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Automated phase improvement and model building with Parrot and Buccaneer

Automated phase improvement and model building with Parrot and Buccaneer. Kevin Cowtan cowtan@ysbl.york.ac.uk. X-ray structure solution pipeline. Data collection. Data processing. Experimental phasing. Molecular Replacement. Density Modification. Model building. Refinement.

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Automated phase improvement and model building with Parrot and Buccaneer

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  1. Automated phase improvementand model building withParrot and Buccaneer Kevin Cowtancowtan@ysbl.york.ac.uk

  2. X-ray structure solution pipeline... Data collection Data processing Experimental phasing Molecular Replacement Density Modification Model building Refinement Rebuilding Validation

  3. Density modification Density modification is a problem in combining information:

  4. Density modification 1. Rudimentary calculation: FFT |F|, φ ρ(x) φ=φmod Modify ρ |Fmod|, φmod ρmod(x) FFT-1 Reciprocal space Real space

  5. Density modification 3. Phase probability distributions: centroid FFT |F|, P(φ) |Fbest|, φbest ρ(x) P(φ)=Pexp(φ),Pmod(φ) Modify ρ Pmod(φ) |Fmod|, φmod ρmod(x) likelihood FFT-1 Reciprocal space Real space

  6. Density modification 4. Bias reduction (gamma-correction): DM, SOLOMON, (CNS) centroid FFT |F|, P(φ) |Fbest|, φbest ρ(x) Modify ρ P(φ)=Pexp(φ),Pmod(φ) ρmod(x) γ-correct Pmod(φ) |Fmod|, φmod ργ(x) likelihood FFT-1 J.P.Abrahams

  7. Density modification 5. Maximum Likelihood H-L: PARROT centroid FFT |F|, P(φ) |Fbest|, φbest ρ(x) Modify ρ ρmod(x) γ-correct |Fmod|, φmod ργ(x) MLHL FFT-1

  8. Density modification Traditional density modification techniques: Solvent flattening Histogram matching Non-crystallographic symmetry (NCS) averaging

  9. Solvent flattening

  10. Histogram matching A technique from image processing for modifying the protein region. Noise maps have Gaussian histogram. Well phased maps have a skewed distribution: sharper peaks and bigger gaps. Sharpen the protein density by a transform which matches the histogram of a well phased map. Useful at better than 4A. P() Noise True 

  11. Non-crystallographic symmetry If the molecule has internalsymmetry, we can averagetogether related regions. In the averaged map, thesignal-noise level is improved. If a full density modificationcalculation is performed,powerful phase relationshipsare formed. With 4-fold NCS, can phasefrom random!

  12. Non-crystallographic symmetry How do you know if you have NCS? Cell content analysis – how many monomers in ASU? Self-rotation function. Difference Pattersons (pseudo-translation only). How do you determine the NCS? From heavy atoms. From initial model building. From molecular replacement. From density MR (hard). Mask determined automatically.

  13. Density modification in Parrot • Builds on existing ideas: • DM: • Solvent flattening • Histogram matching • NCS averaging • Perturbation gamma • Solomon: • Gamma correction • Local variance solvent mask • Weighted averaging mask

  14. Density modification in Parrot • New developments: • MLHL phase combination • (as used in refinement: refmac, cns) • Anisotropy correction • Problem-specific density histograms • (rather than a standard library) • Pairwise-weighted NCS averaging...

  15. Estimating phase probabilities • Solution: • MLHL-type likelihood • target function. Perform the error estimation and phase combination in a single step, using a likelihood function which incorporates the experimental phase information as a prior. This is the same MLHL-type like likelihood refinement target used in modern refinement software such as refmac or cns.

