designing rna structure and functionpks/presentation/heidelberg-1312.pdf · designing rna structure...

Designing RNA Structure and Function A Toolbox for Synthetic Biology

Peter Schuster

Institut für Theoretische Chemie, Universität Wien, Austria and

The Santa Fe Institute, Santa Fe, New Mexico, USA

Synthetic Biology – From understanding to application

DKFZ-Heidelberg, 09.– 11.12.2013

Web-Page for further information:

http://www.tbi.univie.ac.at/~pks

Prologue

The goals of synthetic biology

A general method for genetically encoding unnatural amino acids in live cells.

Qian Wang, Angela R. Parrish, Lei Wang. Expanding the genetic code for biological studies. Chemistry & Biology 16:323-336, 2009.

Lei Wang, Peter G. Schultz. Expanding the genetic code. Angew.Chem.Int.Ed. 44:34-66, 2005.

1. One RNA sequence – one structure

2. Many RNA sequences – one structure

3. One RNA sequence – many structures

4. RNA switches

1. One RNA sequence – one structure

2. Many RNA sequences – one structure

3. One RNA sequence – many structures

4. RNA switches

one sequence one structure function

The paradigm of structural biology

GCGGA UUGCACCA

OCH2

OHO

O

PO

O

O

N1

OCH2

OHO

PO

O

O

N2

OCH2

OHO

PO

O

O

N3

OCH2

OHO

PO

O

O

N4

N A U G Ck = , , ,

3' - end

5' - end

Na

Na

Na

Na

5'-end 3’-endGCGGAU AUUCGCUUA AGUUGGGA G CUGAAGA AGGUC UUCGAUC A ACCAGCUC GAGC CCAGA UCUGG CUGUG CACAG

Definition of RNA structure

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

Criterion: Minimum free energy (mfe)

Rules: _ ( _ ) _ {AU,CG,GC,GU,UA,UG}

RNA sequence

RNA structure of minimal free energy

RNA folding:

Structural biology, spectroscopy of biomolecules, understanding molecular function

Empirical parameters

Biophysical chemistry: thermodynamics and kinetics

Sequence, structure, and design

Vienna RNA-Package

Version 2.0 http://www.tbi.univie.ac.at

RNA folding into secondary structures

Ivo L. Hofacker, Walter Fontana, Peter F. Stadler, Sebastian Bonhoeffer, Manfred Tacker, Peter Schuster. 1994. Fast folding and comparison of RNA secondary structures. Monath. Chem. 125:167-188.

Michael S. Waterman, T. F. Smith. 1978. RNA secondary structures: A complete mathematical analysis. Math.Biosci. 42:257-266.

Ruth Nussinov, A. B. Jacobson. 1980. Fast algorithm for predicting the secondary structure of single-stranded RNA. Proc.Natl.Acad.Sci. USA 77:6309-6313.

Michael Zuker, P. Stiegler. 1981. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 9:133-148.

Conventional definition of RNA secondary structures

Restrictions on physically acceptable mfe-structures: 3 and 2

RNA sequence

RNA structure of minimal free energy

RNA folding:

Structural biology, spectroscopy of biomolecules, understanding molecular function

Inverse folding of RNA:

Biotechnology, design of biomolecules with predefined structures and functions

Inverse Folding Algorithm

Iterative determination of a sequence for the given secondary structure

Sequence, structure, and design

Vienna RNA-Package

Version 2.0 http://www.tbi.univie.ac.at

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 ... base or base pair mutation operator

dS (Si,Sj) ... distance between the two structures Si and Sj ‚Unsuccessful trial‘ ... termination after n steps

Approach to the target structure Sk in the inverse folding algorithm

Target structure Sk

Initial trial sequences

Target sequence

Stop sequence of anunsuccessful trial

Intermediate compatible sequences

Intermediate compatible sequences

Department of Statistics University of Oxford

Rune B. Lyngsø, James J.W. Anderson, Elena Sizikova, Amarendra Badugu, Thomas Hyland, Jotun Hein. 2012. fRNAkenstein: Multiple traget inverse RNA folding. BMC Bioinformatics 13:e260.

