By Jerome Barraquand and Jean-Claude Latombe Presenter: Yubin Zou
Robotics Algorithms for the Study of Protein Structure and Motion Based on Itay Lotan’s PhD...
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Robotics Algorithms for the Study of
Protein Structure and Motion
Based on Itay Lotan’s PhD
Jean-Claude LatombeComputer Science Department
Stanford University
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Unfolded (denatured) state
Folded (native) stateMany pathways
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Loops connect helices and strands
Folded State
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amino-acid(residue)
peptide bonds
Protein Sequence Structure
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Kinematic Linkage Model
Conformational space
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Molecule Robot
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Why Studying Proteins?
They perform many vital functions, e.g.:• catalysis of reactions • storage of energy• transmission of signals • building blocks of muscles
They are linked to key biological problems that raise major computational challenges
mostly due to their large sizes (100s to several 1000s of atoms), many degrees of kinematic freedom, and their huge number (millions)
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Two problems Structure determination from
electron density maps• Inverse kinematics techniques
[Itay Lotan, Henry van den Bedem, Ashley Deacon (Joint Center for Structural Genomics)]
Energy maintenance during Monte Carlo simulation• Distance computation techniques
[Itay Lotan, Fabian Schwarzer, and Danny Halperin (Tel Aviv University)]
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Structure Determination: X-Ray Crystallography
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Software Software systems: RESOLVE, TEXTAL, ARP/wARP, MAID
• 1.0Å < d < 2.3Å ~ 90% completeness• 2.3Å ≤ d < 3.0Å ~ 67% completeness (varies widely)1
Manually completing a model:
• Labor intensive, time consuming• Existing tools are highly
interactive
JCSG: 43% of data sets 2.3Å
1Badger (2003) Acta Cryst. D59
Model completion is high-throughput bottleneck
1.0Å 3.0Å
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The Completion Problem Input:
• Electron-density map• Partial structure•Two anchor residues•Amino-acid sequence of missing fragment (typically 4 – 15 residues long)
Output: • Ranked conformations Q of fragment that
- Respect the closure constraint- Maximize target function T(Q) measuring fit with
electron-density map- No atomic clashes
Main part of protein (f olded)
Protein f ragment (f uzzy map)
Anchor 1(3 atoms)
Anchor 2(3 atoms)
Main part of protein (f olded)
Protein f ragment (f uzzy map)
Anchor 1(3 atoms)
Anchor 2(3 atoms)
Partial structure(folded)
(Inverse Kinematics)
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Two-Stage IK Method
1. Candidate generations Closed fragments
2. Candidate refinement Optimize fit with EDM
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Stage 1: Candidate Generation
1. Generate a random conformation of fragment (only one end attached to anchor)
2. Close fragment (i.e., bring other end to second anchor) using Cyclic Coordinate Descent (CCD) (Wang & Chen ’91, Canutescu & Dunbrack ’03)
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fixed end
moving end
Closure Distance
Closure Distance: 2 22
S N N C C C C
Compute
+ bias toward avoiding steric clashes
s.t. 0ii
Sq
q
A.A. Canutescu and R.L. Dunbrack Jr.Cyclic coordinate descent: A robotics algorithm for protein loop closure. Prot. Sci. 12:963–972, 2003.
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Exact Inverse Kinematics
Repeat for each conformation of a closed fragment:
1. Pick 3 amino-acids at random (3 pairs of - angles)
2. Apply exact IK solver to generate all IK solutions [Coutsias et al, 2004]
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TM0813
GLU-83
GLY-96
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Stage 2: Candidate Refinement
1-D manifold
Target function T (Q) measuring quality of the fit with the EDM
Minimize T while retaining closure Closed conformations lie on a self-motion
manifold of lower dimension
d3d2
d1(1,2,3)
Null space
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Closure and Null Space dX = J dQ, where J is the 6n Jacobian
matrix (n > 6) Null space {dQ | J dQ = 0} has dim = n – 6 N: orthonormal basis of null space dQ = NNT T(Q)
X
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dX U66 VT6n dQ66
=
Computation of NSVD of J
12
6
Gram-Schmidt orthogonalization
0
(n-6) basis N of null space
NT
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Refinement Procedure
Repeat until minimum of T is reached: 1. Compute J and N at current Q2. Compute T at current Q
(analytical expression of T + linear-time recursive computation [Abe et al., Comput. Chem., 1984])
3. Move by small increment along dQ = NNT T
(+ Monte Carlo / simulated annealing protocol to deal with local minima)
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TM0813
GLU-83
GLY-96
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Tests #1: Artificial Gaps
TM1621 (234 residues) and TM0423 (376 residues), SCOP classification a/b
Complete structures (gold standard) resolved with EDM at 1.6Å resolution
Compute EDM at 2, 2.5, and 2.8Å resolution
Remove fragments and rebuild
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TM1621 103 Fragments from TM1621 at 2.5Å
Produced by H. van den Bedem
Long Fragments:
12: 96% < 1.0Å aaRMSD15: 88% < 1.0Å aaRMSD
Short Fragments:
100% < 1.0Å aaRMSD
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Example: TM0423PDB: 1KQ3, 376 res.2.0Å resolution12 residue gapBest: 0.3Å aaRMSD
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Tests #2: True Gaps Structure computed by RESOLVE Gaps completed independently (gold
standard) Example: TM1742 (271 residues) 2.4Å resolution; 5 gaps left by RESOLVE
Length Top scorer
4 0.22Å
5 0.78Å
5 0.36Å
7 0.72Å
10 0.43Å
Produced by H. van den Bedem
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TM1621
Green: manually completed conformation
Cyan: conformation computed by stage 1
Magenta: conformation computed by stage 2
The aaRMSD improved by 2.4Å to 0.31Å
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Current/Future Work
A
B
Software actively being used at the JCSG
What about multi-modal loops?
