Kinetic Folding of RNA and the Design of Molecules with Predefined - - PowerPoint PPT Presentation

Kinetic Folding of RNA and the Design of Molecules with Predefined Secondary Structures Peter Schuster Institut für Theoretische Chemie und Molekulare Strukturbiologie der Universität Wien

Gunnar and Gunnel Källén Memorial Lecture Lund University, 10.– 11.05.2004

Web-Page for further information: http://www.tbi.univie.ac.at/~pks

RNA

RNA as scaffold for supramolecular complexes

ribosome ? ? ? ? ?

RNA as adapter molecule

GAC ... CUG ...

leu genetic code

RNA as transmitter of genetic information

DNA

messenger-RNA protein transcription translation RNA as

• f genetic information

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

N2

N3

N4

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

5'-End 5'-End 3'-End 3'-End

70 60 50 40 30 20 10 GCGGAUUUAGCUCAGDDGGGAGAGCMCCAGACUGAAYAUCUGGAGMUCCUGUGTPCGAUCCACAGAAUUCGCACCA

Sequence Secondary structure

Definition and physical relevance of RNA secondary structures

RNA secondary structures are listings of Watson-Crick and GU wobble base pairs, which are free of knots and pseudokots. „Secondary structures are folding intermediates in the formation of full three-dimensional structures.“ D.Thirumalai, N.Lee, S.A.Woodson, and D.K.Klimov. Annu.Rev.Phys.Chem. 52:751-762 (2001):

Stacking of free nucleobases or other planar heterocyclic compounds (N6,N9-dimethyl-adenine)

The stacking interaction as driving force of structure formation in nucleic acids

Stacking of nucleic acid single strands (poly-A)

James D. Watson and Francis H.C. Crick Nobel prize 1962 1953 – 2003 fifty years double helix Stacking of base pairs in nucleic acid double helices (B-DNA)

2 2 6 5 6 8 C ’

1

C ’

1

5 4 4 6 2 9 7 4 3 3 2 1 1

54.4 55.7

10.72 Å 2 2 6 5 6 8 C ’

1

C ’

1

5 4 4 4 2 9 7 6 3 3 1 1

56.2 57.4

10.44 Å

U = A C G

• Watson-Crick type base pairs

O O O H H H H H H N N N N O O H N N H O N N N N N N N

G=U U=G

Deviation from Watson-Crick geometry Deviation from Watson-Crick geometry

Wobble base pairs

RNA sequence

Empirical parameters Biophysical chemistry: thermodynamics and kinetics

RNA structure

Inverse folding of RNA: Biotechnology, design of biomolecules with predefined structures and functions RNA folding: Structural biology, spectroscopy of biomolecules, understanding molecular function

Sequence, structure, and function

S1

(h)

S9

(h)

Free energy G Minimum of free energy Suboptimal conformations

S0

(h) S2

(h)

S3

(h)

S4

(h)

S7

(h)

S6

(h)

S5

(h)

S8

(h)

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

The minimum free energy structures on a discrete space of conformations

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

hairpin loop hairpin loop stack stack stack hairpin loop stack free end free end free end hairpin loop hairpin loop stack stack free end free end joint hairpin loop stack stack stack internal loop bulge multiloop

Elements of RNA secondary structures as used in free energy calculations

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

free energy of stacking < 0

L

∑ ∑ ∑ ∑

+ + + + = ∆

loops internal bulges loops hairpin pairs base

• f

stacks , 300

) ( ) ( ) (

i b l kl ij

n i n b n h g G

Folding of RNA sequences into secondary structures of minimal free energy, G0

300

5'-End 5'-End 5'-End 3'-End 3'-End 3'-End

70 60 50 40 30 20 10 GCGGAUUUAGCUCAGDDGGGAGAGCMCCAGACUGAAYAUCUGGAGMUCCUGUGTPCGAUCCACAGAAUUCGCACCA

Sequence Secondary structure Symbolic notation

• A symbolic notation of RNA secondary structure that is equivalent to the conventional graphs

Minimal hairpin loop size: nlp 3 Minimal stack length: nst 2

Recursion formula for the number of acceptable RNA secondary structures

Computed numbers of minimum free energy structures over different nucleotide alphabets

• P. Schuster, Molecular insights into evolution of phenotypes. In: J. Crutchfield & P.Schuster,

Evolutionary Dynamics. Oxford University Press, New York 2003, pp.163-215.

