Evolution in vitro Theorie und Praxis der Herstellung - - PowerPoint PPT Presentation
Evolution in vitro Theorie und Praxis der Herstellung mageschneiderter Molekle Peter Schuster Institut fr Theoretische Chemie und Molekulare Strukturbiologie der Universitt Wien Vortragsreihe zum Jahr der Chemie TU Ilmenau, 22.11.2003
Evolution in vitro
Theorie und Praxis der Herstellung maßgeschneiderter Moleküle Peter Schuster Institut für Theoretische Chemie und Molekulare Strukturbiologie der Universität Wien Vortragsreihe zum Jahr der Chemie TU Ilmenau, 22.11.2003
Web-Page for further information: http://www.tbi.univie.ac.at/~pks
Generation time 10 000 generations 106 generations 107 generations RNA molecules 10 sec 1 min 27.8 h = 1.16 d 6.94 d 115.7 d 1.90 a 3.17 a 19.01 a Bacteria 20 min 10 h 138.9 d 11.40 a 38.03 a 1 140 a 380 a 11 408 a Higher multicelluar
10 d 20 a 274 a 20 000 a 27 380 a 2 × 107 a 273 800 a 2 × 108 a
Time scales of evolutionary change
1. Controlled evolution experiments with bacteria and RNA 2. Optimization in the RNA model 3. Sequence-structure maps, neutral networks, and intersections 4. Selection experiments and design of RNA molecules
1. Controlled evolution experiments with bacteria and RNA 2. Optimization in the RNA model 3. Sequence-structure maps, neutral networks, and intersections 4. Selection experiments and design of RNA molecules
Bacterial Evolution
rare beneficial mutants. Science 272 (1996), 1802-1804
Genomic evolution during a 10,000-generation experiment with bacteria. Proc.Natl.Acad.Sci.USA 96 (1999), 3807-3812
24 h 24 h
Serial transfer of Escherichia coli cultures in Petri dishes
1 day 6.67 generations 1 month 200 generations
nutrient agar
1 year
Epochal evolution of bacteria in serial transfer experiments under constant conditions
Science 272 (1996), 1802-1804
2000 4000 6000 8000 Time 5 10 15 20 25 Hamming distance to ancestor Generations
Variation of genotypes in a bacterial serial transfer experiment
10,000-generation experiment with bacteria. Proc.Natl.Acad.Sci.USA 96 (1999), 3807-3812
Evolution of RNA molecules based on Qβ 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 Stock solution: Q RNA-replicase, ATP, CTP, GTP and UTP, buffer
1 2 3 4 5 6 69 70 The serial transfer technique applied to RNA evolution in vitro
Reproduction of the original figure of the serial transfer experiment with Q RNA β D.R.Mills, R,L,Peterson, S.Spiegelman, . Proc.Natl.Acad.Sci.USA (1967), 217-224 An extracellular Darwinian experiment with a self-duplicating nucleic acid molecule 58
Decrease in mean fitness due to quasispecies formation
The increase in RNA production rate during a serial transfer experiment
1. Controlled evolution experiments with bacteria and RNA 2. Optimization in the RNA model 3. Sequence-structure maps, neutral networks, and intersections 4. Selection experiments and design of RNA molecules
RNA as scaffold for supramolecular complexes
ribosome ? ? ? ? ?
RNA as adapter molecule
GAC ... CUG ...
