The TEXTAL System for Automated Model Building

Thomas R. Ioerger

Texas A&M University

Role of Automated Model Building

• input: map ==> output: model (coords)

• goal: automation– what level is possible? need for human

judgement/correction for difficult cases?– incorporation in systems like PHENIX– use on beam-lines– detection of NCS; molecular replacement– iteration with phase improvement (Resolve)

• Based on pattern recognition

• Consider a spherical region of 5Å radius...

• Have I ever seen a region of density similar to this in any previously-interpreted map?

• if so, use coordinates of atoms from matched region, translated and rotated

• metric: density correlation, but must be rotation-invariant (optimize orientation)

The TEXTAL Approach

Feature Extraction

• faster distance metric:– weighted Euclidean distance of feature vectors

• examples of (rotation-invariant) features:– standard deviation, other statistics in region – distance to center of mass– moments of inertia, ratios (for symmetry)

• search a database of regions from solved maps, with features extracted off-line

1. sequence alignment2. real-space refinement

3. heuristics to fix backbone

Outline of the Processelec. dens. map

atomic coordsstructure factors(with est. phases)

CAPRA

LOOKUP

Post-Processing

C-alpha chains(PDB file of predicted CA coords)

initial model(complete coords)

calculate features in 5A region around each C-alpha;search database for matches

CAPRA: C-Alpha PatternRecognition Algorithm

1. Map scaling - adjust density so on average, >1.0 captures to 20% of volume, <-1.0 capture bottom 20%

2. Tracing - skeletonization - pseudo-atoms on 0.5A grid; eliminate lowest density pts first; don’t break connectivity

3. Calculate features for 5A region around each pseudo-atom

4. Use neural network to predict distance to nearest C-alpha– trained on features from random pts in 1A contour of known map

5. Select way-points: predicted closest locally, >2.5A apart

6. Link way-points together into C-alpha chains– consider quality of neural net prediction

– prefer longer chains; don’t break off into side-chains

– take secondary structure into account: straightness and helicity

Examples of CAPRA Steps

Example of CA-chains fit by CAPRA

Example of Models Built by Textal

Future Work

• correction by sequence alignment

• characterizing accuracy of Textal as function of: resolution, phase quality– at what point (of refinement) will it work?

– how well will it work? (rmsd, errph)

• iteration with phase improvement

Potential Points of Collaboration

• Tracer as a tool (and density scaling?)

• Using model-building for NCS detection, mask generation

• Interaction with solvent-flattening

Acknowledgements

• James C. Sacchettini– Kreshna Gopal– Reetal Pai– Tod Romo

• funding from National Institutes of Health