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Automated Model Building with TEXTAL: Overview and Techniques for Protein Structure Interpretation

Learn about TEXTAL, an automated model-building program optimized for medium-resolution electron density maps in protein crystallography. Explore its main stages, including CAPRA and LOOKUP algorithms, and post-processing routines. See examples of protein model fitting and find out how TEXTAL uses numeric density features to match patterns efficiently. Discover the innovative interface options and future directions in macromolecular model-building.

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Automated Model Building with TEXTAL: Overview and Techniques for Protein Structure Interpretation

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  1. Automated Model-Building with TEXTAL Thomas R. Ioerger Department of Computer Science Texas A&M University

  2. Overview of TEXTAL • Automated model-building program • Can we automate the kind of visual processing of patterns that crystallographers use? • Intelligent methods to interpret density, despite noise • Exploit knowledge about typical protein structure • Focus on medium-resolution maps • optimized for 2.8A (actually, 2.6-3.2A is fine) • typical for MAD data (useful for high-throughput) • other programs exist for higher-res data (ARP/wARP) Electron density map (not structure factors) Protein model (may need refinement) TEXTAL

  3. Main Stages of TEXTAL electron density map CAPRA build-in side-chain and main-chain atoms locally around each Ca Reciprocal-space refinement/DM Ca chains LOOKUP example: real-space refinement model (initial coordinates) Human Crystallographer (editing) Post-processing routines model (final coordinates)

  4. CAPRA: C-Alpha Pattern-Recognition Algorithm tracing Neural network: estimates which pseudo-atoms are closest to true Ca’s linking

  5. Example of Ca-chains fit by CAPRA Rat a2 urinary protein (P. Adams) data: 2.5A MR map generated at 2.8A % built: 84% # chains: 2 lengths: 47, 88 RMSD: 0.82A

  6. Stage 2: LOOKUP • LOOKUP is based on Pattern Recognition • Given a local (5A-spherical) region of density, have we seen a pattern like this before (in another map)? • If so, use similar atomic coordinates. • Use a database of maps with known structures • 200 proteins from PDB-Select (non-redundant) • back-transformed (calculated) maps at 2.8A (no noise) • regions centered on 50,000 Ca’s • Use feature extraction to match regions efficiently • feature (e.g. moments) represent local density patterns • features must be rotation-invariant (independent of 3D orientation) • use density correlation for more precise evaluation

  7. Examples of Numeric Density Features Distance from center-of-sphere to center-of-mass Moments of inertia - relative dispersion along orthogonal axes Geometric features like “Spoke angles” Local variance and other statistics TEXTAL uses 19 distinct numeric features to represent the pattern of density in a region, each calculated over 4 different radii, for a total of 76 features.

  8. F=<1.72,-0.39,1.04,1.55...> F=<1.58,0.18,1.09,-0.25...> F=<0.90,0.65,-1.40,0.87...> F=<1.79,-0.43,0.88,1.52...>

  9. The LOOKUP Process Find optimal rotation Database of known maps Region in map to be interpreted

  10. Stage 3: Post-Processing

  11. Interfaces for Using TEXTAL • Stand-alone commands and scripts • capra-scale prot.xplor prot-scaled.xplor • neotex.sh myprotein > textal.log • lots of intermediate files and logs… • WINTEX: Tcl/Tk interface • creates jobs in sub-directories • Public Release: July 2004 • http://textal.tamu.edu:12321 • Integrated into Phenix • http://phenix-online.org • Python module • model-building tasks in GUI

  12. Gallery of Examples

  13. Conclusions • Pattern recognition is a successful technique for macromolecular model-building • Future directions: • building ligands, co-factors, etc. • recognizing disulfide bridges • phase improvement (iterating with refinement) • loop-building • further integration with Phenix • Intelligent Agent-based methods for guiding/automating model-building • interactive graphics for specialized needs (e.g. fixing chains, editing identities)

  14. Acknowledgements • Funding: • National Institutes of Health • People: • James C. Sacchettini • Kevin Childs, Kreshna Gopal, Lalji Kanbi, Erik McKee, Reetal Pai, Tod Romo • Our association with the PHENIX group: • Paul Adams (Lawrence Berkeley National Lab) • Randy Read (Cambridge University) • Tom Terwilliger (Los Alamos National Lab)

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