1 / 37

Recent Developments in TEXTAL

Recent Developments in TEXTAL. Phenix Workshop Berkeley Sept. 2006 Thomas R. Ioerger Texas A&M University. NCS Identification via Pattern Recognition.

hanne
Télécharger la présentation

Recent Developments in TEXTAL

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Recent Developmentsin TEXTAL Phenix Workshop Berkeley Sept. 2006 Thomas R. Ioerger Texas A&M University

  2. NCS Identification via Pattern Recognition • Pai, R., Sacchettini, J.C. and Ioerger, T.R. (2006). Identifying non-crystallographic symmetry in protein electron-density maps: a feature-based approach. Acta Crystallographica, D62(9):1012-1021. • The Problem: • Symmetry averaging can greatly improve phases. • Typical methods for finding NCS require ≥ 3 heavy atoms, and are sensitive to errors in coordinates. • Despite noise and breaks from symmetry, similar patterns of density exist over large regions of real space (even if imperfectly phased). • How to efficiently identify these similarities and derive symmetry operators?

  3. Our Approach to NCS • Step 1: calculate backbone using CAPRA • Putative C-alpha atoms become centers of regions for initial matching • Step 2: Calculate local features for each CA based on pattern of surround CA’s and density; select subset of candidates that are likely to be similar • Example features: #CAs, center of mass, moments of inertia, std.dev., skewness, kurtosis…

  4. Step 3: Calculate local density correlation between each pair of CA’s (over 5A spheres), with rotation-optimization • Step 4: Cluster pairs of matching regions with similar rotation matrices • How can you tell if two local transformations are related (from same pair of domains)? • Each can transform the coordinates of the other. • Definition 1: similar rotation matrices. Given RUV and RPQ as • rotation matrices that optimally superpose regions U and V • and regions P and Q, respectively, and u, v, p and q as the • coordinates of the centers of regions U, V, P and Q, respectively, • then RUV is similar to RPQ if q RUV p ≤ 2 A° and • u RPQ v ≤ 2 A°. V U P Q

  5. Step 5: Extend regions to molecular boundaries (excluding non-symmetric deviations) • - caveat: doesn’t work for proper symmetry (can’t identify unique boundaries) • Step 6: Organize and output N-1 operators • (Step 7): Run DM to do symmetry-averaging

  6. Results on Experimental Maps

  7. 2a2u 1a7a 1p32 One subunit (identified by algorithm) superposed on the other subunits using symmetry operators (also identified by algorithm)

  8. Availability • Pattern Recognition Algorithm for NCS (by Reetal Pai, PhD student in Ioerger lab) • Initial implementation in C and csh scripts • User input: structure factors (.mtz), expected # copies • Runs CAPRA, extracts features, matches regions… • Automatically runs DM to improve phases via averaging • Output: • NCS operators • masks for each region • C-alpha chains for each region • NCS-averaged structure factors (.mtz) • Web server: textal.tamu.edu/NCS • Users can upload reflection file; results emailed back

  9. Port to Python • Command line # first source phenix_setup and ccp4_setup >textal.find_ncs prot.mtz <N> <FP> <PHIB> <FOM> ... Outputs: prot_ncs_ops.dat, prot_ncs_avg.mtz prot_mask_1.xplor, prot_mask_2.xplor... prot_region_1.pdb, prog_region_2.pdb... • Script-level API from textal.find_ncs import find_ncs from textal.io.reflection_file import reflection_file ref = reflection_file("mbp.mtz") obj = find_ncs(reflections=ref,copies=2, amplitude='FP',phases='PHIB',FOM='FOM') obj.find_ncs() (rot_mat,trans_vec) = obj.get_operators(0) model1 = obj.get_subunit(0) # type pdb_extended mask1 = obj.get_mask(0) # type emap

  10. Improving Sequence Alignment with Simplex • Romo, T.R., Sacchettini, J.C. and Ioerger, T.R. (2006). Improving Amino Acid Identification, Fit, and C-alpha Prediction using the Simplex Method in Automated Model-Building. Acta Crystallographica, accepted. • The Problem: • Most model-building programs build backbone first, then try to recognize side-chains (using probabilities, free atoms, features…) • Identification of amino acids is sensitive to errors in predicted Ca coordinates (often up to 1Å rms) • Even if sequence alignment is used to correct mistakes, initial side-chains must be sufficiently accurate

  11. Our Approach: Simplex Optimization • Simplex is a classic optimization algorithm • High radius of convergence • Does not require explicit computation of derivatives • Simplex can be applied to refine individual residues as rigid bodies (translation+rotation) • Several programs do local real-space rigid-body refinement of individual side-chains to improve fit. • Typically, applied after aa identity has been determined • We apply Simplex in Textal (LOOKUP) during residue selection, to help pick the template from our database that matches the local density pattern best, allowing the Ca atom to shift up to 2Å

  12. Effect of Errors in Ca Coordinates Artificially-introduced errors, starting from perfect Ca’s from refined model Percent amino acid identity Accuracy of amino acids output by LOOKUP for CzrA (without sequence alignment)

  13. Procedure • Step 1: Given a Ca, extract density-based features and retrieve K=400 most similar regions from database • Step 2: Re-rank by local density correlation (5Å) • Original method: • try to find optimal rotation only • New method: • Generate initial Simplex: N+1 perturbations of configuration vector (6-DOF) • Evaluate density correlation coefficient of each • Pick the lowest, and ‘reflect’ over average of remaining configuration vectors worst score mean of rest new 6D config. space Vector representing original position (3 coords) and orientation (3 angles) of side-chain

