Protein Folding by Robotics
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1 Protein Folding by Robotics 1 TBI Winterseminar 2006 February 21, 2006
2 Protein Folding by Robotics 1 TBI Winterseminar 2006 February 21, 2006
3 Protein Folding by Robotics Probabilistic Roadmap Planning (PRM) [ Thomas, Song, Amato. Protein folding by motion planning. Phys. Biol., 2005 ] 1 TBI Winterseminar 2006 February 21, 2006
4 Aims Find good quality folding paths (into given native structure) no structure prediction! Predict formation orders (of secondary structure) 2 TBI Winterseminar 2006 February 21, 2006
5 Motion planning Motion planning 3 TBI Winterseminar 2006 February 21, 2006
6 Motion planning Motion planning 3 TBI Winterseminar 2006 February 21, 2006
7 Motion planning Motion planning Probabilistic roadmap planing 3 TBI Winterseminar 2006 February 21, 2006
8 Motion planning Motion planning Probabilistic roadmap planing Sampling of configuration space Q 3 TBI Winterseminar 2006 February 21, 2006
9 Motion planning Motion planning Probabilistic roadmap planing Sampling of configuration space Q Connecting nearest configurations by a (simple) local planner 3 TBI Winterseminar 2006 February 21, 2006
10 Motion planning Motion planning Probabilistic roadmap planing Sampling of configuration space Q Connecting nearest configurations by a (simple) local planner Apply graph algorithms to roadmap : Find shortest path 3 TBI Winterseminar 2006 February 21, 2006
11 More on PRM for motion planning tree-like robots 4 TBI Winterseminar 2006 February 21, 2006
12 More on PRM for motion planning tree-like robots (articulated robots) Articulated Joint 4 TBI Winterseminar 2006 February 21, 2006
13 More on PRM for motion planning tree-like robots (articulated robots) Articulated Joint 4 TBI Winterseminar 2006 February 21, 2006
14 More on PRM for motion planning tree-like robots (articulated robots) Articulated Joint configuration = vector of angles configuration space Q = {q q S n } S set of angles n number of angles = degrees of freedom (dof) 4 TBI Winterseminar 2006 February 21, 2006
15 Proteins are Robots (aren t they?) Obvious similarity == 5 TBI Winterseminar 2006 February 21, 2006
16 Proteins are Robots (aren t they?) Obvious similarity ;-) == Our model O O N C C N C C N C C O 5 TBI Winterseminar 2006 February 21, 2006
17 Proteins are Robots (aren t they?) Obvious similarity ;-) == Our model O O N C C N C C N C C O 5 TBI Winterseminar 2006 February 21, 2006
18 Proteins are Robots (aren t they?) Obvious similarity ;-) == Our model O O N C phi C psi N C C N C C O 5 TBI Winterseminar 2006 February 21, 2006
19 Proteins are Robots (aren t they?) Obvious similarity ;-) /== Our model O O N C phi C psi N C C N C C O 5 TBI Winterseminar 2006 February 21, 2006
20 Proteins are Robots (aren t they?) Obvious similarity ;-) /== Our model O O N C phi C psi N C C N C C O Protein == vector of phi and psi angles (treelike robot with 2n dof) possible models range from only backbone up to full atom 5 TBI Winterseminar 2006 February 21, 2006
21 Differences to usual PRM no external obstacles, but self-avoidingness torsion angles quality of paths low energy intermediate states kinetically prefered paths highly probable paths 6 TBI Winterseminar 2006 February 21, 2006
22 Energy Function method can use any potential 7 TBI Winterseminar 2006 February 21, 2006
23 Energy Function method can use any potential Our coarse potential [Levitt. J.Mol.Biol., ] each sidechain by only one atom (zero dof) U tot = 7 TBI Winterseminar 2006 February 21, 2006
24 Energy Function method can use any potential Our coarse potential [Levitt. J.Mol.Biol., ] each sidechain by only one atom (zero dof) U tot = K d {[(d i d 0 ) 2 + dc 2 ] 2 1 d c } restraints first term favors known secondary structure through main chain hydrogen bonds and disulphide bonds 7 TBI Winterseminar 2006 February 21, 2006
