RNA Bioinformatics Beyond the One Sequence-One Structure Paradigm. Peter Schuster

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2 RNA Bioinformatics Beyond the One Sequence-One Structure Paradigm Peter Schuster Institut für Theoretische Chemie, Universität Wien, Austria and The Santa Fe Institute, Santa Fe, New Mexico, USA 2008 Molecular Informatics and Bioinformatics Collegium Budapest,

3 Web-Page for further information:

4 1. Computation of RNA equilibrium structures 2. Inverse folding and neutral networks 3. Evolutionary optimization of structure 4. Suboptimal conformations and kinetic folding

5 1. Computation of RNA equilibrium structures 2. Inverse folding and neutral networks 3. Evolutionary optimization of structure 4. Suboptimal conformations and kinetic folding

6 O 5' - end CH 2 O N 1 5'-end GCGGAUUUAGCUCAGUUGGGAGAGCGCCAGACUGAAGAUCUGGAGGUCCUGUGUUCGAUCCACAGAAUUCGCACCA 3 -end O OH N k = A, U, G, C O P O CH 2 O N 2 Na O O OH O P O CH 2 O N 3 Na O Definition of RNA structure O OH O P O CH 2 O N 4 Na O O OH O P O 3' - end Na O

7 N = 4 n N S < 3 n Criterion: Minimum free energy (mfe) Rules: _ ( _ ) _ {AU,CG,GC,GU,UA,UG} A symbolic notation of RNA secondary structure that is equivalent to the conventional graphs

8 Conventional definition of RNA secondary structures

9 H-type pseudoknot

10 S n n+ = Sn S j= j 1 1 S n j Counting the numbers of structures of chain length n n+1 M.S. Waterman, T.F. Smith (1978) Math.Bioscience 42:

11 Restrictions on physically acceptable mfe-structures: 3 and 2

12 Size restriction of elements: (i) hairpin loop (ii) stack σ λ stack loop n n + = = Ξ = Φ Φ + = Ξ + Φ Ξ = 2 1)/ ( λ σ σ λ m k k m m m k k m k m m m m m S S S S n # structures of a sequence with chain length n Recursion formula for the number of physically acceptable stable structures I.L.Hofacker, P.Schuster, P.F. Stadler Discr.Appl.Math. 89:

13 RNA sequence: GUAUCGAAAUACGUAGCGUAUGGGGAUGCUGGACGGUCCCAUCGGUACUCCA RNA folding: Structural biology, spectroscopy of biomolecules, understanding molecular function Biophysical chemistry: thermodynamics and kinetics Empirical parameters RNA structure of minimal free energy Sequence, structure, and design

14 S 5 (h) S 4 (h) S 3 (h) 0 Free energy G S 6 (h) S 7 (h) S 8 (h) (h) S (h) 1 S 2 (h) S 9 Suboptimal conformations S 0 (h) Minimum of free energy The minimum free energy structures on a discrete space of conformations

15 Elements of RNA secondary structures as used in free energy calculations gij kl + h( nl ) + b ( nb ) + i ( ni G0 = ), stacks base pairs of hairpin loops bulges internal loops L

16 Maximum matching An example of a dynamic programming computation of the maximum number of base pairs Back tracking yields the structure(s). [i,k-1] [ k+1,j ] j i G G C G C G C C C G G C G C C 1 G * * G * * C * * G * * C * * G * * C * * C * * C * * G * * G * * C * * G * * 1 14 C * * 15 C * i i+1 i+2 k j-1 j j+1 X i,k-1 X k+1,j X { X, max (( X + 1 X ρ )} i, j+ 1 = max i, j i k j 1 i, k 1 + k+ 1, j) k, j+ 1 Minimum free energy computations are based on empirical energies

17 1. Computation of RNA equilibrium structures 2. Inverse folding and neutral networks 3. Evolutionary optimization of structure 4. Suboptimal conformations and kinetic folding

18 RNA sequence: GUAUCGAAAUACGUAGCGUAUGGGGAUGCUGGACGGUCCCAUCGGUACUCCA RNA folding: Structural biology, spectroscopy of biomolecules, understanding molecular function Iterative determination of a sequence for the given secondary structure Inverse Folding Algorithm Inverse folding of RNA: Biotechnology, design of biomolecules with predefined structures and functions RNA structure of minimal free energy Sequence, structure, and design

19 Compatibility of sequences and structures

20 Compatibility of sequences and structures

21 Inverse folding algorithm I 0 I 1 I 2 I 3 I 4... I k I k+1... I t S 0 S 1 S 2 S 3 S 4... S k S k+1... S t I k+1 = M k (I k ) and d S (S k,s k+1 ) = d S (S k+1,s t ) - d S (S k,s t ) < 0 M... base or base pair mutation operator d S (S i,s j )... distance between the two structures S i and S j Unsuccessful trial... termination after n steps

22 Approach to the target structure S k in the inverse folding algorithm

23 The inverse folding algorithm searches for sequences that form a given RNA secondary structure under the minimum free energy criterion.