leu genetic code
RNA as transmitter of genetic information
DNA
...AGAGCGCCAGACUGAAGAUCUGGAGGUCCUGUGUUC...messenger-RNA protein transcription translation RNA as
working copy
RNA as carrier of genetic information RNA RNA viruses and retroviruses as information carrier in evolution and evolutionary biotechnology in vitro
RNA as catalyst ribozyme
The RNA DNA protein world as a precursor of the current + biology
RNA as regulator of gene expression
gene silencing by small interfering RNAs
RNA is modified by epigenetic control RNA RNA editing Alternative splicing of messenger RNA is the catalytic subunit in
supramolecular complexes
Functions of RNA molecules
N1
O CH2 OH O P O O ON2
O CH2 OH O P O O ON3
O CH2 OH O P O O ON4
N A U G C
k =
, , ,
3' - end 5' - end Na Na Na Na
nd 3’-end
GCGGAU AUUCGC UUA AGUUGGGA G CUGAAGA AGGUC UUCGAUC A ACCA GCUC GAGC CCAGA UCUGG CUGUG CACAG 3'-end 5’-end
70 60 50 40 30 20 10
Definition of RNA structure
5'-e
The three-dimensional structure of a short double helical stack of B-DNA
James D. Watson, 1928- , and Francis Crick, 1916- , Nobel Prize 1962
1953 – 2003 fifty years double helix
G G G G C C C G C C G C C G C C G C C G C C C C G G G G G C G C
Plus Strand Plus Strand Minus Strand Plus Strand Plus Strand Minus Strand
3' 3' 3' 3' 3' 5' 5' 5' 3' 3' 5' 5' 5' +
Complex Dissociation Synthesis Synthesis
Complementary replication as the simplest copying mechanism of RNA Complementarity is determined by Watson-Crick base pairs: G C and A=U
G G G G G G G G G G G G G G G G U U U U U U U U U U U A A A A A A A A A A A A U C C C C C C C C C C C C 5’-end 3’-end GGCGCGCCCGGCGCC GUAUCGAAAUACGUAGCGUAUGGGGAUGCUGGACGGUCCCAUCGGUACUCCA UGGUUACGCGUUGGGGUAACGAAGAUUCCGAGAGGAGUUUAGUGACUAGAGG
RNAStudio.lnk
Folding of RNA sequences into secondary structures of minimal free energy, G0
300
f0 f f1 f2 f3 f4 f6 f5 f7
Replication rate constant: fk = / [+ dS
(k)]
(k) = dH(Sk,S
)
Evaluation of RNA secondary structures yields replication rate constants
Stock Solution Reaction Mixture
Replication rate constant: fk = / [+ dS
(k)]
(k) = dH(Sk,S
) Selection constraint: # RNA molecules is controlled by the flow N N t N ± ≈ ) ( The flowreactor as a device for studies of evolution in vitro and in silico
s p a c e Sequence Concentration
Master sequence Mutant cloud “Off-the-cloud” mutations
The molecular quasispecies in sequence space
S{ = ( ) I{ f S
{ {
ƒ = ( )
S{ f{ I{
Mutation Genotype-Phenotype Mapping Evaluation of the Phenotype
Q{
j
I1 I2 I3 I4 I5 In
Q
f1 f2 f3 f4 f5 fn
I1 I2 I3 I4 I5 I{ In+1 f1 f2 f3 f4 f5 f{ fn+1
Q
Evolutionary dynamics including molecular phenotypes
In silico optimization in the flow reactor: Trajectory (biologists‘ view) Time (arbitrary units) A v e r a g e d i s t a n c e f r
i n i t i a l s t r u c t u r e 5
500 750 1000 1250 250 50 40 30 20 10
Evolutionary trajectory
In silico optimization in the flow reactor: Trajectory (physicists‘ view) Time (arbitrary units) A v e r a g e s t r u c t u r e d i s t a n c e t
a r g e t d
500 750 1000 1250 250 50 40 30 20 10
Evolutionary trajectory
AUGC GC Movies of optimization trajectories over the AUGC and the GC alphabet
Runtime of trajectories F r e q u e n c y
1000 2000 3000 4000 5000 0.05 0.1 0.15 0.2
Statistics of the lengths of trajectories from initial structure to target (AUGC-sequences)
44
Average structure distance to target dS
1250 10
44 42 40 38 36 Relay steps Number of relay step Time
Endconformation of optimization
44 43
Average structure distance to target dS
1250 10
44 42 40 38 36 Relay steps Number of relay step Time
Reconstruction of the last step 43 44
44 43 42
Average structure distance to target dS
1250 10
44 42 40 38 36 Relay steps Number of relay step Time
Reconstruction of last-but-one step 42 43 ( 44)
44 43 42 41
Average structure distance to target dS
1250 10
44 42 40 38 36 Relay steps Number of relay step Time