S0 S1

Kinetic Structures F r e e E n e r g y S0 S0 S1 S2 S3 S4 S5 S6 S7 S8 S10 S9 Minimum Free Energy Structure Suboptimal Structures T = 0 K , t T > 0 K , t T > 0 K , t finite

Different notions of RNA structure including suboptimal conformations and folding kinetics

Suboptimal RNA Secondary Structures

Michael Zuker. On finding all suboptimal foldings of an RNA molecule. Science 244 (1989), 48-52 Stefan Wuchty, Walter Fontana, Ivo L. Hofacker, Peter Schuster. Complete suboptimal folding of RNA and the stability of secondary structures. Biopolymers 49 (1999), 145-165

3' 5'

Total number of structures including all suboptimal conformations, stable and unstable (with G0>0): #conformations = 1 416 661 Minimum free energy structure AAAGGGCACAGGGUGAUUUCAAUAAUUUUA Sequence

Example of a small RNA molecule: n=30

Density of stares of suboptimal structures of the RNA molecule with the sequence: AAAGGGCACAGGGUGAUUUCAAUAAUUUUA

Partition Function of RNA Secondary Structures

John S. McCaskill. The equilibrium function and base pair binding probabilities for RNA secondary structure. Biopolymers 29 (1990), 1105-1119 Ivo L. Hofacker, Walter Fontana, Peter F. Stadler, L. Sebastian Bonhoeffer, Manfred Tacker, Peter Schuster. Fast folding and comparison of RNA secondary structures. Monatshefte für Chemie 125 (1994), 167-188

3' 5'

Example of a small RNA molecule with two low-lying suboptimal conformations which contribute substantially to the partition function

UUGGAGUACACAACCUGUACACUCUUUC

Example of a small RNA molecule: n=28

U U G G A G U A C A C A A C C U G U A C A C U C U U U C U U G G A G U A C A C A A C C U G U A C A C U C U U U C C U U U C U C A C A U G U C C A A C A C A U G A G G U U U U G G A G U A C A C A A C C U G U A C A C U C U U U C

U U G G A G U A C A C A A C C U G U A C A C U C U U U C

U U G G A G U A C A C A A C C U G U A C A C U C U U U C U U G G A G U A C A C A A C C U G U A C A C U C U U U C

second suboptimal configuration first suboptimal configuration

minimum free energy configuration

∆E = 0.55 kcal / mole

0→2

∆E = 0.50 kcal / mole

1 →

• G = - 5.39 kcal / mole

3' 5'

„Dot plot“ of the minimum free energy structure (lower triangle) and the partition function (upper triangle) of a small RNA molecule (n=28) with low energy suboptimal configurations

GCGGAU AUUCGC UUA AGUUGGGA G CUGAAGA AGGUC UUCGAUC A ACCA GCUC GAGC CCAGA UCUGG CUGUG CACAG GCGGAU AUUCGC UUA AGDDGGGA M CUGAAYA AGMUC TPCGAUC A ACCA GCUC GAGC CCAGA UCUGG CUGUG CACAG

Phenylalanyl-tRNA as an example for the computation of the partition function

tRNAphe

modified bases without

first suboptimal configuration E = 0.43 kcal / mole ∆ 0

1 →

3’ 5’

tRNA modified bases

phe

with

first suboptimal configuration E = 0.94 kcal / mole ∆ 0

1 →

3’ 5’

5.10

2 8

3.30 7.40

5 3 7 4 10 9 6

S0 S1

Kinetic folding

S0 S1 S2 S3 S4 S5 S6 S7 S8 S10 S9

Suboptimal structures

lim t finite folding time

5.90

A typical energy landscape of a sequence with two (meta)stable comformations

Kinetic Folding of RNA Secondary Structures

Christoph Flamm, Walter Fontana, Ivo L. Hofacker, Peter Schuster. RNA folding kinetics at elementary step resolution. RNA 6:325-338, 2000 Christoph Flamm, Ivo L. Hofacker, Sebastian Maurer-Stroh, Peter F. Stadler, Martin Zehl. Design of multistable RNA molecules. RNA 7:325-338, 2001

The Folding Algorithm

A sequence I specifies an energy ordered set of compatible structures S(I):

S(I) = {S0 , S1 , … , Sm , O}

A trajectory Tk(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 S0 or a metastable structure Sk which represents a local energy minimum:

T0(I) = {O , S (1) , … , S (t-1) , S (t) , S (t+1) , … , S0} Tk(I) = {O , S (1) , … , S (t-1) , S (t) , S (t+1) , … , Sk}

Transition probabilities Pij(t) = P rob{Si→Sj} are defined by

Pij(t) = Pi(t) kij = Pi(t) exp(-∆Gij/2RT) / Σi Pji(t) = Pj(t) kji = Pj(t) exp(-∆Gji/2RT) / Σj exp(-∆Gki/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. 145:224-230, 1966).