Mirela Andronescu, Antony P. Fejes, Frank Hutter, Holger H. Hoos, Anne Condon. 2004. A new algorithm for RNA secondary structure design. J. Mol. Biol. 336:607-624.

Department of Computer Science University of British Comlumbia

Vancouver, BC, Canada

Robert M. Dirks, Milo Lin, Erik Winfree, Niles A. Pierce. 2004. Paradigms for computational nucleic acid design. Nucleic Acids Research 32:1392-1403.

California Institute of Technology Pasadena, CA, USA

RNA inverse folding: Secondary structures sequences

1. One RNA sequence – one structure

2. Many RNA sequences – one structure

3. One RNA sequence – many structures

4. RNA switches

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

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

Criterion: Minimum free energy (mfe)

Rules: _ ( _ ) _ {AU,CG,GC,GU,UA,UG}

N= 4n

NS < 3n

Every point in sequence space is equivalent

Sequence space of binary sequences with chain length n = 5

Sketch of structure space

Structures are not equivalent in structure space

many genotypes one phenotype

Evolution as a global phenomenon in genotype space

1. One RNA sequence – one structure

2. Many RNA sequences – one structure

3. One RNA sequence – many structures

4. RNA switches

Computation of suboptimal 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

Interconversion of suboptimal structures

Base pair probability derived from the partition function Q(T)

( ) ( ) ( ) ( ) ( ) ( )( ) ( )

∑∑

∑∑

≠

−−

−=−=

=

==

ijj ijiij ijiji

k k

kTkkkijk kij

pppps

TTQ

TQegTSaTTXp k

,

/

1withln

/with, 0

γ

γγ εε base pair probability

base pairing entropy

John S. McCaskill. The equilibrium partition function and base pair binding probabilities for RNA secondary structures. Biopolymers 29:1105-1119, 1990.

3'

5'

UUGGAGUACACAACCUGUACACUCUUUC

Example of a small RNA molecule: n=28

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

„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

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 CC

UU

UC

UC

AC

AU

GU

CC

AA

CA

CA

UG

AG

GU

U UU

GG

AG

UA

CA

CA

AC

CU

GU

AC

AC

UC

UU

UC

U U G G A G UACACA A C

CUGUACACUCU

UUC

U UG

GA

GUA

C AC

AA

CCU

GUA

CAC

UC

U

UUC

U UG

GAGU

AC

ACA A

CC

UG

UA

CA

CUC

UU

UC

second suboptimal configuration

first suboptimal configuration

minimum free energyconfiguration

∆E = 0.55 kcal / mole0→2

∆E = 0.50 kcal / mole0 1→

G = - 5.39 kcal / mole0

3'

5'

Kinetic folding of RNA secondary structures

Michael T. Wolfinger, W. Andreas Svrcek-Seiler, Christoph Flamm, Ivo L. Hofacker, Peter F. Stadler. 2004. Efficient computation of RNA folding dynamics. Z.Phys.A: Math.Gen. 37:4731-4741.

Christoph Flamm, Walter Fontana, Ivo. L. Hofacker, Peter Schuster. 2000. RNA folding kinetics at elementary step resolution. RNA. 6:325-338.

Christoph Flamm, Ivo. L. Hofacker, Sebastian Maurer-Stroh, Peter F. Stadler, Martin Zehl. 2001. Design of multistable RNA molecules. RNA. 7:254-265

Christoph Flamm, Ivo. L. Hofacker, Peter F. Stadler, Michael T. Wolfinger. 2002. Barrier trees of degenerate landscapes. Z.Phys.Chem. 216:155-173.