  16. Recent Developments: • Pairwise-weighted NCS averaging: • Average each pair of NCS related molecules separately with its own mask. • Generalisation and automation of multi-domain averaging. C B A A C B B C A

  17. Parrot

  18. Parrot: Rice vs MLHL Map correlations Comparing old and new likelihood functions.

  19. Parrot: simple vs NCS averaged Map correlations Comparing with and without NCS averaging.

  20. DM vs PARROT vs PIRATE • % residues autobuilt and sequenced • 50 JCSG structures, 1.8-3.2A resolution 79.1% 78.4% 74.2% DM PARROT PIRATE

  21. DM vs PARROT vs PIRATE • Mean time taken • 50 JCSG structures, 1.8-3.2A resolution 887s 10s 6s DM PARROT PIRATE

  22. DM vs PARROT vs PIRATE % residues autobuilt and sequenced 50 JCSG structures, 1.8-3.2A resolution 79.1% 78.4% 74.2% DM PARROT PIRATE

  23. DM vs PARROT vs PIRATE Mean time taken 50 JCSG structures, 1.8-3.2A resolution 887s 10s 6s DM PARROT PIRATE

  24. Buccaneer Statistical model building software based on the use of a reference structure to construct likelihood targets for protein features. Buccaneer-Refmac pipeline NCS auto-completion Improved sequencing

  25. Buccaneer: Latest Buccaneer 1.2 Use of Se atoms, MR model in sequencing. Improved numbering of output sequences (ins/del) Favour more probable sidechain rotamers Prune clashing side chains Optionally fix the model in the ASU Performance improvements (1.5 x) Including 'Fast mode' (2-3 x for good maps) Multi-threading (not in CCP4 6.1.1) Buccaneer 1.3 Molecular replacement rebuild mode Performance improvements, more cycles.

  26. Buccaneer: Method Compare simulated map and known model to obtain likelihood target, then search for this target in the unknown map. Reference structure: Work structure: LLK

  27. Buccaneer: Method Compile statistics for reference map in 4A sphere about C => LLK target. • Use mean/variance. 4A sphere about Ca also used by 'CAPRA' Ioeger et al. (but different target function).

  28. Buccaneer 10 stages: Find candidate C-alpha positions Grow them into chain fragments Join and merge the fragments, resolving branches Link nearby N and C terminii (if possible) Sequence the chains (i.e. dock sequence) Correct insertions/deletions Filter based on poor density NCSRebuild to complete NCS copies of chains Prune any remaining clashing chains Rebuild side chains

  29. Buccaneer Use a likelihood function based on conserved density features. The same likelihood function is used several times. This makes the program very simple (<3000 lines), and the whole calculation works over a range of resolutions. Finding, growing: Look for C-alpha environment Sequencing: Look for C-beta environment ... x20 ALA CYS HIS MET THR

  30. Buccaneer Case Study: A difficult loop in a 2.9A map, calculated using real data from the JCSG.

  31. Find candidate C-alpha positions

  32. Grow into chain fragments

  33. Join and merge chain fragments

  34. Sequence the chains

  35. Correct insertions/deletions

  36. Prune any remaining clashing chains

  37. Rebuild side chains

  38. Comparison to the final model

  39. Buccaneer: Results Model completeness not very dependent on resolution:

  40. Buccaneer: Results Model completeness dependent on initial phases:

  41. Buccaneer Cycle BUCCANEER and REFMAC for most complete model Single run of BUCCANEER only (more options) quick assessment/advanced use

  42. Buccaneer

  43. Buccaneer What it does: Trace protein chains (trans-peptides only) Link across small gaps Sequence Apply NCS Build side chains (roughly) Refine (if recycled) WORK AT LOW RESOLUTIONS 3.7A with good phases

  44. Buccaneer What it does not do (yet): Cis-peptides Waters Ligands Loop fitting Tidy up the resulting model In other words, it is an ideal component for use in larger pipelines.

  45. Buccaneer What you need to do afterwards: Tidy up with Coot. Or ARP/wARP when resolution is good. Buccaneer/ARP/wARP better+faster than ARP/wARP. Typical Coot steps: Connect up any broken chains. Use density fit and rotamer analysis to check rotamers. Check Ramachandran, molprobity, etc. Add waters, ligands, check un-modeled blobs.. Re-refine, examine difference maps.

  46. Buccaneer: Summary A simple, fast, easy to use (i.e. MTZ and sequence) method of model building which is robust against resolution. User reports for structures down to 3.7A when phasing is good. Results can be further improved by iterating with refinement in refmac (and in future, density modification). Proven on real world problems.

  47. Achnowledgements Help: JCSG data archive: www.jcsg.org Eleanor Dodson, Paul Emsley, Randy Read, Clemens Vonrhein, Raj Pannu Funding: The Royal Society

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