∑

+ ≠ =

= Σ

2 i , 1 i m k k

Formulation of kinetic RNA folding as a stochastic process

Base pair formation

Nucleation

Base pair cleavage Base pair formation

Elongation

Base pair cleavage

Base pair formation and base pair cleavage moves for nucleation and elongation of stacks

Base pair shift Class 1

Base pair shift move of class 1: Shift inside internal loops or bulges

Base pair shift Class 2

Base pair shift move of class 2: Shift involving free ends

Mean folding curves for three small RNA molecules with different folding behavior

I1 = ACUGAUCGUAGUCAC I2 = AUUGAGCAUAUUCAC I3 = CGGGCUAUUUAGCUG S0 = • • ( ( ( ( • • • • ) ) ) ) •

Sh S1

(h)

S6

(h)

S7

(h)

S5

(h)

S2

(h)

S9

(h)

Free energy G Local minimum Suboptimal conformations

Search for local minima in conformation space

Free energy G "Reaction coordinate" Sk S{ Saddle point T

{ k

F r e e e n e r g y G Sk S{ T

{ k

"Barrier tree"

Definition of a ‚barrier tree‘

I1 = ACUGAUCGUAGUCAC S0 S1 S2 S3 O

Example of an unefficiently folding small RNA molecule with n = 15

I2 = AUUGAGCAUAUUCAC S0 S1 S4 S2 S3 O

Example of an easily folding small RNA molecule with n = 15

I3 = CGGGCUAUUUAGCUG

S0 S1 S2 S3 O

Example of an easily folding and especially stable small RNA molecule with n = 15

Examples of two folding trajectories leading to different local minima

Folding dynamics of the sequence GGCCCCUUUGGGGGCCAGACCCCUAAAAAGGGUC

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 G G G G G G G G G G G G G G G G G G C C C C C C C C U U U U U U G G G G G C C C C C C C C C C C C C U U U A A A A A A A A A A U

3’-end

Minimum free energy conformation S0 Suboptimal conformation S1

C G

One sequence is compatible with two structures

2

8 14 15 18

17 23 19 27 22 38 45 25 36 33 39 40

43

41

5 3 7

4 10 9

6 13 12

11 21 20 16 28 29 26 30 32 42 46 44 24 35 34 37 49

31 47 48

S0 S1

Barrier tree of a sequence with two conformations

Kinetics RNA refolding between a long living metastable conformation and the minmum free energy structure

J.H.A. Nagel, J. Møller-Jensen, C. Flamm, K.J. Öistämö, J. Besnard, I.L. Hofacker, A.P. Gultyaev, M.H. de Smit, P. Schuster, K. Gerdes and C.W.A. Pleij. The refolding mechanism of the metastable structure in the 5’-end of the hok mRNA of plasmid R1, submitted 2004. J.H.A. Nagel, C. Flamm, I.L. Hofacker, K. Franke, M.H. de Smit, P. Schuster, and C.W.A. Pleij. Structural parameters affecting the kinetic competition of RNA hairpin formation, in press 2004.

GCGGAU AUUCGC UUA AGUUGGGA G CUGAAGA AGGUC UUCGAUC A ACCA GCUC GAGC CCAGA UCUGG CUGUG CACAG GCGGAU AUUCGC UUA AGDDGGGA M CUGAAYA AGMUC TPCGAUC A ACCA GCUC GAGC CCAGA UCUGG CUGUG CACAG

Kinetic folding of phenylalanyl-tRNA

modified

unmodified Folding dynamics of tRNAphe with and without modified nucelotides

Barrier tree of tRNAphe without modified nucelotides

Theory of sequence – structure mappings

• P. Schuster, W.Fontana, P.F.Stadler, I.L.Hofacker, From sequences to shapes and back:

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

Minimum free energy criterion Inverse folding of RNA secondary structures

The idea of inverse folding algorithm is to search for sequences that form a given RNA secondary structure under the minimum free energy criterion.

Inverse folding algorithm I0 I1 I2 I3 I4 ... Ik Ik+1 ... It S0 S1 S2 S3 S4 ... Sk Sk+1 ... St Ik+1 = Mk(Ik) and dS(Sk,Sk+1) = dS(Sk+1,St) - dS(Sk,St) < 0 M M ... base or base pair mutation operator dS (Si,Sj) ... distance between the two structures Si and Sj ‚Unsuccessful trial‘ ... termination after n steps

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.