25 Energy Function method can use any potential Our coarse potential [Levitt. J.Mol.Biol., ] each sidechain by only one atom (zero dof) U tot = K d {[(d i d 0 ) 2 + dc 2 ] 2 1 d c } + E hp restraints first term favors known secondary structure through main chain hydrogen bonds and disulphide bonds second term hydrophobic effect 7 TBI Winterseminar 2006 February 21, 2006
26 Energy Function method can use any potential Our coarse potential [Levitt. J.Mol.Biol., ] each sidechain by only one atom (zero dof) U tot = K d {[(d i d 0 ) 2 + dc 2 ] 2 1 d c } + E hp restraints first term favors known secondary structure through main chain hydrogen bonds and disulphide bonds second term hydrophobic effect Van der Waals interaction modeled by step function 7 TBI Winterseminar 2006 February 21, 2006
27 Energy Function method can use any potential Our coarse potential [Levitt. J.Mol.Biol., ] each sidechain by only one atom (zero dof) U tot = K d {[(d i d 0 ) 2 + dc 2 ] 2 1 d c } + E hp restraints first term favors known secondary structure through main chain hydrogen bonds and disulphide bonds second term hydrophobic effect Van der Waals interaction modeled by step function All-atom potential: EEF1 [Lazaridis, Karplus. Proteins, ] 7 TBI Winterseminar 2006 February 21, 2006
28 PRM method for Proteins 8 TBI Winterseminar 2006 February 21, 2006
29 PRM method for Proteins Sampling 8 TBI Winterseminar 2006 February 21, 2006
30 PRM method for Proteins Sampling Connecting 8 TBI Winterseminar 2006 February 21, 2006
31 PRM method for Proteins Sampling Connecting Extracting 8 TBI Winterseminar 2006 February 21, 2006
32 Sampling Node Generation Sampling Connecting Extracting 9 TBI Winterseminar 2006 February 21, 2006
33 Node Generation No uniform sampling configuration space too large need biased sampling strategy 10 TBI Winterseminar 2006 February 21, 2006
34 Node Generation No uniform sampling configuration space too large need biased sampling strategy Gaussian sampling centered around native conformation with different STDs 5,10,...,160 ensure representants for different numbers of native contacts 10 TBI Winterseminar 2006 February 21, 2006
35 Node Generation No uniform sampling configuration space too large need biased sampling strategy Gaussian sampling centered around native conformation with different STDs 5,10,...,160 ensure representants for different numbers of native contacts Selection by energy P(accept q) = 1 if E(q) < E min E max E(q) E max E if E min min E(q) E max 0 if E(q) > E max 10 TBI Winterseminar 2006 February 21, 2006
36 More on Node Generation Visualization of Sampling Strategy 11 TBI Winterseminar 2006 February 21, 2006
37 More on Node Generation Visualization of Sampling Strategy Distribution Psi and Phi angles RMSD vs. Energy 11 TBI Winterseminar 2006 February 21, 2006
38 Node Connection Sampling Connecting Extracting 12 TBI Winterseminar 2006 February 21, 2006
39 Connecting Nodes by Local Planner connect configurations in close distance generate N intermediary nodes by local planner 13 TBI Winterseminar 2006 February 21, 2006
40 Connecting Nodes by Local Planner connect configurations in close distance generate N intermediary nodes by local planner 13 TBI Winterseminar 2006 February 21, 2006
41 Connecting Nodes by Local Planner connect configurations in close distance generate N intermediary nodes by local planner 13 TBI Winterseminar 2006 February 21, 2006
42 Connecting Nodes by Local Planner connect configurations in close distance generate N intermediary nodes by local planner 13 TBI Winterseminar 2006 February 21, 2006
43 Connecting Nodes by Local Planner connect configurations in close distance generate N intermediary nodes by local planner 13 TBI Winterseminar 2006 February 21, 2006