Reconstruction of step 41 42 ( 43 44)
44 43 42 41 40
Average structure distance to target dS
1250 10
44 42 40 38 36 Relay steps Number of relay step Time
Reconstruction of step 40 41 ( 42 43 44)
44 43 42 41 40 39 Evolutionary process Reconstruction
Average structure distance to target dS
1250 10
44 42 40 38 36 Relay steps Number of relay step Time
Reconstruction of the relay series
Transition inducing point mutations Neutral point mutations
Change in RNA sequences during the final five relay steps 39 44
In silico optimization in the flow reactor: Trajectory and relay steps Time (arbitrary units) A v e r a g e s t r u c t u r e d i s t a n c e t
a r g e t d
500 750 1000 1250 250 50 40 30 20 10
Evolutionary trajectory
Relay steps
10 08 12 14 Time (arbitrary units) Average structure distance to target dS
250 20 10
Uninterrupted presence Evolutionary trajectory Number of relay step
28 neutral point mutations during a long quasi-stationary epoch Transition inducing point mutations Neutral point mutations
Neutral genotype evolution during phenotypic stasis
In silico optimization in the flow reactor: Main transitions Relay steps
00 09 31 44
Three important steps in the formation of the tRNA clover leaf from a randomly chosen initial structure corresponding to three main transitions.
Number of transitions F r e q u e n c y
20 40 60 80 100 0.05 0.1 0.15 0.2 0.25 0.3
All transitions Main transitions
Statistics of the numbers of transitions from initial structure to target (AUGC-sequences)
Alphabet Runtime Transitions Main transitions
AUGC 385.6 22.5 12.6 1017 GUC 448.9 30.5 16.5 611 GC 2188.3 40.0 20.6 107
Statistics of trajectories and relay series (mean values of log-normal distributions)
1. Controlled evolution experiments with bacteria and RNA 2. Optimization in the RNA model 3. Sequence-structure maps, neutral networks, and intersections 4. Selection experiments and design of RNA molecules
The inverse folding algorithm searches for sequences that form a given RNA secondary structure under the minimum free energy criterion.
RNA sequences as well as RNA secondary structures can be visualized as objects in metric spaces. At constant chain length the sequence space is a (generalized) hypercube. The mapping from RNA sequences into RNA secondary structures is many-to-one. Hence, it is redundant and not invertible. RNA sequences, which are mapped onto the same RNA secondary structure, are neutral with respect to structure. The pre-images of structures in sequence space are neutral
connect sequences of Hamming distance dH = 1.
Theory of sequence – structure mappings
A case study in RNA secondary structures. Proc.Roy.Soc.London B 255 (1994), 279-284 W.Grüner, R.Giegerich, D.Strothmann, C.Reidys, I.L.Hofacker, P.Schuster, Analysis of RNA sequence structure maps by exhaustive enumeration. I. Neutral networks. Mh.Chem. 127 (1996), 355-374 W.Grüner, R.Giegerich, D.Strothmann, C.Reidys, I.L.Hofacker, P.Schuster, Analysis of RNA sequence structure maps by exhaustive enumeration. II. Structure of neutral networks and shape space covering. Mh.Chem. 127 (1996), 375-389 C.M.Reidys, P.F.Stadler, P.Schuster, Generic properties of combinatory maps. Bull.Math.Biol. 59 (1997), 339-397 I.L.Hofacker, P. Schuster, P.F.Stadler, Combinatorics of RNA secondary structures. Discr.Appl.Math. 89 (1998), 177-207 C.M.Reidys, P.F.Stadler, Combinatory landscapes. SIAM Review 44 (2002), 3-54
Sk I. = ( ) ψ
fk f Sk = ( )
Sequence space Structure space Real numbers Mapping from sequence space into structure space and into function
Sk I. = ( ) ψ
fk f Sk = ( )
Sequence space Structure space Real numbers
Sk I. = ( ) ψ
fk f Sk = ( )
Sequence space Structure space Real numbers
The pre-image of the structure Sk in sequence space is the neutral network Gk
λj = 27 = 0.444 ,
/
12 λk = (k)
j
| | Gk
λ κ
cr = 1 -
1)
/ κ- λ λ
k cr . . . .