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TM0755: data at 1.8Å 8-residue fragment crystallized in 2 conformations Overlapping density: Difficult to interpret
manually
Algorithm successfully identified and built both conformations
A323Hist
A316Ser
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Current/Future Work
A
B
Software actively being used at the JCSG
What about multi-modal loops?
Fuzziness in EDM can then be exploited
Use EDM to infer probability measure over the conformation space of the loop
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Amylosucrase
J. Cortés, T. Siméon, M. Renaud-Siméon, and V. Tran. J. Comp. Chemistry, 25:956-967, 2004
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Energy maintenance during Monte Carlo simulation
joint work with Itay Lotan, Fabian Schwarzer, and Dan Halperin1
1 Computer Science Department, Tel Aviv University
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Random walk through conformation space At each attempted step:
• Perturb current conformation at random• Accept step with probability:
The conformations generated by an arbitrarily long MCS are Boltzman distributed, i.e.,
#conformations in V ~
/( ) min 1, bE k TP accept e
Monte Carlo Simulation (MCS)
E
-kT
Ve dV
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Used to:• sample meaningful distributions of conformations • generate energetically plausible motion pathways
A simulation run may consist of millions of steps
energy must be evaluated a large number of times
Problem: How to maintain energy efficiently?
Monte Carlo Simulation (MCS)
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Energy Function E = bonded terms
+ non-bonded terms + solvation terms
Bonded terms - O(n)
Non-bonded terms - E.g., Van der Waals and electrostatic- Depend on distances between pairs of atoms - O(n2) Expensive to compute
Solvation terms- May require computing molecular surface
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Non-Bonded Terms Energy terms go to 0 when distance
increases Cutoff distance (6 - 12Å)
vdW forces prevent atoms from bunching up Only O(n) interacting pairs [Halperin&Overmars 98]
Problem: How to find interacting pairswithout enumerating all atom pairs?
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Grid Method
dcutoff
Subdivide 3-space into cubic cells
Compute cell that contains each atom center
Represent grid as hashtable
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Grid Method
dcutoff Θ(n) time to build grid O(1) time to find
interactive pairs for each atom
Θ(n) to find all interactive pairs of atoms [Halperin&Overmars, 98]
Asymptotically optimal in worst-case
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Can we do better on average?
Few DOFs are changed at each MC step
Number kof DOF changes
0 10 20 305
simulationof 100,000attempted steps
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Can we do better on average?
Few DOFs are changed at each MC step Proteins are long chain kinematics
Long sub-chains stay rigid at each step Many interacting pairs of atoms are unchanged Many partial energy sums remain constant
Problem: How to find new interacting pairs and retrieve unchanged partial sums?
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Two New Data Structures
1. ChainTree Fast detection of interacting atom pairs
2. EnergyTree Retrieval of unchanged partial energy sums
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ChainTree(Twofold Hierarchy: BVs +
Transforms)
links
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TNO
TJK
TAB
joints
ChainTree(Twofold Hierarchy: BVs +
Transforms)
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Updating the ChainTree
Update path to root:– Recompute transforms that “shortcut” the DOF change– Recompute BVs that contain the DOF change– O(k log2(2n/k)) work for k changes
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Finding Interacting Pairs
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Finding Interacting Pairs
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Finding Interacting Pairs
Do not search inside rigid sub-chains (unmarked nodes)
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Finding Interacting Pairs
Do not search inside rigid sub-chains (unmarked nodes)
Do not test two nodes with no marked node between them
New interacting pairs
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EnergyTree
E(N,N)
E(J,L)
E(K.L)
E(L,L)
E(M,M)
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EnergyTree
E(N,N)
E(J,L)
E(K.L)
E(L,L)
E(M,M)
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Complexity
n : total number of DOFs k : number of DOF changes at each MCS step k << n
Complexity of: updating ChainTree: O(k log2(2n/k)) finding interacting pairs: O(n4/3)
but performs much better in practice!!!
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Experimental Setup
Energy function: Van der Waals Electrostatic Attraction between native contacts Cutoff at 12Å
300,000 steps MCS with Grid and ChainTree
Steps are the same with both methods Early rejection for large vdW terms
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Results: 1-DOF change
(68) (144) (374) (755)# amino acids
3.5
12.5
5.8
7.8
speedup
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Results: 5-DOF change
(68) (144) (374) (755)
2.2
3.4
4.5
5.9
speedup
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Two-Pass ChainTree (ChainTree+)
1st pass: small cutoff distance to detect steric clashes2nd pass: normal cutoff distance
>5Tests around native state
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Interaction with Solvent
Implicit solvent model: solvent as continuous medium, interface is solvent-accessible surface
E. Eyal, D. Halperin. Dynamic Maintenance of Molecular Surfaces underConformational Changes. http://www.give.nl/movie/publications/telaviv/EH04.pdf
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Summary
Inverse kinematics techniques Improve structure determination from fuzzy electron density maps
Collision detection techniques Speedup energy maintenance during Monte Carlo simulation
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About Computational Biology
Computational Biology is more than mimicking nature (e.g., performing Molecular Dynamic simulation)
One of its goals is to achieve algorithmic efficiency by exploiting properties of molecules, e.g.: • Atoms cannot bunch up together• Forces have relatively short ranges • Proteins are long kinematic chains