24 Space of genotypes: I = { I, I, I, I,..., I } ; Hamming metric N Space of phenotypes: S = { S, S, S, S,..., S } ; metric (not required) M N M ( I) = j S k -1 G k = ( S ) U I ( I) = S k j j k A mapping and its inversion

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26 1. Computation of RNA equilibrium structures 2. Inverse folding and neutral networks 3. Evolutionary optimization of structure 4. Suboptimal conformations and kinetic folding

27 Structure of andomly chosen initial sequence Phenylalanyl-tRNA as target structure

28 Evolution in silico W. Fontana, P. Schuster, Science 280 (1998),

29 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

30 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

31 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

32 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

33 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

34 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

35 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

36 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

37 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

38 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

39 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

40 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

41 Evolution of RNA molecules as a Markow process and its analysis by means of the relay series

42 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 = / site replication The flowreactor as a device for studies of evolution in vitro and in silico

43 In silico optimization in the flow reactor: Evolutionary Trajectory

44 28 neutral point mutations during a long quasi-stationary epoch Transition inducing point mutations change the molecular structure Neutral point mutations leave the molecular structure unchanged Neutral genotype evolution during phenotypic stasis

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47 A sketch of optimization on neutral networks

48 Randomly chosen initial structure Phenylalanyl-tRNA as target structure

49 Application of molecular evolution to problems in biotechnology

50 1. Computation of RNA equilibrium structures 2. Inverse folding and neutral networks 3. Evolutionary optimization of structure 4. Suboptimal conformations and kinetic folding

51 RNA secondary structures derived from a single sequence

52 An algorithm for the computation of all suboptimal structures of RNA molecules using the same concept for retrieval as applied in the sequence alignment algorithm by M.S. Waterman and T.F. Smith. Math.Biosci. 42: , 1978.

53 An algorithm for the computation of RNA folding kinetics

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

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

56 0 Free energy G Saddle point T {k S { 0 Free energy G T {k S { S k S k "Reaction coordinate" "Barrier tree" Definition of a barrier tree

57 CUGCGGCUUUGGCUCUAGCC...((((...)))) (((.(((...))).))) (((..((...))..))) (((...))) (((((...)))...)) (((...))) ((..(((...)))..)) ((.((...))...)) (((...))) (((...))) ((.(((...))).)) ((((..(...).)))) ((..((...))..)) (((.(((...)).)))) (((.((...)).))) ((((..(...))))) ((...)) (.(((...)))) (((...)).) (((..(...)..))) (((((...)).)..)) (.(((...))).) ((...)) ((...)) ((...)) (((.(((...)))).)) (((.(...)))) (((..((...))))) (..((...))..) (..(((...)))..) S 0 S 1 M.T. Wolfinger, W.A. Svrcek-Seiler, C. Flamm, I.L. Hofacker, P.F. Stadler J.Phys.A: Math.Gen. 37:

58 CUGCGGCUUUGGCUCUAGCC...((((...)))) (((.(((...))).))) (((..((...))..))) (((...))) (((((...)))...)) (((...))) ((..(((...)))..)) ((.((...))...)) (((...))) (((...))) ((.(((...))).)) ((((..(...).)))) ((..((...))..)) (((.(((...)).)))) (((.((...)).))) ((((..(...))))) ((...)) (.(((...)))) (((...)).) (((..(...)..))) (((((...)).)..)) (.(((...))).) ((...)) ((...)) ((...)) (((.(((...)))).)) (((.(...)))) (((..((...))))) (..((...))..) (..(((...)))..) M.T. Wolfinger, W.A. Svrcek-Seiler, C. Flamm, I.L. Hofacker, P.F. Stadler J.Phys.A: Math.Gen. 37:

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

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

61 Design of an RNA switch 1D R 2D GGGUGGAACCACGAGGUUCCACGAGGAACCACGAGGUUCCUCCC 3 13 G D 2D CG CG A A A A C G C G C G C G A U A U A U A U G C G C G C G C U A/G A U 3 G C G C 44 GG R 23 CC 5' kcal mol kcal mol -1 JN1LH R 23 CG G/ A A C G C G U A U A G C G C A A 13 1D G C 2D C G 33 A A C G C G A U A U G C G C U U 3G CCC G G 44 5' kcal mol kcal mol -1

62 Free energy [kcal / mole] J.H.A. Nagel, C. Flamm, I.L. Hofacker, K. Franke, M.H. de Smit, P. Schuster, and C.W.A. Pleij. Nucleic Acids Res. 34: (2006) J1LH barrier tree

63 A ribozyme switch E.A.Schultes, D.B.Bartel, Science 289 (2000),

64 Two ribozymes of chain lengths n = 88 nucleotides: An artificial ligase (A) and a natural cleavage ribozyme of hepatitis- -virus (B)

65 The sequence at the intersection: An RNA molecules which is 88 nucleotides long and can form both structures

66 Two neutral walks through sequence space with conservation of structure and catalytic activity

67 Acknowledgement of support Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Projects No , 10578, 11065, , and Universität Wien Wiener Wissenschafts-, Forschungs- und Technologiefonds (WWTF) Project No. Mat05 Jubiläumsfonds der Österreichischen Nationalbank Project No. Nat-7813 European Commission: Contracts No , (NEST) Austrian Genome Research Program GEN-AU: Bioinformatics Network (BIN) Österreichische Akademie der Wissenschaften Siemens AG, Austria Universität Wien and the Santa Fe Institute

68 Coworkers Walter Fontana, Harvard Medical School, MA Christian Forst, Christian Reidys, Los Alamos National Laboratory, NM Universität Wien Peter Stadler, Bärbel Stadler, Universität Leipzig, GE Jord Nagel, Kees Pleij, Universiteit Leiden, NL Christoph Flamm, Ivo L.Hofacker, Andreas Svrček-Seiler, Universität Wien, AT Stefan Bernhart, Jan Cupal, Lukas Endler, Kurt Grünberger, Michael Kospach, Ulrike Langhammer, Rainer Machne, Ulrike Mückstein, Hakim Tafer, Andreas Wernitznig, Stefanie Widder, Michael Wolfinger, Stefan Wuchty, Dilmurat Yusuf, Universität Wien, AT Ulrike Göbel, Walter Grüner, Stefan Kopp, Jaqueline Weber, Institut für Molekulare Biotechnologie, Jena, GE

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