1. Energy Landscapes and Dynamics of Biopolymers
Michael Thomas Wolfinger
University of Vienna
5 March 2012

2. Outline
1 Biopolymer structure
2 Energy landscapes
3 Folding kinetics
4 RNA refolding
5 Summary

3. The RNA model
AA
GCGGAUUUAGCUCAGUUGGGAGAGCGCCAGACUGAAGAUCUGGAGGUCCUGUGUUCGAUCCACAGAAUUCGCACCA
G G
U A
CAU
GC
AU
CG
CG AG
✲ GG A G G ✲
G GAGC U
U CUCG C
UGA A C
UU AG U
A G
UA C U
AU A G UU
C
GU C C
GC U AG
CG
GC
A
C
C
A
´´´´´´´ ºº ´´´´ ºººººººº µµµµ º ´´´´´ ººººººº µµµµµ ººººº ´´´´´ ººººººº µµµµµ µµµµµµµ ºººº
A secondary structure is a list of base pairs that fulfills two constraints:
A base may participate in at most one base pair.
Base pairs must not cross, i.e., no two pairs (i, j) and (k, l) may have i < k < j < l.
(no pseudo-knots)
The optimal as well as the suboptimal structures can be computed recursively.

4. The HP-model
F(l) L(l)
H
In this simplified model, a conformation is a F(f)
R(r)
self-avoiding walk (SAW) on a given lattice in 2 P
or 3 dimensions. Each bond is a straight line, F(f) R(b) L(f)
bond angles have a few discrete values. The 20 L(r)
letter alphabet of amino acids (monomers) is
reduced to a two letter alphabet, namely H and FRRLLFLF
P. H represents hydrophobic monomers, P
represents hydrophilic or polar monomers. L
U
L
Advantages: B F
B F
lattice-independent folding algorithms
R
R
D
P F
simple energy function W
M Z
hydrophobicity can be reasonably modeled
Q
Q L F
R X
B R
N Y
B P

5. Energy functions
RNA Lattice Proteins
The standard energy model expresses the free The energy function for a sequence with
energy of a secondary structure S as the sum n residues S = s1 s2 . . . sn with si ∈ A =
of the energies of its loops l {a1 , a2 , . . . , ab }, the alphabet of b residues, and
an overall configuration x = (x1 , x2 , . . . , xn ) on
a lattice L can be written as the sum of pair
E (S) = ∑ E (l)
l∈S
potentials
E (S, x) = ∑ Ψ[si , sj ].
i < j −1
|xi − xj | = 1
H P N X
i j H P H −4 0 0 0
H −1 0 P 0 1 −1 0
N P 0 0 N 0 −1 1 0
1
G
A U
X 0 0 0 0
A G
U U GC U
A UG
G A UUC
C G U C G A U C UUG
U U G
A UCCC C
G U A G G U GU
GC A G G G
AU
C G
G G
G C
A U
U UAC
G U
GG
E = −17.5kcal/mol E = −16

6. Folding landscape - energy landscape
The energy landscape of a biopolymer molecule is a complex surface of
the (free) energy versus the conformational degrees of freedom.
Number of RNA secondary structures dim Lattice Type µ γ
3 SQ 2.63820 1.34275
cn ∼ 1.86n · n− 2 2 TRI 4.15076 1.343
dynamic programming algorithms available
HEX 1.84777 1.345
SC 4.68391 1.161
Number of LP structures 3 BCC 6.53036 1.161
cn ∼ µ n · nγ −1 FCC 10.0364 1.162
problem is NP-hard
Formally, three things are needed to construct an energy landscape:
A set X of configurations
an energy function f : X → R
a symmetric neighborhood relation N : X × X

7. Folding landscape - energy landscape
The energy landscape of a biopolymer molecule is a complex surface of
the (free) energy versus the conformational degrees of freedom.
Number of RNA secondary structures dim Lattice Type µ γ
3 SQ 2.63820 1.34275
cn ∼ 1.86n · n− 2 2 TRI 4.15076 1.343
dynamic programming algorithms available
HEX 1.84777 1.345
SC 4.68391 1.161
Number of LP structures 3 BCC 6.53036 1.161
cn ∼ µ n · nγ −1 FCC 10.0364 1.162
problem is NP-hard
Formally, three things are needed to construct an energy landscape:
A set X of configurations
an energy function f : X → R
a symmetric neighborhood relation N : X × X

