RNA – A Model for Molecular Evolution Peter Schuster Institut für Theoretische Chemie und Molekulare Strukturbiologie der Universität Wien GDCh-Jahrestagung 2003 Fachgruppe Biochemie München, 09.10.2003
Web-Page for further information: http://www.tbi.univie.ac.at/~pks
RNA as adapter molecule RNA is the catalytic subunit in RNA as scaffold for supramolecular RNA as transmitter of genetic information supramolecular complexes complexes DNA transcription ... CUG ... ...AGAGCGCCAGACUGAAGAUCUGGAGGUCCUGUGUUC... leu GAC messenger- RNA genetic code translation protein ribosome RNA as working copy of genetic information ? ? ? ? ? RNA as catalyst RNA RNA is modified by epigenetic control RNA editing Alternative splicing of messenger RNA ribozyme RNA as regulator of gene expression RNA as carrier of genetic information The RNA world as a precursor of RNA viruses and retroviruses the current DNA + protein biology RNA as information carrier in evolution in vitro and evolutionary biotechnology Functions of RNA molecules gene silencing by small interfering RNAs
1. Experiments on controlled evolution and RNA replication 2. Sequence-structure maps, neutral networks, and intersections 3. Optimization in the RNA model 4. What we can learn from molecules for evolution proper
1. Experiments on controlled evolution and RNA replication 2. Sequence-structure maps, neutral networks, and intersections 3. Optimization in the RNA model 4. What we can learn from molecules for evolution proper
Bacterial Evolution S. F. Elena, V. S. Cooper, R. E. Lenski. Punctuated evolution caused by selection of rare beneficial mutants . Science 272 (1996), 1802-1804 D. Papadopoulos, D. Schneider, J. Meier-Eiss, W. Arber, R. E. Lenski, M. Blot. Genomic evolution during a 10,000-generation experiment with bacteria . Proc.Natl.Acad.Sci.USA 96 (1999), 3807-3812
lawn of E.coli 24 h 24 h nutrient agar Serial transfer of Escherichia coli cultures in Petri dishes � 1 day 6.67 generations � 1 month 200 generations � 1 year 2400 generations
1 year Epochal evolution of bacteria in serial transfer experiments under constant conditions S. F. Elena, V. S. Cooper, R. E. Lenski. Punctuated evolution caused by selection of rare beneficial mutants . Science 272 (1996), 1802-1804
Hamming distance to ancestor 25 20 15 10 5 2000 4000 6000 8000 Generations Time Variation of genotypes in a bacterial serial transfer experiment D. Papadopoulos, D. Schneider, J. Meier-Eiss, W. Arber, R. E. Lenski, M. Blot. Genomic evolution during a 10,000-generation experiment with bacteria . Proc.Natl.Acad.Sci.USA 96 (1999), 3807-3812
Evolution of RNA molecules based on Q β phage D.R.Mills, R.L.Peterson, S.Spiegelman, An extracellular Darwinian experiment with a self-duplicating nucleic acid molecule . Proc.Natl.Acad.Sci.USA 58 (1967), 217-224 S.Spiegelman, An approach to the experimental analysis of precellular evolution . Quart.Rev.Biophys. 4 (1971), 213-253 C.K.Biebricher, Darwinian selection of self-replicating RNA molecules . Evolutionary Biology 16 (1983), 1-52 G.Bauer, H.Otten, J.S.McCaskill, Travelling waves of in vitro evolving RNA. Proc.Natl.Acad.Sci.USA 86 (1989), 7937-7941 C.K.Biebricher, W.C.Gardiner, Molecular evolution of RNA in vitro . Biophysical Chemistry 66 (1997), 179-192 G.Strunk, T.Ederhof, Machines for automated evolution experiments in vitro based on the serial transfer concept . Biophysical Chemistry 66 (1997), 193-202
RNA sample Time 0 1 2 3 4 5 6 69 70 � Stock solution: Q RNA-replicase, ATP, CTP, GTP and UTP, buffer The serial transfer technique applied to RNA evolution in vitro
Reproduction of the original figure of the β serial transfer experiment with Q RNA D.R.Mills, R,L,Peterson, S.Spiegelman, An extracellular Darwinian experiment with a self-duplicating nucleic acid molecule . Proc.Natl.Acad.Sci.USA 58 (1967), 217-224
Decrease in mean fitness due to quasispecies formation The increase in RNA production rate during a serial transfer experiment
No new principle will declare itself from below a heap of facts. Sir Peter Medawar, 1985
