evolutionary optimization at the molecular level
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Evolutionary Optimization at the Molecular Level Peter Schuster - PowerPoint PPT Presentation

Evolutionary Optimization at the Molecular Level Peter Schuster Institut fr Theoretische Chemie, Universitt Wien, Austria and The Santa Fe Institute, Santa Fe, New Mexico, USA Physikalisches Kolloquium TU Wien, 28.11.2005 Web-Page for


  1. Properties of RNA sequence to secondary structure mapping 1. More sequences than structures 2. Few common versus many rare structures 3. Shape space covering of common structures 4. Neutral networks of common structures are connected

  2. Properties of RNA sequence to secondary structure mapping 1. More sequences than structures 2. Few common versus many rare structures 3. Shape space covering of common structures 4. Neutral networks of common structures are connected

  3. RNA 9 :1456-1463, 2003 Evidence for neutral networks and shape space covering

  4. Evidence for neutral networks and intersection of apatamer functions

  5. AUGC , n = 100 Mean length of path h Degree of neutrality λ Unconstrained fold 0.33 > 95 Cofold with one sequence 0.32 75 Cofold with two sequences 0.18 40 Folding constraints, degree of neutrality and lengths of neutral path

  6. 1. RNA sequences and structures 2. Neutral networks 3. Evolutionary optimization of structure 4. Suboptimal structures and kinetic folding 5. Comparison of kinetic folding and evolution

  7. Evolution in silico W. Fontana, P. Schuster, Science 280 (1998), 1451-1455

  8. Replication rate constant : f k = � / [ � + � d S (k) ] � d S (k) = d H (S k ,S � ) Selection constraint : Population size, N = # RNA molecules, is controlled by the flow ≈ ± ( ) N t N N Mutation rate : p = 0.001 / site � replication The flowreactor as a device for studies of evolution in vitro and in silico

  9. Randomly chosen Phenylalanyl-tRNA as initial structure target structure

  10. Genotype-Phenotype Mapping Evaluation of the = � ( ) S { I { S { Phenotype I { ƒ f = ( S ) { { f { Q { f 1 j f 1 Mutation I 1 f 2 f n+1 I 1 I n+1 I 2 f n f 2 I n I 2 f 3 I 3 Q Q I 3 f 3 I { I 4 f 4 f { I 5 I 4 I 5 f 4 f 5 f 5 Evolutionary dynamics including molecular phenotypes

  11. In silico optimization in the flow reactor: Evolutionary Trajectory

  12. 28 neutral point mutations during a long quasi-stationary epoch Transition inducing point mutations Neutral point mutations leave the change the molecular structure molecular structure unchanged Neutral genotype evolution during phenotypic stasis

  13. Evolutionary trajectory Spreading of the population on neutral networks Drift of the population center in sequence space

  14. Spreading and evolution of a population on a neutral network: t = 150

  15. Spreading and evolution of a population on a neutral network : t = 170

  16. Spreading and evolution of a population on a neutral network : t = 200

  17. Spreading and evolution of a population on a neutral network : t = 350

  18. Spreading and evolution of a population on a neutral network : t = 500

  19. Spreading and evolution of a population on a neutral network : t = 650

  20. Spreading and evolution of a population on a neutral network : t = 820

  21. Spreading and evolution of a population on a neutral network : t = 825

  22. Spreading and evolution of a population on a neutral network : t = 830

  23. Spreading and evolution of a population on a neutral network : t = 835

  24. Spreading and evolution of a population on a neutral network : t = 840

  25. Spreading and evolution of a population on a neutral network : t = 845

  26. Spreading and evolution of a population on a neutral network : t = 850

  27. Spreading and evolution of a population on a neutral network : t = 855

  28. Mount Fuji Example of a smooth landscape on Earth

  29. Dolomites Bryce Canyon Examples of rugged landscapes on Earth

  30. End of Walk Fitness Start of Walk Genotype Space Evolutionary optimization in absence of neutral paths in sequence space

  31. End of Walk Adaptive Periods s s e n t i F Random Drift Periods Start of Walk Genotype Space Evolutionary optimization including neutral paths in sequence space

  32. Grand Canyon Example of a landscape on Earth with ‘neutral’ ridges and plateaus

  33. 1. RNA sequences and structures 2. Neutral networks 3. Evolutionary optimization of structure 4. Suboptimal structures and kinetic folding 5. Comparison of kinetic folding and evolution