> λ λ
k cr . . . .
< network is connected Gk network is connected not Gk Connectivity threshold: Alphabet size : = 4
G S S
k k k
= ( ) | ( ) =
U
I
j j
2 0.5 3 0.423 4 0.370
GC,AU GUC,AUG AUGC
Mean degree of neutrality and connectivity of neutral networks
A connected neutral network
Giant Component
A multi-component neutral network
Degree of neutrality of cloverleaf RNA secondary structures over different alphabets
Reference for postulation and in silico verification of neutral networks
Gk Neutral Network
Structure S
k
Gk C k
Compatible Set Ck
The compatible set Ck of a structure Sk consists of all sequences which form Sk as its minimum free energy structure (the neutral network Gk) or one of its suboptimal structures.
Structure S Structure S
1
The intersection of two compatible sets is always non empty: C0 C1
Reference for the definition of the intersection and the proof of the intersection theorem
C U G G G A A A A A U C C C C A G A C C G G G G G U U U C C C C G G
3’-end
M i n i m u m f r e e e n e r g y c
f
m a t i
S S u b
t i m a l c
f
m a t i
S 1
G G G G G G G G G G G G C C C C U U U U C C C C C C U A A A A A C G G G G G G C C C C U U G G G G G C C C C C C C U U A A A A A U G
A sequence at the intersection of two neutral networks is compatible with both structures
5.10 5.90
2 8
14 15 18 17 23 19 27 22 38 45 25 36 33 39 40 43 413.30 7.40
5 3 7 4 10 9 6
13 12 3 . 1 11 21 20 16 28 29 26 30 32 42 46 44 24 35 34 37 49 31 47 48S0 S1
basin '1' long living metastable structure basin '0' minimum free energy structure
Barrier tree for two long living structures
1. Controlled evolution experiments with bacteria and RNA 2. Optimization in the RNA model 3. Sequence-structure maps, neutral networks, and intersections 4. Selection experiments and design of RNA molecules
A ribozyme switch
E.A.Schultes, D.B.Bartel, Science 289 (2000), 448-452
Two ribozymes of chain lengths n = 88 nucleotides: An artificial ligase (A) and a natural cleavage ribozyme of hepatitis-
The sequence at the intersection: An RNA molecules which is 88 nucleotides long and can form both structures
Two neutral walks through sequence space with conservation of structure and catalytic activity
Sequence of mutants from the intersection to both reference ribozymes
Catalytic activity in the AUG alphabet
O O O O H H H H H H H H H N N N N N N N N N O O H N N H O N N N N N N N
G=U (U=A) A=U U=G
O N
Base pairs in the AUG alphabet
Catalytic activity in the DU alphabet
2 2
The 2,6-diamino purine – uracil, DU, base pair
A = U G C
Evolutionary design of RNA molecules
D.B.Bartel, J.W.Szostak, In vitro selection of RNA molecules that bind specific ligands. Nature 346 (1990), 818-822 C.Tuerk, L.Gold, SELEX - Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 249 (1990), 505-510 D.P.Bartel, J.W.Szostak, Isolation of new ribozymes from a large pool of random sequences. Science 261 (1993), 1411-1418 R.D.Jenison, S.C.Gill, A.Pardi, B.Poliski, High-resolution molecular discrimination by RNA. Science 263 (1994), 1425-1429
Biology 2 (1995), 281-290 Jiang, A. K. Suri, R. Fiala, D. J. Patel, Saccharide-RNA recognition in an aminoglycoside antibiotic-RNA aptamer complex. Chemistry & Biology 4 (1997), 35-50
