1. CommonCoot Common Coot (Fulica atra) (Fulica atra)

2. Ligands and validation クート オオバン

3. Ligands and validation with Coot Bernhard Lohkamp Karolinska Institutet December 2011 OIST

4. Ligands in Coot Fitting ligand Fitting ligand Ligand validation Ligand validation Ligand representation Ligand representation Ligand builder Ligand builder

5. Ligand Overlay Algorithm and Code by Eugene Krissinel Algorithm and Code by Eugene Krissinel Tries to overlay different ligands/monomers by graph matching Tries to overlay different ligands/monomers by graph matching Useful for “database” ligands where atom names are not selected by hand Useful for “database” ligands where atom names are not selected by hand Has been used as the basis of the function which “mutates” residues to alternative monomer types Has been used as the basis of the function which “mutates” residues to alternative monomer types e.g. phosphorylation e.g. phosphorylation

6. Restraint Editor

7. Displaying LINKs

8. Ligand validation

9. Ligand validation Why validate? 70.000 structures in PDB 70.000 structures in PDB 11.000 'ligands' in 50.000 structures 11.000 'ligands' in 50.000 structures Sulphate ions in 1DW9 Sulphate ions in 1DW9 1.65 A resolution 1.65 A resolution R/Rfree 0.15/0.19 R/Rfree 0.15/0.19

10. Ligand validation How to validate ligand geometry? How to validate ligand geometry? Compare observed structure to restraints Coot: Validate->Geometry analysis Compare observed structure to restraints Coot: Validate->Geometry analysis But what if parametrization (dictionary) is wrong? But what if parametrization (dictionary) is wrong?

11. How to validate ligand geometry? QM QM CPU hungry CPU hungry In vaccuo In vaccuo Low energy ≠ (?) bound ligand Low energy ≠ (?) bound ligand PDB (e.g. ValLigURL) PDB (e.g. ValLigURL) Good cofactor structures Good cofactor structures Less useful for novel ligands Less useful for novel ligands (occ.) questionable quality (occ.) questionable quality Cambridge Structural Database (CSD) Cambridge Structural Database (CSD) Using small molecule geometries Using small molecule geometries e.g. Mogul e.g. Mogul

12. Ligand representation

13. Bond order representation from dictionary files Bond order representation from dictionary files

14. Surfaces Partial charges Partial charges

15. Surfaces Transparent surfaces – surface complementarity Transparent surfaces – surface complementarity

16. Binding mode analysis Binding site highlighting Binding site highlighting Isolated molprobity dots Isolated molprobity dots

17. Binding mode analysis Binding site highlighting Binding site highlighting Isolated molprobity dots Isolated molprobity dots

18. Ligand environment 2D ligand pocket layout (ligplot, poseview) 2D ligand pocket layout (ligplot, poseview) Can we do better?

19. Ligand environment layout Binding pocket residues Binding pocket residues Interactions Interactions Substitution contour Substitution contour Solvent accessible halos Solvent accessible halos Work in progress!!!!!

20. Ligand environment layout Considerations Considerations 2D placement and distances should reflect 3D metrics 2D placement and distances should reflect 3D metrics As much as possible As much as possible Residues should not overlap the ligand Residues should not overlap the ligand Residues should not overlap each other Residues should not overlap each other H-bonded residues should be close to atoms to which they are bonded H-bonded residues should be close to atoms to which they are bonded Etc. Etc. c.f. Clark & Labute (2007); poseview c.f. Clark & Labute (2007); poseview Work in progress

21. Ligand environment layout Initial residue placement Initial residue placement

22. Ligand environment layout Residue position minimization Residue position minimization

24. Solvent exposure calculation Identification of solvent accessible atoms Identification of solvent accessible atoms Different from substitution contour Different from substitution contour

26. Ligand futures Make ligand builder (and environment layout) available Make ligand builder (and environment layout) available Interface to JLigand Interface to JLigand Ligand validation with CSD (Mogul or similar) Ligand validation with CSD (Mogul or similar)

