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 alignment 2. real-space refinement 3. heuristics to fix backbone Outline of the Process elec. dens. map atomic coords structure 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 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; 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,  err ph ) 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