The TEXTAL System for Automated Model Building

The TEXTAL System for Automated Model Building

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) The TEXTAL Approach Based on pattern recognition Consider a spherical region of 5 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) 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

Outline of the Process elec. dens. map C-alpha chains (PDB file of predicted CA coords) calculate features in 5A region around each C-alpha; search database for matches

CAPRA LOOKUP initial model (complete coords) structure factors (with est. phases)

1. sequence alignment 2. real-space refinement 3. heuristics to fix backbone Post-Processing atomic coords

CAPRA: C-Alpha Pattern Recognition 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; dont 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; dont 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

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