27. Validation

28. Refinement Validation External e.g. REFMAC Internal Internal External e.g. MolProbity Feature Integration

31. What is Validation? Comparison of various aspects of the model with pre-conceived notions of “good quality” Comparison of various aspects of the model with pre-conceived notions of “good quality” Includes unrestrained and restrained criteria Includes unrestrained and restrained criteria Many aspects of validation overlap with refinement and model-building Many aspects of validation overlap with refinement and model-building Global and local indicators Global and local indicators

32. Why Validate? Model-building is error-prone Model-building is error-prone (although automated methods seem to do better) (although automated methods seem to do better) Map interpretation is subjective Map interpretation is subjective Someone else did the model-building Someone else did the model-building The model was built several years ago The model was built several years ago and the notion of “good quality” has changed and the notion of “good quality” has changed Deposition requires validation Deposition requires validation

33. A “good” model Makes statistical sense Makes statistical sense The reciprocal space representation agrees tolerably well with the observations (R-factor) The reciprocal space representation agrees tolerably well with the observations (R-factor) No meaningful difference map peaks (no under- or over-fitting) No meaningful difference map peaks (no under- or over-fitting)

34. A “good” model Makes statistical sense Makes statistical sense The reciprocal space representation agrees tolerably well with the observations (R-factor) The reciprocal space representation agrees tolerably well with the observations (R-factor) No meaningful difference map peaks (no under- or over-fitting) No meaningful difference map peaks (no under- or over-fitting) Makes chemical sense Makes chemical sense Model geometry is consistent with the restraints Model geometry is consistent with the restraints Ramachandran Plot has less than 1% outliers Ramachandran Plot has less than 1% outliers A good clashscore A good clashscore

35. A “good” model Makes statistical sense Makes statistical sense The reciprocal space representation agrees tolerably well with the observations (R-factor) The reciprocal space representation agrees tolerably well with the observations (R-factor) No meaningful difference map peaks (no under- or over-fitting) No meaningful difference map peaks (no under- or over-fitting) Makes chemical sense Makes chemical sense Model geometry is consistent with the restraints Model geometry is consistent with the restraints Ramachandran Plot has less than 1% outliers Ramachandran Plot has less than 1% outliers A good clashscore A good clashscore Make physical sense Make physical sense Crystal packing and contacts Crystal packing and contacts

36. A “good” model Makes statistical sense Makes statistical sense The reciprocal space representation agrees tolerably well with the observations (R-factor) The reciprocal space representation agrees tolerably well with the observations (R-factor) No meaningful difference map peaks (no under- or over-fitting) No meaningful difference map peaks (no under- or over-fitting) Makes chemical sense Makes chemical sense Model geometry is consistent with the restraints Model geometry is consistent with the restraints Ramachandran Plot has less than 1% outliers Ramachandran Plot has less than 1% outliers A good clashscore A good clashscore Make physical sense Make physical sense Crystal packing and contacts Crystal packing and contacts Makes biological sense Makes biological sense Residues in chemically sensible environment Residues in chemically sensible environment Is consistent (on the whole) with external biochemistry observations (active site residues) Is consistent (on the whole) with external biochemistry observations (active site residues)

37. Quick Bayes Bayes Eq: Bayes Eq: Pr( model | data )  Pr( data | model ) * Pr( model ) Pr( model | data )  Pr( data | model ) * Pr( model ) Pr( data | model ) is also called the model likelihood, L M ( model | data ) Pr( data | model ) is also called the model likelihood, L M ( model | data )

38. Validation Tools - Pr(model) Ramachandran Plot Ramachandran Plot Kleywegt Plot (NCS differences) Kleywegt Plot (NCS differences) Geometry Analysis Geometry Analysis Peptide  Analysis Peptide  Analysis Temperature Factor Analysis Temperature Factor Analysis Rotamer Analysis Rotamer Analysis Clashes Clashes