  34. The Folding Algorithm Master equation A sequence I specifies an energy ordered set of dP ( ) ∑ ∑ ∑ + + + 1 1 1 = m − = m − m ( ) ( ) k P t P t k P P k compatible structures S (I): = ik ki = ik i k = ki 0 0 0 i i i dt = + 0 , 1 , , 1 K k m S (I) = {S 0 , S 1 , … , S m , O } Transition probabilities P ij (t) = Prob {S i → S j } are A trajectory T k (I) is a time ordered series of defined by structures in S (I). A folding trajectory is defined by starting with the open chain O and P ij (t) = P i (t) k ij = P i (t) exp(- ∆ G ij /2RT) / Σ i ending with the global minimum free energy structure S 0 or a metastable structure S k which P ji (t) = P j (t) k ji = P j (t) exp(- ∆ G ji /2RT) / Σ j represents a local energy minimum: ∑ T 0 (I) = { O , S (1) , … , S (t-1) , S (t) , + 2 m Σ = exp(- ∆ G ki /2RT) S (t+1) , … , S 0 } k = ≠ 1 , k k i T k (I) = { O , S (1) , … , S (t-1) , S (t) , The symmetric rule for transition rate parameters is due S (t+1) , … , S k } to Kawasaki (K. Kawasaki, Diffusion constants near the critical point for time depen-dent Ising models . Phys.Rev. 145 :224-230, 1966). Formulation of kinetic RNA folding as a stochastic process

  35. Corresponds to base pair distance : d P ( S 1 , S 2 ) Base pair formation and base pair cleavage moves for nucleation and elongation of stacks

  36. Base pair closure, opening and shift corresponds to Hamming distance: d H ( S 1 , S 2 ) Base pair shift move of class 1: Shift inside internal loops or bulges

  37. Two measures of distance in shape space: Hamming distance between structures, d H (S i ,S j ) and base pair distance, d P (S i ,S j )

  38. (h) S 5 (h) S 1 (h) S 2 (h) (h) 0 S 9 S 7 Free energy G � (h) S 6 Suboptimal conformations Search for local minima in conformation space S h Local minimum

  39. 0 G � y T g { k r 0 e G n e � e e y r F g r e n e e e r F S { S { Saddle point T { k S k S k "Barrier tree" "Reaction coordinate" Definition of a ‚barrier tree‘

  40. CUGCGGCUUUGGCUCUAGCC ....((((........)))) -4.30 (((.(((....))).))).. -3.50 (((..((....))..))).. -3.10 ..........(((....))) -2.80 ..(((((....)))...)). -2.20 ....(((..........))) -2.20 ((..(((....)))..)).. -2.00 ..((.((....))....)). -1.60 ....(((....)))...... -1.60 .....(((........))). -1.50 .((.(((....))).))... -1.40 ....((((..(...).)))) -1.40 .((..((....))..))... -1.00 (((.(((....)).)))).. -0.90 (((.((......)).))).. -0.90 ....((((..(....))))) -0.80 .....((....))....... -0.80 ..(.(((....))))..... -0.60 ....(((....)).)..... -0.60 (((..(......)..))).. -0.50 ..(((((....)).)..)). -0.50 ..(.(((....))).).... -0.40 ..((.......))....... -0.30 ..........((......)) -0.30 ...........((....)). -0.30 (((.(((....)))).)).. -0.20 ....(((.(.......)))) -0.20 ....(((..((....))))) -0.20 ..(..((....))..).... 0.00 .................... 0.00 M.T. Wolfinger, W.A. Svrcek-Seiler, C. Flamm, .(..(((....)))..)... 0.10 I.L. Hofacker, P.F. Stadler. 2004. J.Phys.A: Math.Gen. 37 :4731-4741.

