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

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, A is fine) –typical for MAD data (useful for high-throughput) –other programs exist for higher-res data (ARP/wARP) Overview of TEXTAL Electron density map (not structure factors) TEXTAL Protein model (may need refinement)

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

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

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

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 C  ’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

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.

F=

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

Stage 3: Post-Processing

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 – Integrated into Phenix – –Python module –model-building tasks in GUI

Gallery of Examples

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)

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)