Sh

S1(h)

S6(h)

S7(h)

S5(h)

S2(h)

S9(h)

Free

ene

rgy

G

0

Local minimum

Suboptimal conformations

Search for local minima in conformation space

Free

ene

rgy

G

0

"Reaction coordinate"

Sk

S{Saddle point T {k

Free

ene

rgy

G

0

Sk

S{

T {k

"Barrier tree"

Definition of a ‚barrier tree‘

open chain

A nucleic acid molecule folding in two dominant conformations

Folding dynamics of the sequence GGCCCCUUUGGGGGCCAGACCCCUAAAAAGGGUC

Interconversion of suboptimal structures

RNA inverse folding and structure design

Vienna RNA Package

Ivo L. Hofacker, Walter Fontana, Peter F. Stadler, Sebastian Bonhoeffer, Manfred Tacker, Peter Schuster. 1994. Fast folding and comparison of RNA secondary structures. Monath. Chem. 125:167-188.

Christoph Flamm, Ivo L. Hofacker, Sebastian Maurer-Stroh, Peter F. Stadler, Martin Zehl. 2001. Design of multistable RNA molecules. RNA 7:254-265.

Ronny Lorenz, Stephan H. Bernhart, Christian Höner zu Siederissen, Hakim Tafer, Christoph Flamm, Peter F. Stadler, Ivo L. Hofacker. 2011. Vienna RNA Package 2.0. Algorithms for Molecular Bioology 6:e26.

Christian Höner zu Siederissen, Stefan Hammer, Ingrid Abfalter, Ivo L. Hofacker, Christoph Flamm, Peter F. Stadler. 2013. Computational design of RNAs with complex energy landscapes. Biopolymers 99:1124-1136.

Peter Schuster. 2006. Prediction of RNA secondary structures: From theory to models and real molecules. Reports on Progress in Physics 69:1419-1477.

Christian Höner zu Siederissen, Stefan Hammer, Ingrid Abfalter, Ivo L. Hofacker, Christoph Flamm , Peter F. Stadler. 2013. Computational design of RNAs with complex energy landscapes. Biopolymers 99:1124-1136.

Christian Höner zu Siederissen, Stefan Hammer, Ingrid Abfalter, Ivo L. Hofacker, Christoph Flamm , Peter F. Stadler. 2013. Computational design of RNAs with complex energy landscapes. Biopolymers 99:1124-1136.

Christian Höner zu Siederissen, Stefan Hammer, Ingrid Abfalter, Ivo L. Hofacker, Christoph Flamm , Peter F. Stadler. 2013. Computational design of RNAs with complex energy landscapes. Biopolymers 99:1124-1136.

Christian Höner zu Siederissen, Stefan Hammer, Ingrid Abfalter, Ivo L. Hofacker, Christoph Flamm , Peter F. Stadler. 2013. Computational design of RNAs with complex energy landscapes. Biopolymers 99:1124-1136.

Christian Höner zu Siederissen, Stefan Hammer, Ingrid Abfalter, Ivo L. Hofacker, Christoph Flamm , Peter F. Stadler. 2013. Computational design of RNAs with complex energy landscapes. Biopolymers 99:1124-1136.

Christian Höner zu Siederissen, Stefan Hammer, Ingrid Abfalter, Ivo L. Hofacker, Christoph Flamm , Peter F. Stadler. 2013. Computational design of RNAs with complex energy landscapes. Biopolymers 99:1124-1136.

1. One RNA sequence – one structure

2. Many RNA sequences – one structure

3. One RNA sequence – many structures

4. RNA switches

JN2C

A

A

AGAA

A UU

UCUU

U

UUUUUUU UUU UC

UUUUU

UG

GG

GGG GG

G

C

C

CCC

AGAA

A U

GG

G

CC

C GG C

AAGA

GC

GC

AGAA

GG CC

C

5' 5'3' 3'

CUGUUUUUGCA U AGCUUCUGUUGGCAGAAGC GCAGAAGC

-19.5 kcal·mol-1 -21.9 kcal·mol-1

A

A

A

B

B B C

C

C

3

3

3

15

15

15

36

36

36

24

24

24

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. Nucleic Acids Res. 34:3568-3576 (2006)