  41. CUGCGGCUUUGGCUCUAGCC ....((((........)))) -4.30 (((.(((....))).))).. -3.50 (((..((....))..))).. -3.10 ..........(((....))) -2.80 ..(((((....)))...)). -2.20 ....(((..........))) -2.20 ((..(((....)))..)).. -2.00 ..((.((....))....)). -1.60 ....(((....)))...... -1.60 .....(((........))). -1.50 .((.(((....))).))... -1.40 ....((((..(...).)))) -1.40 .((..((....))..))... -1.00 (((.(((....)).)))).. -0.90 (((.((......)).))).. -0.90 ....((((..(....))))) -0.80 .....((....))....... -0.80 ..(.(((....))))..... -0.60 ....(((....)).)..... -0.60 (((..(......)..))).. -0.50 ..(((((....)).)..)). -0.50 ..(.(((....))).).... -0.40 ..((.......))....... -0.30 ..........((......)) -0.30 ...........((....)). -0.30 (((.(((....)))).)).. -0.20 ....(((.(.......)))) -0.20 ....(((..((....))))) -0.20 ..(..((....))..).... 0.00 .................... 0.00 M.T. Wolfinger, W.A. Svrcek-Seiler, C. Flamm, .(..(((....)))..)... 0.10 I.L. Hofacker, P.F. Stadler. 2004. J.Phys.A: Math.Gen. 37 :4731-4741.

  42. Arrhenius kinetics M.T. Wolfinger, W.A. Svrcek-Seiler, C. Flamm, I.L. Hofacker, P.F. Stadler. 2004. J.Phys.A: Math.Gen. 37 :4731-4741.

  43. Arrhenius kinetic Exact solution of the master equation M.T. Wolfinger, W.A. Svrcek-Seiler, C. Flamm, I.L. Hofacker, P.F. Stadler. 2004. J.Phys.A: Math.Gen. 37 :4731-4741.

  44. Many suboptimal structures Metastable structures One sequence - one structure Partition function Conformational switches 3.30 3.40 3.10 49 48 47 2.80 46 Free Energy 45 44 42 43 41 40 38 39 37 36 35 34 33 32 31 29 30 28 27 2.60 26 25 24 23 22 21 20 3.10 19 18 17 16 S10 15 13 14 12 S8 3.40 2.90 S9 11 10 9 S7 5.10 3.00 S5 8 S6 7 6 5 S4 4 S3 3 7.40 S2 2 5.90 S1 S0 S0 S0 S1 Minimum free energy structure Suboptimal structures Kinetic structures RNA secondary structures derived from a single sequence

  45. Structure S k G k Neutral Network � G k C k Compatible Set C k The compatible set C k of a structure S k consists of all sequences which form S k as its minimum free energy structure (the neutral network G k ) or one of its suboptimal structures.

  46. Structure S 0 Structure S 1 The intersection of two compatible sets is always non empty: C 0 � C 1 � �

  47. Reference for the definition of the intersection and the proof of the intersection theorem

  48. R 1D 2D GGGUGGAAC CACGAG GUUC CACGAG GAAC CACGAG GUUCCUCCC G 3 13 23 33 44 R 1D 2D 23 13 33 C G C G C G A A A A G/ A A C G C C G G G C G C G C A U A U U A U A A U A U G C G C G C G C G C G C A A U A /G A U G C 13 3 G C G CCC 44 1D 2D C G 33 GG 23 R 5' 3’ A A C G C G -1 -28.6 kcal·mol A U A U -1 -28.2 kcal·mol G C G C U U G C 3 G C An RNA switch G C 44 5' 3’ JN1LH -1 -28.6 kcal·mol J.H.A. Nagel, C. Flamm, I.L. Hofacker, K. Franke, -1 -31.8 kcal·mol M.H. de Smit, P. Schuster, and C.W.A. Pleij. Structural parameters affecting the kinetic competition of RNA hairpin formation, Nucleic Acids Res., in press 2005 .

  49. -26.0 2.89 -28.0 4.88 -30.0 8 6.13 . 6 3.04 3.04 2.97 -32.0 Free energy [kcal / mole] 7 1.49 4 2.14 4 2.14 2.51 2.51 1 . 1 . 50 2 49 47 46 48 -34.0 45 44 3 1.9 41 40 2 4 38 39 4 36 5 7 3 4 3 3 32 1 0 8 3 9 3 3 3 6 7 2 5 4 2 3 2 -36.0 2 1 2 2 22 2 0 9 2 8 1.66 2 1 1 7 6 1 1 5 1 4 3 1.44 2 -38.0 1.46 1 1 1 11 4 4 10 9 . 2 2.36 0 -40.0 . 2 3.4 9 8 7 -42.0 2.44 5 6 2.44 4 -44.0 5.32 3 -46.0 -48.0 2 2.77 J1LH barrier tree -50.0 1

  50. A ribozyme switch E.A.Schultes, D.B.Bartel, Science 289 (2000), 448-452

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