TEXTAL: A System for Automated Model Building Based on Pattern Recognition Thomas R. Ioerger Department of Computer Science Texas A&M University

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

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CAPRA: C-Alpha Pattern Recognition Algorithm

Overview of CAPRA goal: predict CA chains from density map not just “tracing” - more than Bones desire 1:1 correspondence, ~3.8A apart based on principles of pattern recognition –use neural net to estimate which pseudo-atoms in trace “look” closest to true C-alphas –use feature extraction to capture 3D patterns in density for input to neural net –use other heuristics for “linking” together into chains, including geometric analysis (s.s.)

CAPRA: C-Alpha Pattern-Recognition Algorithm Tracer - remove lattice points from map (lowest density first) without breaking connectivity Neural nework - for each pseudo atom, extract features, input to network, predict distances to CAs (1:10 in trace), trained on example points in real maps Linking - desire long chains, good CA predictions (not in side-chains), “structurally plausible” (e.g. linear, helical) Density Trace Neural Network Linking into C-alpha chains pseudo atoms predictions of distance to true CA map C-alpha coordinates

Steps in CAPRA

Examples of CAPRA Steps

Tracer

Neural Network

Feature Extraction characterize 3D patterns in local density must be “rotation invariant” examples: –average density in region –standard deviation, kurtosis... –distance to center of mass –moments of inertia, ratios of moments –“spoke angles” calculated over spheres of 3A and 4A radius

Forward Propagation: Backward Propagation:

Selection of Candidate C-alpha’s method: –pick candidates in order of lowest predicted distance first, –among all pseudo-atoms in trace, –as long as not closer than 2.5A notes: –no 3.8A constraint; distance can be as high as 5A –don’t rely on branch points (though often near) –picked in random order throughout map –initially covers whole map, including side-chains and disconnected regions (e.g. noise in solvent)

Linking into Chains initial connectivity of CA candidates based on the trace “over-connected” graph - branches, cycles... start by computing connected components (islands, or clusters) two strategies: –for small clusters (<=20 candidates), find longest internal chain with “good” atoms –for large clusters (>20 candidates), incrementally clip branch points using heuristics

Extracting Chains from Small Clusters exhaustive depth-first search of all paths scoring function: –length –penalty for inclusion of points with high predicted distance to true CA by neural net –preference for following secondary structure (locally straight or helical)

Secondary Structure Analysis generate all 7-mers (connected fragments of candidate CAs of length 7) evaluate “straightness” –ratio of sum of link lengths to end-to-end distance –straightness>0.8 ==> potential beta-strand evaluate “helicity” –average absolute deviation of angles and torsions along 7-mer from ideal values (95º and 50º) –helicity potential alpha-helix

Handling Large Clusters start by breaking cycles (near “bad” atoms) clip links at branch points till only linear chains remain clip the most “obvious” links first, e.g. –if other two links are part of sec. struct. –if clipped branch has “bad” atom nearby –if clipped branch is small and other 2 are large ?? ?

Example of CA-chains for CzrA fit by CAPRA

Results for MVK

Results

Availability Textal web site: – –server-side processing –free access to Capra –beta-testing of Textal To contact us,

Acknowledgements Funding –National Institutes of Health –Welch Foundation People –Dr. James C. Sacchettini –The rest of the TEXTAL Group: Tod Romo Kreshna Gopal Reetal Pai