8. Folding landscape - energy landscape
The energy landscape of a biopolymer molecule is a complex surface of
the (free) energy versus the conformational degrees of freedom.
Number of RNA secondary structures dim Lattice Type µ γ
3 SQ 2.63820 1.34275
cn ∼ 1.86n · n− 2 2 TRI 4.15076 1.343
dynamic programming algorithms available
HEX 1.84777 1.345
SC 4.68391 1.161
Number of LP structures 3 BCC 6.53036 1.161
cn ∼ µ n · nγ −1 FCC 10.0364 1.162
problem is NP-hard
Formally, three things are needed to construct an energy landscape:
A set X of configurations
an energy function f : X → R
a symmetric neighborhood relation N : X × X

9. Folding landscape - energy landscape
The energy landscape of a biopolymer molecule is a complex surface of
the (free) energy versus the conformational degrees of freedom.
Number of RNA secondary structures dim Lattice Type µ γ
3 SQ 2.63820 1.34275
cn ∼ 1.86n · n− 2 2 TRI 4.15076 1.343
dynamic programming algorithms available
HEX 1.84777 1.345
SC 4.68391 1.161
Number of LP structures 3 BCC 6.53036 1.161
cn ∼ µ n · nγ −1 FCC 10.0364 1.162
problem is NP-hard
Formally, three things are needed to construct an energy landscape:
A set X of configurations
an energy function f : X → R
a symmetric neighborhood relation N : X × X

10. The move set
o
o o
o o
o o
o
o o o
o o
o o
o o FRLLRLFRR FRLLFLFRR
o o
o
o o
FUDLUDLU FURLUDLU
For each move there must be an inverse move
Resulting structure must be in X
Move set must be ergodic

11. Energy barriers and barrier trees
Some topological definitions:
A structure is a
d
local minimum if its energy is lower c
b
than the energy of all neighbors
5 a
local maximum if its energy is higher 4
3
than the energy of all neighbors
2
saddle point if there are at least two E
local minima thar can be reached by a 1*
downhill walk starting at this point
We further define
a walk between two conformations x and y as a list of conformations
x = x1 . . . xm+1 = y such that ∀1 ≤ i ≤ m : N(xi , xi+1 )
the lower part of the energy landscape (written as X ≤η ) as all
conformations x such that E (S, x) ≤ η (with a predefined threshold η ).
C. Flamm, I. L. Hofacker, P. F. Stadler, and M. T. Wolfinger.
Barrier trees of degenerate landscapes.
Z. Phys. Chem., 216:155–173, 2002.

12. Energy barriers and barrier trees
Some topological definitions:
A structure is a
d
local minimum if its energy is lower c
b
than the energy of all neighbors
5 a
local maximum if its energy is higher 4
3
than the energy of all neighbors
2
saddle point if there are at least two E
local minima thar can be reached by a 1*
downhill walk starting at this point
We further define
a walk between two conformations x and y as a list of conformations
x = x1 . . . xm+1 = y such that ∀1 ≤ i ≤ m : N(xi , xi+1 )
the lower part of the energy landscape (written as X ≤η ) as all
conformations x such that E (S, x) ≤ η (with a predefined threshold η ).
C. Flamm, I. L. Hofacker, P. F. Stadler, and M. T. Wolfinger.
Barrier trees of degenerate landscapes.
Z. Phys. Chem., 216:155–173, 2002.

13. The lower part of the energy landscape
Two conformations x and y are mutually accessible at the level η
(written as x η y ) if there is a walk from x to y such that all
conformations z in the walk satisfy E (S, z) ≤ η . The saddle height
ˆ
f (x, y ) of x and y is defined by
f (x, y ) = min{η | x
ˆ η y}
≤η
Given the set of all local minima Xmin below threshold η , the lower
energy part X ≤η of the energy landscape can alternatively be written as
≤η ˆ
X ≤η = {y | ∃x ∈ Xmin : f (x, y ) ≤ η }
Given a restricted set of low-energy conformations, Xinit , and a reasonable
value for η , the lower part of the energy landscape can be calculated.
M. T. Wolfinger, S. Will, I. L. Hofacker, R. Backofen, and P. F. Stadler.
Exploring the lower part of discrete polymer model energy landscapes.
Europhys. Lett., 2006.