Theory of molecular evolution M.Eigen, Self-organization of matter and the evolution of biological macromolecules . Naturwissenschaften 58 (1971), 465-526 C.J.Thompson, J.L.McBride, On Eigen's theory of the self-organization of matter and the evolution of biological macromolecules . Math. Biosci . 21 (1974), 127-142 B.L.Jones, R.H.Enns, S.S.Rangnekar, On the theory of selection of coupled macromolecular systems. Bull.Math.Biol . 38 (1976), 15-28 M.Eigen, P.Schuster, The hypercycle. A principle of natural self-organization. Part A: Emergence of the hypercycle . Naturwissenschaften 58 (1977), 465-526 M.Eigen, P.Schuster, The hypercycle. A principle of natural self-organization. Part B: The abstract hypercycle . Naturwissenschaften 65 (1978), 7-41 M.Eigen, P.Schuster, The hypercycle. A principle of natural self-organization. Part C: The realistic hypercycle . Naturwissenschaften 65 (1978), 341-369 J.Swetina, P.Schuster, Self-replication with errors - A model for polynucleotide replication. Biophys.Chem. 16 (1982), 329-345 J.S.McCaskill, A localization threshold for macromolecular quasispecies from continuously distributed replication rates . J.Chem.Phys. 80 (1984), 5194-5202 M.Eigen, J.McCaskill, P.Schuster, The molecular quasispecies . Adv.Chem.Phys. 75 (1989), 149-263 C. Reidys, C.Forst, P.Schuster, Replication and mutation on neutral networks . Bull.Math.Biol. 63 (2001), 57-94
I 1 I j + Σ Φ dx / dt = f Q ji x - x f j Q j1 i j j j i I j I 2 + Σ i Φ = Σ ; Σ = 1 ; f x x Q ij = 1 j j i j j � i =1,2,...,n ; f j Q j2 [Ii] = xi 0 ; I i I j + [A] = a = constant f j Q ji l -d(i,j) d(i,j) I j (A) + I j Q = (1- ) p p + I j ij f j Q jj p .......... Error rate per digit l ........... Chain length of the f j Q jn polynucleotide I j d(i,j) .... Hamming distance I n + between Ii and Ij Chemical kinetics of replication and mutation as parallel reactions
Master sequence Mutant cloud n o i t a r t n e c n o C Sequence space The molecular quasispecies in sequence space
1. Experiments on controlled evolution and RNA replication 2. Sequence-structure maps, neutral networks, and intersections 3. Optimization in the RNA model 4. What we can learn from molecules for evolution proper
5' - end N 1 O CH 2 O GCGGAU UUA GCUC AGUUGGGA GAGC CCAGA G CUGAAGA UCUGG AGGUC CUGUG UUCGAUC CACAG A AUUCGC ACCA 5'-e nd 3’-end N A U G C k = , , , OH O N 2 O P O CH 2 O Na � O O OH N 3 O P O CH 2 O Na � 3'-end O RNA O OH 5’-end N 4 O P O CH 2 O Na � 70 O O OH 60 3' - end O P O 10 Na � O 50 20 30 40 Definition of RNA structure
How to compute RNA secondary structures Efficient algorithms based on dynamic programming are available for computation of minimum free energy and many suboptimal secondary structures for given sequences. M.Zuker and P.Stiegler. Nucleic Acids Res . 9 :133-148 (1981) M.Zuker, Science 244 : 48-52 (1989) Equilibrium partition function and base pairing probabilities in Boltzmann ensembles of suboptimal structures. J.S.McCaskill. Biopolymers 29 :1105-1190 (1990) The Vienna RNA Package provides in addition: inverse folding (computing sequences for given secondary structures), computation of melting profiles from partition functions, all suboptimal structures within a given energy interval, barrier tress of suboptimal structures, kinetic folding of RNA sequences, RNA-hybridization and RNA/DNA-hybridization through cofolding of sequences, alignment, etc.. I.L.Hofacker, W. Fontana, P.F.Stadler, L.S.Bonhoeffer, M.Tacker, and P. Schuster. Mh.Chem . 125 :167-188 (1994) S.Wuchty, W.Fontana, I.L.Hofacker, and P.Schuster. Biopolymers 49 :145-165 (1999) C.Flamm, W.Fontana, I.L.Hofacker, and P.Schuster. RNA 6 :325-338 (1999) Vienna RNA Package : http://www.tbi.univie.ac.at
5’-end 3’-end A C C U G C U A A U Folding of an RNA sequence into U G its secondary structure of C G minimum free energy G C A U A A A C U C A U G G C C A G G U U U G G G A C C A U G A G G C G U G Base pair formation is the principle of folding RNA into secondary structures
Minimum free energy criterion 1st 2nd 3rd trial 4th 5th Inverse folding of RNA secondary structures The inverse folding algorithm searches for sequences that form a given RNA secondary structure under the minimum free energy criterion.
Structure
3’-end 3’-end C C A A A A U U G G U U A A G G G G G C G C A A A A G C G C A A A A G C G C A A U U G C G C C C C C A A U U C C C C C G C G A A G G A A A A C C G C G C C C G G G C G C G G G G C G U G G G G G C G C G U U U U C G C G U U C C C C G G C C C C U G U G C U G G 5’-end U 5’-end U U U G G Structure Compatible sequences
Structure
3’-end C A A U G A U G G G C A A G C A A G C A U G C C C A C U C C G A G A A C G C C G G C G G C G G G G G U G C U U C G C C G U G C G U U 5’-end G Structure Incompatible sequence
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