Towards Low Resolution Refinement Garib N Murshudov York

Towards Low Resolution Refinement Garib N Murshudov York Structural Laboratory Chemistry Department University of York

Contents 1) 2) 3) 4) Some of the projects TWIN refinement in REFMAC and its extension Problems of low resolution refinement Some tools for low resolution: ncs, external restraints, B value restraints and “jelly” body 5) Map sharpening: General approach and some applications 6) Some future plans 7) Conclusions

BALBES: automatic molecular replacement pipeline People involved: Alexei Vagin, Fei Long Using redesigned PDB with their domain and multimeric organisation tries to solve molecular replacement problem. 3

Jligand People involved: Andrey Lebedev and Paul Young (and ccp 4) A GUI to design links and covalent link descriptions. Link 4

Conformation invariant alignment and restrain generation People involved: Rob Nicholls 2 cex : Sialic Acid Binding Protein 1 mpd : Maltose Binding Protein Green = 0 Red = 2 average fragment score = 1. 2 A

Resolution of space group uncertainty after refinement Andrey Lebedev 6

Available refinement programs • • • SHELXL CNS REFMAC 5 TNT BUSTER/TNT Phenix. refine RESTRAINT MOPRO MAIN

What can REFMAC do? • • • Simple maximum likelihood restrained refinement Twin refinement Phased refinement (with Hendrickson-Lattmann coefficients) SAD/SIRAS refinement Structure idealisation Library for more than 8000 ligands (from the next version) Covalent links between ligands and ligand-protein Rigid body refinement NCS local, restraints to external structures TLS refinement Map sharpening etc

Twinning

merohedral and pseudo-merohedral twinning Crystal symmetry: Constrain: Lattice symmetry *: (rotations only) Possible twinning: P 3 P 622 P 2 β = 90º P 222 P 2 merohedral pseudo-merohedral - Domain 1 Twinning operator Domain 2 Crystal lattice is invariant with respect to twinning operator. The crystal is NOT invariant with respect to twinning operator. -

Twin refinement in REFMAC Twin refinement in refmac (5. 5 or later) is automatic. – Identify “twin” operators – Calculate “Rmerge” (Σ|Ih-<I>twin| /ΣΙh) for each operator. Ιf Rmerge>0. 50 keep it: Twin plus crystal symmetry operators should form a group – Refine twin fractions. Keep only “significant” domains (default threshold is 5%): Twin plus symmetry operators should form a group Intensities can be used If phases are available they can be used Maximum likelihood refinement is used

Likelihood The dimension of integration is in general twice the number of twin related domains. Since the phases do not contribute to the first part of the integrant the second part becomes Rice distribution. The integration is carried out using Laplace approximation. These equations are general enough to account for: non-merohedral twinning (including allawtwin), unmerged data. A little bit modification should allow handling of simultaneous twin and SAD/MAD phasing, radiation damage 12

Electron density: likelihood based Map coefficients It seems to be working reasonable well. For unbiased map it is necessary to integrate over errors in all parameters (observations as well as refined parameters.

Electron density: 1 jrg Warning: Usually twin refinement reduces R factors but electron density does not improve much “non twin” map “twin” map

Effect of twin on electron density: Data provided by Ivan Campeotto Space group: Cell parameters: Resolution: Twin operators: Twin fractions: “Rmerge”: Rmerge for calc: R/Rfree no twin: R/Rfee twin: P 21 54. 63 142. 77 84. 37 90. 00 108. 76 90. 00 1. 8 -H, -K, H+L (or H, -K, -H-L) 0. 46, 0. 54 0. 065 0. 36 0. 30/0. 34 0. 21/0. 26 R/Rfree are final statistics after refinement and rebuilding Note: Reindexing may be needed. In these cases REFMAC warns that you may reconsider reindexing.

Effect of twin on electron density: Data provided by Ivan Campeotto Twin off (difficult rebuilding) Twin on (final model)

Twinning: Warning Rfactors Random R factor in the presence of perfect twin with twinning modeled is around 40. If twinning is not modeled then it is around 50. Be careful after molecular replacement Small twin fractions: Be careful with small twin fractions. Refmac removes twin domains with fraction less than 5% High symmetry: If twin fractions are refined towards perfect twinning then space group may be higher. Program zanuda from YSBL website may sort out some of the space group uncertainty problems: www. ysbl. york. ac. uk/YSBLPrograms/index. jsp

Twin: Few warnings about R factors For acentric case only: For random structure Crystallographic R factors No twinning 58% For perfect twinning: twin modelled 40% For perfect twinning without twin modelled 50% R merges without experimental error No twinning 50% Along non twinned axes with another axis than twin 37. 5% Non twin Twin

Effect of twinning on electron density Using twinning in refinement programs is straightforward. It improves statistics substantially (sometimes R-factors can go down by 10%). However improvement of electron density is not very dramatic (just like when you use TLS). It may improve electron density in weak parts but in general do not expect miracles. Especially when twinning and NCS are close then improvements are marginal.

