CAPRA: C-Alpha Pattern Recognition Algorithm

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CAPRAC-Alpha Pattern Recognition Algorithm

• Thomas R. Ioerger
• Department of Computer Science
• Texas AM University

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Overview of CAPRA

• goal predict CA chains from density map
• not just tracing - more than Bones
• desire 11 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.)

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What can you do with CA chains?

• build-in side-chain and backbone atoms
• TEXTAL, Segment-Match Modeling (Levitt), Holm and
  Sander
• recognize fold from secondary structure
• identify candidates for molecular replacement
• evaluate map quality (num/len of chains)
• density modification
• create poly-alanine backbone and use it to do
  phase recombination

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Role in Automated Model Building

• Model building is one of the bottlenecks in
  high-throughput Structural Genomics
• Automation is needed

reflections
PHENIX
(ha/dm/ncs)
map
TEXTAL
model
CA chains
refinement
CAPRA
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Steps in CAPRA
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Examples of CAPRA Steps
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Tracer









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Neural Network
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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

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Forward Propagation
Backward Propagation
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Selection of Candidate C-alphas

• 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
• dont 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)

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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 (lt20 candidates), find
  longest internal chain with good atoms
• for large clusters (gt20 candidates),
  incrementally clip branch points using heuristics

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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)

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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
• straightnessgt0.8 gt potential beta-strand
• evaluate helicity
• average absolute deviation of angles and torsions
  along 7-mer from ideal values (95º and 50º)
• helicitylt20 gt potential alpha-helix

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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

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Results
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Analysis of RMS by Sec. Struct. (DSSP)
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Example of CA-chains for CzrA fit by CAPRA
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Results for MVK
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Effect of Resolution

• IF5a
• initial map 2.1A, RMS error 1.23A
• limited map 2.8A, RMS error 0.86A
• PCAa (2Fo-Fc)
• initial map 2.0A, RMS error 1.1A
• limited map 2.8A, RMS error 0.82A

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Effect of Density Modification

• anecdotal evidence from ICL
• before DM many short, broken chains
• after DM longer chains, reasonable model
• hard to quantify, but the moral is
• the accuracy of CAPRA results depends on
  quality of density, and CAPRA might not give
  useful results in noisy maps
• experiments with blurring maps
• convolution with Gaussian by FFT

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Future Work

• build poly-alanine
• must determine directionality
• currently done as part of TEXTAL (fits backbone
  carbonyls as well as side-chain atoms)
• connect ends of chains
• improve robustness to breaks in density
• use partial models to improve phases and hence
  make better maps (iteratively)
• a new form of density modification?

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Related Approaches

• Resolve (Terwilliger)
• template convolution search, max. likelihood
• MAID (D. Levitt)
• density correlation search, grow ends
• Critical-point analysis (Glasgow/Fortier)
• ARP/wARP (Perrakis and Lamzin)
• MAIN (D. Turk)
• chiral carbons iterate extend ends, phase
  recomb.
• X-Powerfit (T. Oldfield, MSI)

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Availability

• on pompano, add /xray/textal/bin/capra to your
  path
• run capra ltproteingt where ltproteingt.xplor is
  your map in X-PLOR fmt
• map should cover at least one whole molecule,
  though smallerfaster
• takes a minutes to an hour (especially for
  feature calculations)
• any space group unit cell
• resolution 2.2-3.2A, 2.8A recommended
• remember quality of density must be high, e.g.
  post- solvent-flattening, etc.

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Acknowledgements

• Funding
• National Institutes of Health
• Welch Foundation
• People
• Dr. James C. Sacchettini
• The TEXTAL Group!
• Tod Romo
• Kreshna Gopal
• Reetal Pai