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Information and Information Processing in Biological Systems Peter Schuster, Ers Szathmry, and Avshalom Elitzur Institut fr Theoretische Chemie, Universitt Wien, Austria, Collegium Budapest Institute for Advanced Study , Ungarn, and
Information and Information Processing in Biological Systems
Peter Schuster, Eörs Szathmáry, and Avshalom Elitzur
Institut für Theoretische Chemie, Universität Wien, Austria, Collegium Budapest – Institute for Advanced Study , Ungarn, and Bar-Ilan University, Israel
Europäisches Forum Alpbach Alpbach, 18.– 25.08.2005
Web-Pages for further information: http://www.tbi.univie.ac.at/~pks http://www.colbud.hu/fellows/szathmary.shtml http://faculty.biu.ac.il/~elitzua/
Peter Schuster, Institut für Theoretische Chemie, Universität Wien
Generation time Selection and adaptation 10 000 generations Genetic drift in small populations 106 generations Genetic drift in large populations 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 Multicelluar organisms 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
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
1 year
Epochal evolution of bacteria in serial transfer experiments under constant conditions
Science 272 (1996), 1802-1804
Variation of genotypes in a bacterial serial transfer experiment
10,000-generation experiment with bacteria. Proc.Natl.Acad.Sci.USA 96 (1999), 3807-3812
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
An example of ‘artificial selection’ with RNA molecules or ‘breeding’ of biomolecules
The SELEX technique for the evolutionary preparation of aptamers
additional methyl group
Dissociation constants and specificity of theophylline, caffeine, and related derivatives
aptamer TCT8-4
Secondary structures of aptamers binding theophyllin, caffeine, and related compounds
Schematic drawing of the aptamer binding site for the theophylline molecule
Aptamer binding to aminoglycosid antibiotics: Structure of ligands
(1995), 281-290
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 with KD = 9 nM
RNA aptamer complex. Chemistry & Biology 4:35-50 (1997)
The three-dimensional structure of the tobramycin aptamer complex
Chemistry & Biology 4:35-50 (1997)
No new principle will declare itself from below a heap of facts.
Sir Peter Medawar, 1985
RNA as scaffold for supramolecular complexes
ribosome ? ? ? ? ? RNA as transmitter of genetic information
DNA
...AGAGCGCCAGACUGAAGAUCUGGAGGUCCUGUGUUC...
messenger-RNA protein transcription translation RNA as
working copy
RNA is modified by epigenetic control RNA RNA editing Alternative splicing of messenger
Functions of RNA molecules
RNA is the catalytic subunit in supramolecular complexes
RNA as regulator of gene expression Gene silencing by small interfering RNAs Allosteric control of transcribed RNA
Riboswitches metabolites controlling transcription and translation through
The world as a precursor of the current + biology RNA DNA protein
RNA as catalyst Ribozyme RNA as adapter molecule
G A C . . . C U G . . .