  14. Results on Experimental Maps Percent identity of model compared to true (refined) structure:

  15. Without Simplex With Simplex True structure Without Simplex With Simplex

  16. TEXTAL for Molecular Replacement • Motivation: • Why not exploit the MR search model if available? • No excuse for mistakes in connectivity or aa identities • Steps toward larger goal of Model Completion • Idea: • Rotate search model into density (MR solution) • Replace amino acid identities with new sequence • Run LOOKUP to build side-chains into new density

  17. Issues: • Backbones sometimes diverge (e.g. in loops) • Phase improvement: How to identify and edit-out incorrect parts of the model built? • Avoiding model bias • Our Approach: • Use CAPRA to generate backbone for new density • Match up Ca’s with search model (core of protein) • Identify divergences (no nearby matches) • Fill in gaps with chains from new density

  18. 5.35Å Deletion in model • Method • Generate map around search model (MR solution) • Run CAPRA to generate new backbone • Assign Ca’s (closest match between models, up to 3Å) • Assign new aa identities based on sequence alignment supplied by user ATAAEIAALPRQKVELVDPPFVHAHSQVAEGGPKVVEFTMVI----IVIDDAGTEVHAM... -------ELPVIDAVTTHAPEVPPAI--DRDYPAKVRVKMETVEKTMKMDD-GVEYRYW... • Format restricted (for now) to 2 long lines (or N pairs of lines for N subunits in search model)

  19. –exp(-(r-1)) r • Connect small gaps (len≤5) • Common (including due to alignment errors) • Method 1: Look for a bridge using existing Ca’s • Method 2: Use a fragment library • 4188 9-mers extracted from 238 non-homologous proteins with min RMS of 1.25Å • Superpose edges of each fragment on chain ends, with expected number of missing Ca’s in middle • Select top 25 fragments by RMS (typically in range of 1-2Å) • Evaluate each fragment based on density measured every 0.5Å along fragment • Score(frag) = S –exp(-(r-1))

  20. Run patch to make any remaining connections • More indiscriminant; may skip residues or insert extra atoms not consistent with alignment • Can turn off via --connectivity=conservative • Run ca_refine • reduces variance in inter-Ca distances • Run LOOKUP to build side-chains • Run simulated annealing

  21. Results • 3 MR datasets from Phenix structure library: native search perc sec MR map resomodel ident size str Rtrue corr ------ ----- ----- ---- ---- ----- ---- a2u-globulin 2.5 Å mup 63% 158(x4) alpha 0.20/0.26 0.94 human-otc 2.4 Å a1s 48% 354 mixed 0.23/0.27 0.89 nitrite-reductase 1.7 Å kbv 35% 339 beta 0.26/0.29 0.81 * Rtrue is R-factor after simulated annealing with refined structure * MR map corr is density correl. between initial MR map and final 2Fo-Fc • After building model with textal.build_mr and running simulated annealing: perc num perc map built chains ident Rmod corr ---- ----- ----- ---- ----- a2u-globulin 93% 4/4 98% 0.24/0.30 0.95 human-otc 93% 2 99% 0.30/0.36 0.82 nitrite-reductase 84% 4 93% 0.35/0.39 0.85 * Rmod is R-factor of model built by Textal, after simulated annealing * Map corr is between model 2Fo-Fc and refined 2Fo-Fc density maps * ideal sequence alignments were used based on structural alignments generated using Shindyalov’s CE (Combinatorial Extension) algorithm

  22. a2u-globulin (white) Textal model (green) disordered loop, res 60-64 11 res N-term tail not built

  23. loop not built, res 266-275 human-otc (white) Textal (red, green) C-term not built, res 345-352

  24. human-otc (white) Textal (red, green)

  25. missing loop: res 186-205 nitrite-reductase (white) Textal model (colors) missing term: res 5-10 missing term: res 334-342 missing loop: res 159-170 missing loop: res 29-36

  26. nitrite-reductase (white) kbv (MR solution, purple) large divergent loop small differences loop insertion

  27. Initial Steps Toward Model EvaluationRun SFCHECK on model built…

  28. quality score (Sfcheck) residues (sorted) Identifying errors with SFCHECK • Which combination of values correlates best with errors in model? • Use backbone_density_index from SFCHECK as residue quality score Thr-203 0.092 Gly-226 0.297 Glu-236 0.306 Thr-269 0.354 ...

  29. Residues in purple (50/284) are those with low backbone density index scores (<0.92)

  30. Re-running SA on editted models Hypothesis: impact ofcompleteness versus accuracy of model on R-factor random deletions • Issues: • B-factors • side-chains • lack of HETATMs (2 Cu, 3Cd, 244 HOH in refined structure) • avoid model bias (use omit maps?)

  31. Availability • Phenix command line: textal.build_mr [-c] [--symmetry] [--amplitudes] [--phases] <reflections> <search_model> <alignment_file> textal.build_mr --symmetry=nitrite-redct.inp –amplitudes=FULL_MOD nitrite-reduce.hkl kbv_mr_solution.pdb NR-KBV-align.txt • Python API: from textal.users.tom.textal_mr import MR_build MR_build(reflections=rx,model=mod,alignment=algn,capra_only=True)

  32. Phenix GUI task: (textal/MR_Build):

  33. Future Work Conclusion • TEXTAL can build highly accurate models for Molecular Replacement (completely automatically), with almost perfect coordinates for backbone and side-chains atoms (with the help of simulated annealing), at least in the core (80-90%) • Handle missing domains in the search model • Incorporate better model evaluation methods • Automate the whole improvement cycle

More Related