1. TEXTAL: Applications of Pattern Recognition to Macromolecular Crystallography
Dr. Thomas R. Ioerger
Department of Computer Science
Texas A&M University
Collaboration with: Dr. James C. Sacchettini,
Center for Structural Biology, Texas A&M
7/2/2019

2. 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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3. 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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4. 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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5. Overview (cont’d) 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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6. Overview (cont’d) 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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7. Overview (cont’d)
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8. 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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9. 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:
<xc,yc,zc>=(1/n)< Sxiri/m,Syiri/m,Sziri/m>
dcen=(xc2+ yc2+ zc2)
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10. 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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11. More Features Spoke angles surface area of contours
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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12. 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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13. Experiments Goal: evaluate potential of pattern-matching
Assumption: CA positions known
Procedure
1. extract features for each region
2. collect top K=400 feature-based matches in DB
3. calculate density correlation, take best match
4. rotate backbone+sidechain atoms into position
~30sec/residue on SGI Origin 2000
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14. Feature Weights
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15. Results 1gcn = glucagon 1fnb = ferredoxin reductase
1tup = p53 tumor suppressor
IFABP = intestinal fatty acid binding protein
BT = back-transformed
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16. 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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17. Results
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18. Example: Portion of 1tup
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19. Example: Glucagon
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20. 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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21. CAPRA Need to find CAs automatically and accurately
Bones doesn’t 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 {<F1,F2…>,Di}
Status: currently 1A rms, need to get
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22. Example
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23. See our forthcoming paper in: Acta Cryst. D
Acknowledgements
Dr. James C. Sacchettini
Center for Structural Biology, Texas A&M
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
7/2/2019