44 Connecting Nodes by Local Planner connect configurations in close distance generate N intermediary nodes by local planner 13 TBI Winterseminar 2006 February 21, 2006
45 Connecting Nodes by Local Planner connect configurations in close distance generate N intermediary nodes by local planner P5 P4 P1 P2 P3 assign weights to edges { e E kt if E > 0 P i = 1 if E 0 13 TBI Winterseminar 2006 February 21, 2006
46 Connecting Nodes by Local Planner connect configurations in close distance generate N intermediary nodes by local planner P5 Weight P4 P1 P2 P3 assign weights to edges { e E kt if E > 0 P i = 1 if E 0 Weight = N log(p i ) i=0 13 TBI Winterseminar 2006 February 21, 2006
47 Connecting Nodes by Local Planner connect configurations in close distance generate N intermediary nodes by local planner P5 Weight P4 P1 P2 P3 assign weights to edges { e E kt if E > 0 P i = 1 if E 0 Weight = N log(p i ) i=0 13 TBI Winterseminar 2006 February 21, 2006
48 Extracting Paths Sampling Connecting Extracting 14 TBI Winterseminar 2006 February 21, 2006
49 Extracting Paths Shortest Path extract one shortest path from some starting conformation, one path at a time 15 TBI Winterseminar 2006 February 21, 2006
50 Extracting Paths Shortest Path extract one shortest path from some starting conformation, one path at a time Single Source Shortest Paths (SSSP) extract shortest paths from all starting conformation compute paths simultaneously generate tree of shortest paths (SSSP tree) 15 TBI Winterseminar 2006 February 21, 2006
51 Big Picture Sampling Connecting Extracting 16 TBI Winterseminar 2006 February 21, 2006
52 Studied Proteins Overview of studied proteins, roadmap size, and construction times 17 TBI Winterseminar 2006 February 21, 2006
53 Formation orders formation order of secondary structure for verifying method formation orders can be determined experimentally [ Li, Woodward. Protein Science, ] Pulse labeling Out-exchange prediction of formation orders single paths averaging over multiple paths (SSSP-tree) 18 TBI Winterseminar 2006 February 21, 2006
54 Timed Contact Maps 19 TBI Winterseminar 2006 February 21, 2006
55 Formation Order 20 TBI Winterseminar 2006 February 21, 2006
56 Formation Order no (reported) contradictions between prediction and validation different kind of information from experiment and prediction 20 TBI Winterseminar 2006 February 21, 2006
57 The Proteins G and L Studied in more detail good test case structurally similar: 1α + 4β fold differently Protein G: β-turn 2 forms first Protein L: β-turn 1 forms first 21 TBI Winterseminar 2006 February 21, 2006
58 Comparison of Analysis Techniques β-turn Formation 22 TBI Winterseminar 2006 February 21, 2006
59 Conclusion 23 TBI Winterseminar 2006 February 21, 2006
60 Conclusion PRM can be applied to realistic protein models 23 TBI Winterseminar 2006 February 21, 2006
61 Conclusion PRM can be applied to realistic protein models Introduced method makes verifiable prediction 23 TBI Winterseminar 2006 February 21, 2006
62 Conclusion PRM can be applied to realistic protein models Introduced method makes verifiable prediction Coarse potential is sufficient 23 TBI Winterseminar 2006 February 21, 2006
63 Conclusion PRM can be applied to realistic protein models Introduced method makes verifiable prediction Coarse potential is sufficient Predictions are in good accordance to experimental data 23 TBI Winterseminar 2006 February 21, 2006
64 Conclusion PRM can be applied to realistic protein models Introduced method makes verifiable prediction Coarse potential is sufficient Predictions are in good accordance to experimental data Interesting relations to e.g. computation of barrier trees 23 TBI Winterseminar 2006 February 21, 2006
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