computing missing loops in automatically resolved x-ray structures itay lotan henry van den bedem...
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Computing Missing Loops in Automatically Resolved X-Ray
Structures
Itay Lotan
Henry van den Bedem (SSRL)
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Bioinformatics core UCSD, SDSC
Crystallomics core TSRI, GNF
Structure Determination Core SSRL Crystal screening / X-ray data collection Structure determination Structure refinement
Funding from NIH Protein Structure Initiative 10 centers Funding for five years from July 2000
Ongoing projects at SDC: Beam line automation:
Sample mounting robotics, automated diffraction quality assessment
Automated structure determination:
Structure Solution Pipeline
Joint Center for Structural Genomics:
Create new technologies to drive high throughput structure determination
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From Model Building to Refinement
Structure Solution Pipeline
Initial Model(s)
Diffraction Images
Final Model
Mos
tly A
utom
ated
Man
ual
• Finalizing model: Labor intensive, time consuming.
• Existing tools to assist in model building unsatisfactory:
1. Produce incorrect configurations2. Lack meaningful scoring algorithm to
rank configurations3. Remain highly interactive – difficult to
integrate in Structure Solution Pipeline
Initial models (RESOLVE, ARP/WARP): Several chains and gaps
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The Problem
We are given:– A density map– A solved structure with a gap (5 – 15 res.)
Goal:– Automatically compute backbone
conformation for the gap region
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Gaps
The structure is solved automatically Gaps appear in areas of “poor” density
– Signal is indistinguishable from noise– Disconnected iso-surfaces – Automatic solver bails out
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Things we can use
The loop-closure constraint What density there is The solved structure The sequence is known (Cβ atoms) Preferred backbone angles
(Ramchandran plots)
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Loop Closure: CCD algorithm
Robot Inverse Kinematics (Wang & Chen ’91)
Protein loops (Canutescu & Dunbrack ’03)
Algorithm:
1.Fix loop at one end
2.Repeat until closure
For each DOF of loop
Minimize closure score for DOF
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CCD for Proteins
Closure score:Sum of squared distances of N, Cα and C atoms of final residue from their target positions
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Our Approach
1. Generate closed loops using density, Ramachandran plot bias and solved structure
2. Optimize highest scoring loops using density and solved structure
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Stage 1: Generate Closed Loops
Perform one big CCD run For residue i:
– Compute closure moves of (φ,ψ) angles– Compute max density of residue i+1
– Combine and bias toward peaks in Ramachandran plot
Weight of closure move is increased gradually
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Stage 2: Loop Optimization
Choose residue i and φ or ψ DOF at random– Apply random change– Use DOFs of residues [i-1,i+2] to close loop using
CCD– Compute new score
Accept change using Metropolis-like criterion Slowly decrease temperature and reduce
StDev of random changes
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Score Density:
Weighted sum of density at atom centers and points away from center along coordinate axes.
Collision:Penalize overlap of loop atoms with solved structure atoms as function penetration depth.
Self Collision:Penalize overlap of atoms in loop
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Local Loop Changes
My CCD method:– Choose DOF at random (from ALL DOFs) with
biases– Compute Direction of change– Move only a little– Allowed change in N-Cα and Cα-C bond lengths,
N-Cα-C angle and Ω angle decreases with distance from optimal value
Repeat until closed or maximum iterations
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3.7Å 0.35Å
8 Residue Loop: Example 1
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8 Residue Loop: Example 2
0.3Å2.79Å
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12 Residue Loop:
1.29Å 0.28Å
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9 Residue Loop:
3Å 0.32Å
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Open Issues
Many parameters that are determined arbitrarily– Annealing regimen– Weight of collision penalty– Acceptance criterion
Have one set of parameters that works for all loops lengths and density qualities