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Crank developments available in CCP4 6.1 PowerPoint Presentation
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Crank developments available in CCP4 6.1

Crank developments available in CCP4 6.1

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Crank developments available in CCP4 6.1

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  1. Progress report on Crank:Experimental phasingBiophysical Structural ChemistryLeiden University, The Netherlands

  2. Crank developments available in CCP4 6.1 • “Greatly enhanced” – better tested • Underlying programs haven’t changed (much), but crank almost completely re-written from version in 6.0.2 • Better ccp4i interface • Support for more programs (PIRATE, BUCCANEER, RESOLVE, COOT) • Faster substructure detection • Use BP3 to (quickly) check trials and look at deviations between different CRUNCH2 trials significantly decreases the time required for successful substructure detection.

  3. Speeding up CRUNCH2:Results showing improvement

  4. Improved CCP4i interface

  5. Preliminary substructure detection results from JCSG test cases • 144 mostly MAD Se-Met data sets • Defaults only: the only input was number of Se-Met per monomer (number of monomer was guessed). Mtz files, f’, f”. • Some data sets had f” < 1 (solved by MR) • Some data sets had incorrectly labelled X-PLOR files as mtz. • DISCLAIMER: 1st logfiles produced and analyzed yesterday after dinner (until 4 a.m.).

  6. AFRO/CRUNCH2 vs SHELXC/D(both run in CRANK) Of the 79 jobs in common, crunch2 was faster in 20 jobs, while shelxd was faster in 59.

  7. Comparison not fair • Same algorithm to identify solution with BP3 can be used in SHELXD • SHELXD uses much better Fa values (i.e. using the MAD data – at the moment, Afro just uses delta F from the data set with the greatest anomalous signal).

  8. Improving FA values • An early step in solving a structure by SAD/MAD or SIRAS is to determine FA values. • FA is the structure factor amplitude corresponding to the substructure to input to direct methods and/or Patterson programs (i.e. SHELXD or CRUNCH2)

  9. Current FA estimation • FA is currently estimated by | |F+| - |F-| | for SAD data in most programs. • Direct method programs are very sensitive to FA values. • Improving estimates can improve hit rates of direct methods and solve substructures that can not previously been solved.

  10. Multivariate SAD equation • E(|FA|,|F+|,|F-|) = •    |FA| P(|FA|, αA,| |F+|, α+,|F-|, α-) d|FA| dαA dα+dα- • Giacovazzo previously proposed multivariate FA estimation, with an implementation assuming Bijvoet phases are equal. • An equation can be obtained without the equal phase assumption requiring only one numerical integration. • The equation has been implemented – which reduces to Giacovazzo’s equation if Bijvoet phases are equal.

  11. Covariance matrix properties • The covariance matrix considers experimental sigmas and correlations between F+, F- and FA. • Problem: Covariance matrix also depends on (overall) substructure occupancy and b-factor. • Solution: Obtain a multivariate likelihood estimate for unknown parameters.

  12. Refining overall substructure parameters • Initial guess of number of substructure atoms per monomer obtained from user. • Initial guess of B-factor obtained from likelihood estimate of overall B-factor of data set. • Result: Refinement is stable and maximizes correlation with calculated final E’s. • Another possible application: Use refined overall occupancy and B-factor for anomalous signal estimation.

  13. Test cases: Correlations with final calculated E’s

  14. More robustness in difficult cases with CRUNCH2 • Using default parameters (resolution cutoff of 0.5 from the high resolution limit). * Can be solved with ΔE by using data to 1.5 Angstroms