leu genetic code
RNA as carrier of genetic information
RNA viruses and retroviruses RNA evolution in vitro Evolutionary biotechnology RNA aptamers, artificial ribozymes, allosteric ribozymes
O CH2 OH O O P O O O
N1
O CH2 OH O P O O O
N2
O CH2 OH O P O O O
N3
O CH2 OH O P O O O
N4
N A U G C
k =
, , ,
3' - end 5' - end Na Na Na Na
5'-end 3’-end
GCGGAU AUUCGC UUA AGUUGGGA G CUGAAGA AGGUC UUCGAUC A ACCA GCUC GAGC CCAGA UCUGG CUGUG CACAG
Definition of RNA structure
Examples of ‘natural selection’ with RNA molecules
Genotype = Genome
GGCUAUCGUACGUUUACCCAAAAAGUCUACGUUGGACCCAGGCAUUGGAC.......G
Mutation Fitness in reproduction: Number of genotypes in the next generation Unfolding of the genotype: RNA structure formation Phenotype Selection
Evolution of phenotypes
Genotype = Genome
GGCTATCGTACGTTTACCCAAAAAGTCTACGTTGGACCCAGGCATTGGAC.......G
Mutation Fitness in reproduction: Number of genotypes in the next generation Unfolding of the genotype: Development Phenotype Selection
Evolution of phenotypes
Genotype = Genome
GGCTATCGTACGTTTACCCAAAAAGTCTACGTTGGACCCAGGCATTGGAC.......G
Mutation Fitness in reproduction: Number of genotypes in the next generation Unfolding of the genotype: Development Phenotype Selection
Evolution of phenotypes
Stock Solution Reaction Mixture
Replication rate constant: fk = / [ + dS
(k)]
dS
(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
f0 f f1 f2 f3 f4 f6 f5 f7
Replication rate constant: fk = / [ + dS
(k)]
dS
(k) = dH(Sk,S)
Evaluation of RNA secondary structures yields replication rate constants
Phenylalanyl-tRNA as target structure Randomly chosen initial structure
Formation of a quasispecies in sequence space
Migration of a quasispecies through 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
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
In silico optimization in the flow reactor: Main transitions Main transitions Relay steps Time (arbitrary units) Average structure distance to target d S
500 750 1000 1250 250 50 40 30 20 10
Evolutionary trajectory
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.
AUGC GC Movies of optimization trajectories over the AUGC and the GC alphabet
RNA sequence RNA structure
energy
RNA folding: Structural biology, spectroscopy of biomolecules, understanding molecular function Empirical parameters Biophysical chemistry: thermodynamics and kinetics
Sequence, structure, and design
RNA sequence RNA structure
energy
RNA folding: Structural biology, spectroscopy of biomolecules, understanding molecular function Inverse Folding Algorithm Iterative determination
given secondary structure
Sequence, structure, and design
Inverse folding of RNA: Biotechnology, design of biomolecules with predefined structures and functions
Minimum free energy criterion
Inverse folding of RNA secondary structures
1st 2nd 3rd trial 4th 5th
The inverse folding algorithm searches for sequences that form a given RNA secondary structure under the minimum free energy criterion.
A multi-component neutral network formed by a rare structure: < cr
A connected neutral network formed by a common structure: > cr
Neighborhood in sequence space and the degree of neutrality
Gk Neutral Network
Structure S
k
Gk C
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
„...Variations neither useful not injurious would not be affected by natural selection, and would be left either a fluctuating element, as perhaps we see in certain polymorphic species, or would ultimately become fixed, owing to the nature of the
...“
Charles Darwin, Origin of species (1859)
Motoo Kimura’s Population genetics of neutral evolution. Evolutionary rate at the molecular level. Nature 217: 624-626, 1955. The Neutral Theory of Molecular Evolution. Cambridge University Press. Cambridge, UK, 1983.
Mount Fuji
Example of a smooth landscape on Earth
Dolomites Bryce Canyon
Examples of rugged landscapes on Earth
Genotype Space Fitness
Start of Walk End of Walk
Evolutionary optimization in absence of neutral paths in sequence space
Genotype Space F i t n e s s
Start of Walk End of Walk Random Drift Periods Adaptive Periods
Evolutionary optimization including neutral paths in sequence space
Grand Canyon
Example of a landscape on Earth with ‘neutral’ ridges and plateaus
RNA 9:1456-1463, 2003
Evidence for neutral networks and shape space covering
Evidence for neutral networks and intersection of apatamer functions
Nature , 323-325, 1999 402
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
Nature , 841-844, 2002 420
Catalytic activity in the DU alphabet
2 2 6 5 6 8 C ’
1
C ’
1
5 4 4 4 2 9 7 6 3 3 2 1 1
The 2,6-diamino purine – uracil, DU, base pair
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--virus (B)
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
GGCUAUCGUACGUUUACCCAAAAAGUCUACGUUGGACCCAGGCAUUGGACG
Better be afraid I can ‚karate‘