Further applications of “twin” likelihood 1) Reticular (or non-merohedral) twinning 2) Split crystals 3) Overlapping spots that have been integrated as single observation

Problems of low resolution refinement 1) Function to describe fit of the model into experiment: likelihood or similar 1) Data may come from very peculiar “crystals”: Twin, OD, multiple cell 2) Radiation damage 3) Converting I-s to |F| may not be valid 2) Limited and noisy data: use of available knowledge 1) Known structures 2) Internal patterns: NCS, secondary structure 3) Smeared electron density with vanishing side chains, secondary structures, domains: High B values and series termination: 1) Filtering methods: Solve inverse problem with regulariser 2) Missing data problem: Data augmentation, bootstrap

Use of available knowledge 1) NCS local 2) Restraints to known structure(s) 3) Restraints to current inter-atomic distances (implicit normal modes or “jelly” body) 4) Better restraints on B values These are available from the version 5. 6 Note Buster/TNT has local NCS and restraints to known structures CNS has restraints to known structures (they call it deformable elastic network) Phenix has B-value restraints on non-bonded atom pairs and automatic global NCS Local NCS (only for torsion angle related atom pairs) was available in SHELXL since the beginning of time

Auto NCS: local and global 1. Align all chains with all chains using Needleman-Wunsh method 2. If alignment score is higher than predefined (e. g. 80%) value then consider them as similar 3. Find local RMS and if average local RMS is less than predefined value then consider them aligned 4. Find correspondence between atoms 5. If global restraints (i. e. restraints based on RMS between atoms of aligned chains) then identify domains 6. For local NCS make the list of corresponding interatomic distances (remove bond angle related atom pairs) 7. Design weights 8. The list of interatomic distance pairs is calculated at every cycle

Auto NCS Aligned regions Global RMS is calculated using all aligned atoms. Local RMS is calculated using k (default is 5) residue sliding windows and then averaging of the results Chain A Chain B k(=5)

Auto NCS: Neighbours Water or ligand After alignment, neighbours are analysed. 1) Each water, ligand is assigned to the chain they are close to. 2) Neighbours included in restrains when possible Shell 2 Shell 1 Chain A Water or ligand Chain B Shell 2 Shell 1

Auto NCS: Iterative alignment Example of alignment: 2 vtu. There are two chains similar to each other. There appears to be gene duplication RMS – all aligned atoms Ave(Rms. Loc) – local RMS ***** Alignment results ***** ---------------------------------------: N: Chain 1 : Chain 2 : No of aligned : Score : RMS : Ave(Rms. Loc): ---------------------------------------: 1 : J( 131 - 256 ) : J( 3 - 128 ) : 126 : 1. 0000 : 5. 2409 : 1. 6608 : : 2 : J( 1 - 257 ) : L( 1 - 257 ) : 257 : 1. 0000 : 4. 8200 : 1. 6694 : : 3 : J( 131 - 256 ) : L( 3 - 128 ) : 126 : 1. 0000 : 5. 2092 : 1. 6820 : : 4 : J( 3 - 128 ) : L( 131 - 256 ) : 126 : 1. 0000 : 3. 0316 : 1. 5414 : : 5 : L( 131 - 256 ) : L( 3 - 128 ) : 126 : 1. 0000 : 0. 4515 : 0. 0464 : -----------------------------------------------------------------------

Auto NCS: Conformational changes Domain 2 In many cases it could be expected that two or more copies of the same molecule will have (slightly) different conformation. For example if there is a domain movement then internal structures of domains will be same but between domains distances will be different in two copies of a molecule Domain 1 Domain 2 Domain 1

Robust estimators One class of robust (to outliers) estimators are called M-estimators: maximum-likelihood like estimators. One of the popular functions is Geman-Mcclure. Essentially when distances are similar then they should be kept similar and when they are too different they should be allowed to be different. This function is used for NCS local restraints as well as for restraints to external structures Red line: Black line: x 2 x^2/(1+w x^2) where x=(d 1 -d 2)/σ, w=0. 1

Restraints to external structures It is done by Rob Nicholls Pro. Smart Compares Two Protein Chains • • Conformation-invariant structural comparison Residue-residue alignment Superimposition Residue-based and global similarity scores Produces local atomic distance restraints • Based on one or more aligned chains • Possibility of multi-crystal refinement

Pro. Smart Restrain structure to be refined xÅ known similar structure (prior)

Pro. Smart Restrain structure to be refined known similar structure (prior) Remove bond angle related pairs

To allow conformational changes, Geman-Mc. Clure type robust estimator functions are used

Restraints to current distances The term is added to the target function: Summation is over all pairs in the same chain and within given distance (default 4. 2 A). dcurrent is recalculated at every cycle. This function does not contribute to gradients. It only contributes to the second derivative matrix. It is equivalent to adding springs between atom pairs. During refinement interatomic distances are not changed very much. If all pairs would be used and weights would be very large then it would be equivalent to rigid body refinement. It could be called “implicit normal modes”, “soft” body or “jelly” body refinement.