14. The Flooder approach
E
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Ep 11111111111111111111
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D 11111111111111111111
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C
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C
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B
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00000000000000000000 B
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A A

15. The concept of Barriers

16. The algorithm of Barriers
Barriers
Require: all suboptimal secondary structures within a certain energy range from mfe
1: B ⇐ 0 /
2: for all x ∈ subopt do
3: K ⇐0 /
4: N ⇐ generate neighbors(x)
5: for all y ∈ N do
6: if b ⇐ lookup hash(y ) then
7: K ⇐ K ∪b
8: end if
9: end for
10: if K = 0 then
/
11: B ⇐ B ∪ {x}
12: end if
13: if |K | ≥ 2 then
14: merge basins(K )
15: end if
16: write hash(x)
17: end for

17. The flooding algorithm
1

18. The flooding algorithm
1 2

19. The flooding algorithm
1 2 3

20. The flooding algorithm
1 2 3 4

21. Barrier tree example

22. Information from the barrier trees
Local minima
Saddle points
Barrier heights
Gradient basins
Partition functions and free energies of (gradient) basins
A gradient basin is the set of all initial points from which a gradient walk
(steepest descent) ends in the same local minimum.

23. Folding kinetics
Biomolecules may have kinetic traps which prevent them from reaching
equilibrium within the lifetime of the molecule. Long molecules are often
trapped in such meta-stable states during transcription.
Possible solutions are
Stochastic folding simulations (predict folding pathways)
Predicting structures for growing fragments of the sequence
Analysis of the energy landscape based on complete suboptimal
folding

24. Biopolymer dynamics
The probability distribution P of structures as a function of time is ruled
by a set of forward equations, also known as the master equation
dPt (x)
= ∑ [Pt (y )kxy − Pt (x)kyx ]
dt y =x
Given an initial population distribution, how does the system evolve in
time? (What is the population distribution after n time-steps?)
d
Pt = UPt =⇒ Pt = e tU P0
dt

25. Kinfold: A stochastic kinetic foling algorithm
Simulate folding kinetics by a rejection-less Monte-Carlo type
algorithm:
Generate all neighbors using the move-set
Assign rates to each move, e.g. P1 P2
P8 P3
P7 P4
∆E P6 P5
Pi = min 1, exp −
kT
Select a move with probability proportional to its rate
Advance clock 1/ ∑i Pi .

26. Treekin: Barrier tree kinetics
Local minima Gradient basins
Saddle points Partition functions
Barrier heights Free energies of (gradient) basins
With this information, a reduced dynamics can be formulated as a Markov
process by means of macrostates (i.e. basins in the barrier tree) and
Arrhenius-like transition rates between them.
1
d
Pt = UPt =⇒ Pt = e tU P0 0.8
✟✟
✙
✟
dt ❍❍
population percentage
❍❥
❍
0.6
✟
macro-states form a partition of the ✟✟
✙
✟
full configuration space 0.4
transition rates between macro-states 0.2 ✠
❍❍
∗ ❍❥
❍
rβ α = Γβ α exp −(Eβ α − Gα )/kT
0
-2 -1 0 1 2 3 4 5 6
10 10 10 10 10 10 10 10 10
time
M. T. Wolfinger, W. A. Svrcek-Seiler, C. Flamm, I. L. Hofacker, and P. F. Stadler.
Efficient computation of RNA folding dynamics.
J. Phys. A: Math. Gen., 37(17):4731–4741, 2004.

27. Dynamics of a short artificial RNA
CUGCGGCUUUGGCUCUAGCC n = 20
6.0
34
33
32
4.0
31
30
29
28
27
25
26
24
23
22
21
20
2.0
18
19
17
16
14
13
15
12
11
10
0.0
9
8
7
6
-2.0
5
4
3
2
-4.0
1
1
mfe
2
0.8 3
4
5
8
population probability
0.6
0.4
0.2
0 -2 0 2 3
-1 1 4
10 10 10 10 10 10 10
time

28. Dynamics of tRNA
GCGGAUUUAGCUCAGDDGGGAGAGCGCCAGACUGAAYAUCUGGAGGUCCUGUGTPCGAUCCACAGAAUUCGCACCA
1 1
mfe
5
0.8 80 0.8
56
population probability
population probability
0.6 0.6
0.4 0.4
0.2 0.2
0 0 0 3 5 6 7 8 9
1 2 3 4 5 6 7 8 4 10
10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
time time