yes
Selection Cycle
no
Genetic Diversity
Desired Properties ? ? ? Selection Amplification Diversification
Selection cycle used in applied molecular evolution to design molecules with predefined properties
The SELEX technique for the evolutionary design of aptamers
Sequences of aptamers binding theophyllin, caffeine, and related compounds
R.D.Jenison, S.C.Gill, A.Pardi, B.Poliski, High-resolution molecular discrimination by RNA. Science 263 (1994), 1425-1429
Secondary structures of aptamers binding theophyllin, caffeine, and related compounds
additional methyl group
Dissociation constants and specificity of theophylline, caffeine, and related derivatives
aptamer TCT8-4
Schematic drawing of the aptamer binding site for the theophylline molecule
tobramycin
A A A A A C C C C C C C C G G G G G G G G U U U U U U
5’- 3’-
A A A A A U U U U U U C C C C C C C C G G G G G G G G
5’-
RNA aptamer
Formation of secondary structure of the tobramycin binding RNA aptamer
antibiotic-RNA aptamer complex. Chemistry & Biology 4:35-50 (1997)
The three-dimensional structure of the tobramycin aptamer complex
Chemistry & Biology 4:35-50 (1997)
Hammerhead ribozyme – The smallest RNA based catalyst
H.W.Pley, K.M.Flaherty, D.B.McKay, Three dimensional structure of a hammerhead
W.G.Scott, J.T.Finch, A.Klug, The crystal structures of an all-RNA hammerhead ribozyme: A proposed mechanism for RNA catalytic cleavage. Cell 81 (1995), 991-1002 J.E.Wedekind, D.B.McKay, Crystallographic structures of the hammerhead ribozyme: Relationship to ribozyme folding and catalysis. Annu.Rev.Biophys.Biomol.Struct. 27 (1998), 475-502 G.E.Soukup, R.R.Breaker, Design of allosteric hammerhead ribozymes activated by ligand- induced structure stabilization. Structure 7 (1999), 783-791
Hammerhead ribozyme: The smallest known catalytically active RNA molecule
Cleavage site
OH OH OH ppp 5' 5' 3' 3'
RNA DNA
Allosteric effectors:
FMN = flavine mononucleotide H10 – H12 theophylline H14 Self-splicing allosteric ribozyme H13
theophylline
Hammerhead ribozymes with allosteric effectors
Acknowledgement of support
Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Projects No. 09942, 10578, 11065, 13093 13887, and 14898 Jubiläumsfonds der Österreichischen Nationalbank Project No. Nat-7813 European Commission: Project No. EU-980189 Siemens AG, Austria The Santa Fe Institute and the Universität Wien The software for producing RNA movies was developed by Robert Giegerich and coworkers at the Universität Bielefeld
Universität Wien
Coworkers
Universität Wien
Walter Fontana, Santa Fe Institute, NM Christian Reidys, Christian Forst, Los Alamos National Laboratory, NM Peter Stadler, Bärbel Stadler, Universität Leipzig, GE Ivo L.Hofacker, Christoph Flamm, Universität Wien, AT Andreas Wernitznig, Michael Kospach, Universität Wien, AT Ulrike Langhammer, Ulrike Mückstein, Stefanie Widder Jan Cupal, Kurt Grünberger, Andreas Svrček-Seiler, Stefan Wuchty Ulrike Göbel, Institut für Molekulare Biotechnologie, Jena, GE Walter Grüner, Stefan Kopp, Jaqueline Weber
Web-Page for further information: http://www.tbi.univie.ac.at/~pks