39. Validation Tools - Pr(data|model) Density Fit Analysis Density Fit Analysis Difference Map Peaks Difference Map Peaks Variance analysis at water positions Variance analysis at water positions Unmodelled blobs Unmodelled blobs

41. Find difference map peaks

42. Check/Delete Waters

43. Difference Map Sampling Detects “Anomalous” Waters

46. Find unmodelled blobs

47. Pr(model)... Ramachandran Plot Ramachandran Plot Kleywegt Plot (NCS differences) Kleywegt Plot (NCS differences) Geometry Analysis Geometry Analysis Peptide  Analysis Peptide  Analysis Temperature Factor Analysis Temperature Factor Analysis Rotamer Analysis Rotamer Analysis Sequence analysis Sequence analysis Clashes Clashes

49. Ramachandran Plot for residues with CB

50. Ramachandran Plot for GLY

51. Ramachandran Plot for PRO

52. Top500-based distribution

53. Not all outliers are bad

54. Kleywegt Plots [*] [*] Named by George Sheldrick

56. Peptide  Needed to check the planarity of the peptide link Needed to check the planarity of the peptide link At low resolutions it is possible to give the protein lots of (too much) freedom to optimize the fit to the density At low resolutions it is possible to give the protein lots of (too much) freedom to optimize the fit to the density Can accidentally create CIS peptides Can accidentally create CIS peptides When discovered they are easily reconverted using the CIS TRANS peptide tool When discovered they are easily reconverted using the CIS TRANS peptide tool Less accidents happen when peptide plane restraints are applied Less accidents happen when peptide plane restraints are applied

57. B-factor variance

58. Rotamers Side-chains have certain preferred combinations of torsions round their rotatable bonds Side-chains have certain preferred combinations of torsions round their rotatable bonds An analysis (batched around the staggered conformations) will give rotamer occurrence An analysis (batched around the staggered conformations) will give rotamer occurrence

59. Torsion-based Validation

61. Not all outliers are bad Correct rotamer(?!)

62. Sequence validation Assign sequence (Extensions  Dock sequence  Associate sequence) Assign sequence (Extensions  Dock sequence  Associate sequence) Sequence alignment of model to given sequence (Validate  Alignment vs PIR) Sequence alignment of model to given sequence (Validate  Alignment vs PIR)

63. More Validation Pr(model) Coot has interface to Molprobity Coot has interface to Molprobity (Molprobity is the widely regarded as the best model validation suite) (Molprobity is the widely regarded as the best model validation suite) Uses identical Ramachandran plot Uses identical Ramachandran plot Uses identical Rotamer library Uses identical Rotamer library Coot reads probe dots directly Coot reads probe dots directly

65. Molprobity’s Reduce & Probe

67. Other Programs Molprobity Suite Molprobity Suite molprobity.biochem.duke.edu molprobity.biochem.duke.edu WHATCHECK WHATCHECK VERIFY-3D VERIFY-3D

68. Acknowledgements Paul Emsley Paul Emsley Kevin Cowtan Kevin Cowtan Eleanor Dodson Eleanor Dodson Keith Wilson Keith Wilson Libraries, dictionaries Libraries, dictionaries Alexei Vagin, Eugene Krissinel, Stuart McNicholas Alexei Vagin, Eugene Krissinel, Stuart McNicholas Dunbrack, Richardsons Dunbrack, Richardsons Coot Builders and Testers Coot Builders and Testers William Scott, Ezra Peisach William Scott, Ezra Peisach York YSBL, Dundee, Glasgow (early adopters) York YSBL, Dundee, Glasgow (early adopters) Coot Mailing List subscribers Coot Mailing List subscribers http://www.biop.ox.ac.uk/coot/or Google: Coot or for WinCoot http://www.ysbl.ac.uk/~lohkamp/coot