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Computing Missing Loops in Automatically Resolved X-Ray Structures Itay Lotan Henry van den Bedem...
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Transcript of Computing Missing Loops in Automatically Resolved X-Ray Structures Itay Lotan Henry van den Bedem...
Computing Missing Loops in Automatically Resolved X-Ray
Structures
Itay Lotan
Henry van den Bedem (SSRL)
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
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
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
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
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)
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
CCD for Proteins
Closure score:Sum of squared distances of N, Cα and C atoms of final residue from their target positions
Our Approach
1. Generate closed loops using density, Ramachandran plot bias and solved structure
2. Optimize highest scoring loops using density and solved structure
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
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
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
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
3.7Å 0.35Å
8 Residue Loop: Example 1
8 Residue Loop: Example 2
0.3Å2.79Å
12 Residue Loop:
1.29Å 0.28Å
9 Residue Loop:
3Å 0.32Å
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