Automated Model-Building with TEXTAL

1
Automated Model-Building with TEXTAL

• Thomas R. Ioerger
• Department of Computer Science
• Texas AM 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 Cas
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 Cas
• 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
Flt1.72,-0.39,1.04,1.55...gt
Flt1.58,0.18,1.09,-0.25...gt
Flt0.90,0.65,-1.40,0.87...gt
Flt1.79,-0.43,0.88,1.52...gt
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 gt textal.log
• lots of intermediate files and logs
• WINTEX Tcl/Tk interface
• creates jobs in sub-directories
• Public Release July 2004
• http//textal.tamu.edu12321
• 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)