traditional ‘straight line fitting’

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, CCP 4 newletter, 2005. Computational Crystallography Initiative Physical Biosciences Division

Likelihood based Wilson Scaling • Likelihood based scaling not extremely sensitive to resolution cut-off,

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

Likelihood based Wilson Scaling • Anisotropy is easily detected and can be ‘corrected’ for.

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 Computational Crystallography Initiative Physical Biosciences Division

Likelihood based Wilson Scaling • Useful by products – For the ML Wilson scaling

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 D 60, 220 -226. – Obtained from over 2000 high quality experimental datasets – ‘Expected intensity’ and its standard deviation obtained Computational Crystallography Initiative Physical Biosciences Division

Likelihood based Wilson Scaling • Resolution dependent problems can be easily/automatically spotted Data is

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

Pseudo Translational Symmetry • Can cause problems in refinement and MR – Incorrect likelihood

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. Computational Crystallography Initiative Physical Biosciences Division

 • A database analyses reveal that the height of the largest offorigin peaks

• A database analyses reveal that the height of the largest offorigin peaks in Patterson functions of truncated X-ray data set are distributed according to: Computational Crystallography Initiative F(Qmax) Pseudo Translational Symmetry Relative peak height Qmax Physical Biosciences Division

Pseudo Translational Symmetry • 1 -F(Qmax): The probability that the largest off origin peak

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. Computational Crystallography Initiative PDBID Height P-value (%) 1 sct 77 9*10 -6 1 ihr 45 1*10 -3 1 c 8 u 20 1 1 ee 2 10 5 Physical Biosciences Division

Twinning • Merohedral twinning can occur when the lattice has a higher symmetry than

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 Computational Crystallography Initiative Physical Biosciences Division

 • Cumulative intensity distribution can be used to identify twinning (acentric data) Pseudo

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

Twinning Pseudo centering + twinning = N(Z) looks normal • Anisotropy in diffraction data

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 Computational Crystallography Initiative Physical Biosciences Division

Twinning - 2 + + +; /N Computational Crystallography Initiative 2 <L> Physical Biosciences

Twinning - 2 + + +; /N Computational Crystallography Initiative 2 <L> Physical Biosciences Division

Twinning • A data base analyses on highly quality, untwinned datasets reveals that the

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 Computational Crystallography Initiative Physical Biosciences Division

Twinning • Determination of twin laws – From first principles Grosse-Kunstleve et al. ,

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. Computational Crystallography Initiative Physical Biosciences Division

Conflicting information • PDBID: 1? ? ? – Unit cell: 99. 5 60. 9

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+2 L, -K, -L : 0. 01 • -H-2 L, K, H+L : 0. 01 – <I 2>/<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 Computational Crystallography Initiative Physical Biosciences Division

Conflicting information • What is going on? – Estimated twin fraction is large, but

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 F 222 (C 2 + H, -K, -H-L = F 222) • Need images to make decision Computational Crystallography Initiative Physical Biosciences Division

Anomalous data • Structure solution via experimental methods (especially SAD) is on the rise.

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/s. DI>; <DF/s. DF> • 2? – Measurability • Fraction of Bijvoet differences for which – DI/s. DI>3 and (I+/s. I(+) and I(-)/s. I(-) > 3) • Easy to interpret – At 3 Angstrom 6% of Bijvoet pairs are significantly larger than zero Computational Crystallography Initiative Physical Biosciences Division

Anomalous data • Measurability and <DI/s. DI> are closely related of course • Measurability

Anomalous data • Measurability and <DI/s. DI> are closely related of course • Measurability more directly translates to the number of ‘useful’ Bijvoet differences in substructure solution/phasing Computational Crystallography Initiative Physical Biosciences Division

Anomalous data <FOM> Sn. B success rate • The quality of the data determines

Anomalous data <FOM> Sn. B success rate • The quality of the data determines the success of structure solution Redundancy Weiss, (2000). J. App. Cryst, 34, 130 -135. Computational Crystallography Initiative Measurability Obtained via numerical methods Physical Biosciences Division

Anomalous data B Measurability A 6 (partially occupied) Iodines in thaumatin at l=1. 5Å.

Anomalous data B Measurability A 6 (partially occupied) Iodines in thaumatin at l=1. 5Å. Raw SAD phases, straight after PHASER 1/resolution 2 Computational Crystallography Initiative Physical Biosciences Division

Anomalous data B 6 (partially occupied) Iodines in thaumatin at l=1. 5Å. Measurability A

Anomalous data B 6 (partially occupied) Iodines in thaumatin at l=1. 5Å. Measurability A Density modified phases 1/resolution 2 Computational Crystallography Initiative Physical Biosciences Division

Anomalous data • SAD phasing with PHASER – Very sensitive residual maps • Lysozyme

Anomalous data • SAD phasing with PHASER – Very sensitive residual maps • Lysozyme soaked with solution containing (NH 4)2(Os. Cl 6) – 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 • Zheavy 2/(Zheavy 2+Zprotein 2)=35% Computational Crystallography Initiative PHASER residual map indicating location of main chain atoms Physical Biosciences Division

Anomalous data • SAD phasing with PHASER – Very sensitive residual maps • Lysozyme

Anomalous data • SAD phasing with PHASER – Very sensitive residual maps • Lysozyme soaked with solution containing (NH 4)2(Os. Cl 6) – 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 • Zheavy 2/(Zheavy 2+Zprotein 2)=35% Computational Crystallography Initiative Raw PHASER SAD phases Physical Biosciences Division

Anomalous data • Another extreme – 2 Fe 4 S 4 clusters in 60

Anomalous data • Another extreme – 2 Fe 4 S 4 clusters in 60 residues • Wilson B: 6. 5Å2; dmin=1. 2Å • Measurability at 3. 0Å: 6% – Data not terribly strong • ZFe 2/(ZFe 2+ZS 2+Zprotein 2)=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) Computational Crystallography Initiative Physical Biosciences Division

Anomalous data • Inclusion of Sulfurs improves phasing – (ZFe 2+ZS 2)/(ZFe 2+ZS 2+Zprotein

Anomalous data • Inclusion of Sulfurs improves phasing – (ZFe 2+ZS 2)/(ZFe 2+ZS 2+Zprotein 2) =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). Computational Crystallography Initiative Physical Biosciences Division

Discussion & Conclusions • Software tools are available to point out specific problems –

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 Computational Crystallography Initiative Physical Biosciences Division

First Aid Analyses at the beamline If problem are detected while at the beam

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 Computational Crystallography Initiative Physical Biosciences Division

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

Pathology/Autopsy Analyses at home The anatomical lesson of dr. Nicolaes Tulp - 1632. Rembrandt van Rijn. Computational Crystallography Initiative Physical Biosciences Division

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

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