B value restraints and TLS Designing restraints on B values is much more difficult. Current available options to deal with B values at low resolutions 1) Group B as implemented in CNS 2) TLS group refinement as implemented in refmac and phenix. refine Both of them have some applications. TLS seems to work for wide range of cases but unfortunately it is very often misused. One of the problems is discontinuity of B values. Neighbouring atoms may end up having wildly different B values In ideal world anisotropic U with good restraints should be used. But this world is far away yet. Only in some cases full aniso refinement at 3Å gives better R/Rfree than TLS refinement. These cases are with extreme ansiotropic data. TLS 2 TLS 1 loop

Parameters: B value restraints and TLS Restraints on B values 1) Differences of projections of aniso U of atom on the bond should be similar (rigid bond) 2) Kullback-Liblier (conditional entropy) divergence should be small: For isotropic atoms (for bonded and non-bonded atoms) B 1/B 2+B 2/B 1 -2 1) Local TLS: Neighboring atoms should be related as TLS groups (not available yet)

Kullback-Leibler divergence If there are two densities of distributions – p(x) and q(x) then symmetrised Kullback. Leibler divergence between them is defined (it is distance between distributions) If both distributions are Gaussian with the same mean values and U 1 and U 2 variances then this distance becomes: And for isotropic case it becomes Restraints for bonded pairs have more weights more than for non-bonded pairs. For nonbonded atoms weights depend on the distance between atoms. This type of restraint is also applied for rigid bond restraints in anisotropic refinement 36

Example, after molecular replacement 3 A resolution, data completeness 71% Rfactors vs cycle Black – simple refinement Red – Global NCS Blue – Local NCS Green – “Jelly” body Solid lines – Rfactor Dashed lines - Rfree

Example: 4 A resolution, data from pdb 2 r 6 c Rfactors vs cycle Black – Simple refinement Red – External restraints Blue – “Jelly” body Solid lines – Rfactor Dashed lines - Rfree

Example: 5 A resolution, data from pdb 2 w 6 h Rfactors vs cycle Black – Simple refinement Red – External restraints Blue – “Jelly” + local NCS Solid lines – Rfactor Dashed lines - Rfree

MAP SHARPENING: INVERSE PROBLEM We want to observe ρ0(x) but we observe ρ(x). These two entities are related: If K is known, calculate ρ0. In general problem is easy: Discretise and solve the linear equation. However these problems are ill-posed (small perturbation in the input causes large deviation in the output). In practice: by sharpening signal as well as noise are amplified. Regularisation may help: L is related with regularisation function. For L 2 norm (value of ρ should be small) L is identity and for Sobolev norm (ρ should be smooth) of first order it is Laplace operators 40

MAP SHARPENING: INVERSE PROBLEM In general K is effect of such terms as TLS or smoothly varying blurring function. Noises in electron density: series termination, errors in phases and noises in experimental data. Very simple case: K is overall B value. Then the problem is solved using FFT: Fourier transform of Gaussian is Gaussian, Fourier transform of Laplace operator is square length of the reciprocal space vector. 41

MAP SHARPENING: INVERSE PROBLEM REGULARISATION PARAMETER. One way of selecting regularisation parameter: Minimise predicted error. Where Aα=K Kα If we restore unobserved data with their expected values Fe then the last term would be replaced by Restoring seems to give less predictive error (problem of bias towards error in phases remains) “Best” regularisation parameter is that minimises PE. 42

MAP SHARPENING: 2 R 6 C, 4Å RESOLUTION Original Sharpening, median B α 0 No sharpening Sharpening, median B α optimised Top left and bottom: After local NCS refinement 43

Some of the other new features in REFMAC SAD refinement SIRAS refinement available from version 5. 5 available from version 5. 6 New and complete dictionary Improved mask solvent Jligand for ligand dictionary and link description available from version 5. 6

Future Electron density calculation: Bayesian filters How to combine two conflicting ideas: Sharpen electron density to have “better” defined atoms and integrate over errors to smoothen the electron density thus reduce noise Local TLS restraints: Needs to be tested Restraints on secondary structures and other internal patterns Reticular twin: Almost there Radiation damage Error estimation

Conclusion • Twin refinement improves statistics and occasionally electron density • Use of similar structures should improve reliability of the derived model: Especially at low resolution • NCS restraints must be done automatically: but conformational flexibility must be accounted for • “Jelly” body works better than I though it should • Regularised map sharpening looks promising. More work should be done on series termination and general sharpening operators

Acknowledgment York Leiden Alexei Vagin Pavol Skubak Andrey Lebedev Raj Pannu Rob Nocholls Fei Long CCP 4, YSBL people REFMAC is available from CCP 4 or from York’s ftp site: www. ysbl. york. ac. uk/refmac/latest_refmac. html This and other presentations can be found on: www. ysbl. york. ac. uk/refmac/Presentations/