Synthetic RNA switches

Loop BC

Loop AB

SS A

C K

C T

G15-

G24-

G30-

G36-G39-

G9-

G3-

5 5 5 5 5 5AlT1D

SS C

G15-

G30-

C K 5 5 5Al

T1D

C K 5 5 5Al

T1D

T1 V1 T2K K KT T T

JN2C

T1 V1 T2 T1 V1 T2

5’ hairpin 3’-hairpin

JN2C small fragments

Loop BCLoop AB

-G39

-G24

JN1LH

1D

1D

1D

2D

2D

2D

R

R

R

GGGGUGGAAC GUUC GAAC GUUCCUCCCCACGAG CACGAG CACGAG

-28.6 kcal·mol-1

G/

-31.8 kcal·mol-1

GGG

G

GG

CCC

CC

CAA

UU

UU

G

G C

CUU A

A

GG

G

CCC

AA

AA

G C

GCAA

GC

/G

-28.2 kcal·mol-1

G G

G GG

GGG

CCC

CC C

C C

U

G GG GC C

C CA AA A

A AA AU U

U U

UG

GC

CA A

-28.6 kcal·mol-1

3

3

3

13

13

13

23

23

23

33

33

33

44

44

44

5'

5' 3’

3’

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. Nucleic Acids Res. 34:3568-3576 (2006)

Synthetic RNA switches

Loop2D

Loop R

Loop1D

C K

C T

G13-

G23-

G33-

C44-

G3-

5 5 5 5 5 5Al

T1D

5

T1 V1 T2K K KT T T

T13 h

J1LH sequencing gels

45

89

11

19 202425 27

33 3436 38 394146 47

3

49

1

2

67

10

12 13 1415

16 17182122 232628 29 30313235 3740 4243

44454850

-26.0

-28.0

-30.0

-32.0

-34.0

-36.0

-38.0

-40.0

-42.0

-44.0

-46.0

-48.0

-50.0

2.77

5.32

2.09

3.42.362.

44

2.44

2.44

1.46 1.44

1.66

1.9

2.14 2.512.14

2.51

2.14 1.47 1.49

3.04 2.973.04

4.886.136.

8

2.89

Free

ene

rgy

[kc

al /

mol

e]

J1LH barrier tree

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

The thiamine-pyrophosphate riboswitch S. Thore, M. Leibundgut, N. Ban. Science 312:1208-1211, 2006.

The thiamine-pyrophosphate riboswitch

S. Thore, M. Leibundgut, N. Ban. Science 312:1208-1211, 2006.

M. Mandal, B. Boese, J.E. Barrick, W.C. Winkler, R.R, Breaker. Cell 113:577-586 (2003)

Coworkers

Peter Stadler, Bärbel M. Stadler, Universität Leipzig, GE

Jord Nagel, Kees Pleij, Universiteit Leiden, NL

Walter Fontana, Harvard Medical School, MA

Martin Nowak, Harvard University, MA

Christian Reidys, University of Southern Denmark, Odense, DK

Christian Forst, University of Texas, Southwestern Medical Center, TX

Ulrike Göbel, Walter Grüner, Stefan Kopp, Jaqueline Weber, Institut für Molekulare Biotechnologie, Jena, GE

Ivo L.Hofacker, Christoph Flamm, Andreas Svrček-Seiler, Universität Wien, AT

Kurt Grünberger, Michael Kospach , Andreas Wernitznig, Stefanie Widder,

Stefan Wuchty, Jan Cupal, Stefan Bernhart, Ulrike Langhammer, Ulrike Mückstein, Universität Wien, AT

Universität Wien

Universität Wien

Acknowledgement of support

Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Projects No. 09942, 10578, 11065, 13093

13887, and 14898

Wiener Wissenschafts-, Forschungs- und Technologiefonds (WWTF) Project No. Mat05

Jubiläumsfonds der Österreichischen Nationalbank

Project No. Nat-7813

European Commission: Contracts No. 98-0189, 12835 (NEST)

Austrian Genome Research Program – GEN-AU: Bioinformatics Network (BIN)

Österreichische Akademie der Wissenschaften

Siemens AG, Austria

Universität Wien and the Santa Fe Institute

Thank you for your attention!

Web-Page for further information:

http://www.tbi.univie.ac.at/~pks