UUUAGCCAGCGCGAGUCGUGCGGACGGGGUUAUCUCUGUCGGGCUAGGGCGC GUGAGCGCGGGGCACAGUUUCUCAAGGAUGUAAGUUUUUGCCGUUUAUCUGG UUAGCGAGAGAGGAGGCUUCUAGACCCAGCUCUCUGGGUCGUUGCUGAUGCG CAUUGGUGCUAAUGAUAUUAGGGCUGUAUUCCUGUAUAGCGAUCAGUGUCCG GUAGGCCCUCUUGACAUAAGAUUUUUCCAAUGGUGGGAGAUGGCCAUUGCAG

Criterion of Minimum Free Energy

Sequence Space Shape Space

CGTCGTTACAATTTA GTTATGTGCGAATTC CAAATT AAAA ACAAGAG..... CGTCGTTACAATTTA GTTATGTGCGAATTC CAAATT AAAA ACAAGAG..... G A G T A C A C

Hamming distance d (I ,I ) =

H 1 2

4 d (I ,I ) = 0

H 1 1

d (I ,I ) = d (I ,I )

H H 1 2 2 1

d (I ,I ) d (I ,I ) + d (I ,I )

H H H 1 3 1 2 2 3

• (i)

(ii) (iii)

The Hamming distance between sequences induces a metric in sequence space

Hamming distance d (S ,S ) =

H 1 2

4 d (S ,S ) = 0

H 1 1

d (S ,S ) = d (S ,S )

H H 1 2 2 1

d (S ,S ) d (S ,S ) + d (S ,S )

H H H 1 3 1 2 2 3

• (i)

(ii) (iii)

The Hamming distance between structures in parentheses notation forms a metric in structure space

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

• networks. They can be represented by graphs where the edges

connect sequences of Hamming distance dH = 1.

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

Reference for postulation and in silico verification of neutral networks

Evolution in silico

• W. Fontana, P. Schuster,

Science 280 (1998), 1451-1455

Neutral networks are sets of sequences forming the same structure. Gk is the pre-image of the structure Sk in sequence space: Gk =

• 1(Sk) π{

j |

(Ij) = Sk} The set is converted into a graph by connecting all sequences of Hamming distance one. Neutral networks of small RNA molecules can be computed by exhaustive folding of complete sequence spaces, i.e. all RNA sequences of a given chain length. This number, N=4n , becomes very large with increasing length, and is prohibitive for numerical computations. Neutral networks can be modelled by random graphs in sequence

• space. In this approach, nodes are inserted randomly into sequence

space until the size of the pre-image, i.e. the number of neutral sequences, matches the neutral network to be studied.

λj = 27 = 0.444 ,

/

12 λk = (k)

j

| | Gk

λ κ

cr = 1 -

• 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

• AUGC

G S S

k k k

= ( ) | ( ) =

• 1

U

• I

I

j j

• cr

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

Structure

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

Compatible sequence Structure

5’-end 3’-end

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 G G G G G G G C C C C G G G G C C C C C C C U A U U G U A A A A U

Compatible sequence Structure

5’-end 3’-end

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 G 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

Compatible sequence Structure

5’-end 3’-end

Single nucleotides: A U G C , , ,

Single bases pairs are varied independently

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 G G C C C C G G G G C C G G G G G C C C C C U A U U G U A A A A U

Compatible sequence Structure

5’-end 3’-end

Base pairs: AU , UA GC , CG GU , UG

Base pairs are varied in strict correlation

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 G G U 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 G G G G G G G G G G G G G G C U C C C C C C U U U U G G G G G G G G G G C C C C C C C C C C C C C C U U U U A A A A A A A A A A U U

Compatible sequences Structure

5’-end 5’-end 3’-end 3’-end

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 G C G G G G G G G G G C G C C U U G G G G G C C C C C C C U U A A A A A U

Structure Incompatible sequence

5’-end 3’-end

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 π

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

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

RNA 9:1456-1463, 2003

Evidence for neutral networks and shape space covering

Evidence for neutral networks and intersection of apatamer functions

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 Austrian Genome Research Program – GEN-AU 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

Walter Fontana, Santa Fe Institute, NM Christian Reidys, Christian Forst, Los Alamos National Laboratory, NM Peter Stadler, Bärbel Stadler, Universität Leipzig, GE Jord Nagel, Kees Pleij, Universiteit Leiden,NL 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 Stefan Bernhart, Lukas Endler Ulrike Göbel, Institut für Molekulare Biotechnologie, Jena, GE Walter Grüner, Stefan Kopp, Jaqueline Weber

Universität Wien

Web-Page for further information: http://www.tbi.univie.ac.at/~pks