TEXTAL:%20Applications%20of%20Pattern%20Recognition%20to%20Macromolecular%20Crystallography

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TEXTALApplications of Pattern Recognition to
Macromolecular Crystallography

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

Collaboration with Dr. James C. Sacchettini,
Center for Structural Biology, Texas AM
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Automating Structure Determination

• Typical Steps
• obtain crystals
• collect data (e.g. MAD, at synchrotron)
• determine initial set of phases
• generate electron density map
• density modification/phase refinement
• construct model (atomic coordinates)

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Automating Structure Determination

• Existing computational routines
• heavy atom search, Patterson correlation, solvent
  flattening, maximum likelihood phase combination
• few methods to interpret electron density maps
• requires humans potential bottleneck
• difficulty low res., phase errors, weak density
• must automate for structural genomics and
  rational drug design

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Overview of TEXTAL

• Apply pattern recognition techniques
• Exploit database of previously-solved maps
• Model molecular structures in local regions (e.g.
  spheres of 5 Angstrom radius)
• Intuitive principles
• 1) Have I ever seen a region with a
• pattern of density like this before?
• 2) If so, what were previous
• local atomic coordinates?

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Overview (contd)

• Divide-and-Conquer
• 1) identify alpha-carbon positions
  (chain-tracing)
• 2) model regions around alpha-carbons (CAs),
  including backbone and side-chain atoms
• 3) concatenate local models back together,
  resolve any conflicts
• Database contains many regions centered on CAs
  from previous maps
• 5A radius right for structural repetition

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Overview (contd)

• Database 105 regions from 100 maps
• How to identify closest match (efficiently)???
• Calculate numerical features that represent the
  pattern in each region
• Must be rotation-invariant
• Search can be very fast just compare features

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Overview (contd)
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Database Construction

• Ideally would use solved MAD/MIR maps
• Using back-transformed maps works well
• PDB ? structure factors (include B-factors)
• keep reflections down to 2.8A
• Fourier transform ? electron density map
• 50 proteins from PDBSelect (non-homol.)
• about 50,000 regions
• Feature extraction done offline

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Rotation-Invariant Features

• Average density m(1/n)Sri, where ri is density
  at each lattice point in region
• Other Statistical Features standard deviation,
  kurtosis
• Distant to center of mass
• ltxc,yc,zcgt(1/n)lt Sxiri/m,Syiri/m,Sziri/mgt
• dcen?(xc2 yc2 zc2)

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More Features

• Moments of inertia
• measures dispersion around axes of symmetry in a
  density distribution
• calculate 3x3 inertia matrix
• diagonalize to get eigenvalues
• sort from largest to smallest
• take magnitudes and ratios of moments

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More Features

• Spoke angles
• if region centered on CA, should have 3 spokes
  of density emanating from center
• find best-fit vectors calc. angles among them
• surface area of contours
• connectivity of density/bones in region
• other geometrical features...

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Details of Matching Process

• Feature-based matching
• Euclidean distance metric between feature
  vectors.
• dist(R1,R2)?Swi(Fi(R1)-Fi(R2))2
• Must weight features by relevance
• less-relevant features add noise
• Slider algorithm optimize weights by comparing
  features in matching regions versus mismatches
• Verify selections by density correlation
• requires search for optimal rotation

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Experiments

• Goal evaluate potential of pattern-matching
• Assumption CA positions known
• Procedure
• 1. extract features for each region
• 2. collect top K400 feature-based matches in DB
• 3. calculate density correlation, take best match
• 4. rotate backbonesidechain atoms into position
• 30sec/residue on SGI Origin 2000

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Feature Weights
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Results
1gcn glucagon 1fnb ferredoxin reductase 1tup
p53 tumor suppressor IFABP intestinal fatty
acid binding protein BT back-transformed
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Results
Structural similarity groups Ala
Asp, Asn, Leu Gly
Glu, Gln Pro Arg, Lys,
Met Cys, Ser Phe, Trp, Tyr,
His Ile, Val, Thr
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Results
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Example Portion of 1tup
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Example Glucagon
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Post-Processing Routines

• Concatenate local models per a.a. into PDB
• Detect and repair flips by majority chain
  direction
• Utilize amino acid sequence information
• map chains into known sequence (alignment)
• re-lookup residues based on identity
• Real-space refinement

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CAPRA

• Need to find CAs automatically and accurately
• Bones doesnt identify CAs (except branches)
• Use pattern recognition again
• Extract features for all lattice points inside 1s
  contour, or along trace
• Use neural net to predict distance to true CA
• Training set examples of ltF1,F2gt,Di
• Status currently 1A rms, need to get 0.5-0.8

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Example
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Acknowledgements

• Dr. James C. Sacchettini
• Center for Structural Biology, Texas AM
• Graduate students/post-docs
• Dr. Jon Christopher, Tom Holton, Lydia Tapia
• Funding provided by NIH (GM-59398)
• See our forthcoming paper in Acta Cryst. D