29. Dynamics of lattice proteins: HEX/TET lattice
NNHHPPNNPHHHHPXP n = 16
2.0
1
NNHHPPNNPHHHHPXP n = 16
1.0 1
4
0.8 7
0.0 24
25
25 (pinfold)
population probability
-1.0 0.6
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
-2.0
13
14
15
16
17
18
19
20
21
22
23
0.4
-3.0
0.2
-4.0
9
10
8
11
12
-5.0
0 -3
2
3
4
5
6
7
1
-2 -1 0 1 2 3 4 5
10 10 10 10 10 10 10 10 10
time
-2.0 1
-4.0
1
0.8 4
16
-6.0 17
population probability
37
0.6 37 (pinfold)
-8.0
-10.0
0.4
-12.0
0.2
40
34
39
42
48
47
46
49
24
25
29
28
30
32
31
33
36
38
45
41
35
37
44
27
50
26
43
-14.0
9
4
5
11
10
20
19
6
18
8
7
12
15
14
17
16
22
13
21
23
3
2
1
0 -2 0 2 6 8
4
10 10 10 10 10 10
time

30. Barrier tree kinetics - problems and pitfalls
The method works fine for moderately sized systems.
Currently, we consider approx. 100 million structures within a single run
of Barriers to calculate the topology of the landscape.
However, we are interested in larger systems:
biologically relevant RNA switches
large 3D lattice proteins
The next steps:
use high-level diagonalization routines for sparse matrices
calculate low-energy structures
sample (thermodynamics properties of) individual basins
sample low-enery refolding paths

31. Barrier tree kinetics - problems and pitfalls
The method works fine for moderately sized systems.
Currently, we consider approx. 100 million structures within a single run
of Barriers to calculate the topology of the landscape.
However, we are interested in larger systems:
biologically relevant RNA switches
large 3D lattice proteins
The next steps:
use high-level diagonalization routines for sparse matrices
calculate low-energy structures
sample (thermodynamics properties of) individual basins
sample low-enery refolding paths

32. The PathFinder tool
A heuristic approach to efficiently estimate low-energy refolding paths
Overall procedure for direct paths:
1 Calculate distance bewteen start and target structure
2 Generate all neighbors of the start structure whose distance to the target is less
than the distance of the start structure
3 Sort those neighbor structures by their energies
4 Take the n energetically best structures, take them as new starting points and
repeat the procedure until the stop structure is found
5 If a path has been found, try to find another one with lower energy barrier
Energy
Energy
START START
STOP STOP
8 7 6 5 4 3 2 1 0 8 7 6 5 4 3 2 1 0
Distance Distance

33. PathFinder example - SV11
SV11 is a RNA switch of length 115
E = −69.2 kcal/mol E = −96.4 kcal/mol
metastable stable
template for Qβ replicase not a template

34. SV11 refolding path 1/3: Esaddle = −52.2 kcal/mol
-50
✛
✛
-60
Energy (kcal/mol)
-70
■
❅
❅
❅ ✕
✁
✁
✁
-80 ✁
-90
✲
-100
0 10 20 30 40 50 60 70 80
steps

35. SV11 refolding path 2/3: Esaddle = −57.7 kcal/mol
-50
✟
✟✟
✙
-60
Energy (kcal/mol)
-70 ❄
■
❅
❅
❅
-80
✒
-90
✲
-100
0 10 20 30 40 50 60 70 80
steps

36. SV11 refolding path 3/3: Esaddle = −59.2 kcal/mol
-50
✟
✟✟
✙
-60
Energy (kcal/mol)
-70 ❄
■
❅
❅
❅ GC
GC
A
GC
C U
U
AU
CG
CG
CG
CG
CG
-80
CG
UAA
UAA
✒
CG
GC
GC
GC
GC
GC
GC
UA
CG
AU
CC G
C U GG C GA
CG A G C
C U GC
U G G C GUU U C
C GA G
AC C GC C C C
U CU A
U G A
A G A U A
A U A
U A
G GCU
GGCC
-90
✲
-100
0 10 20 30 40 50 60 70 80
steps

37. SV11 refolding paths
-50
-60
Energy (kcal/mol)
-70
-80
-90
-100
0 10 20 30 40 50 60 70 80
steps

38. libPF - a generic path sampling library
In practice, this path sampling heuristics is implemented as a C
library
All structures along a path are stored in a hash and therefore
available for the next iterations
Heuristics routines are strictly separated from model-dependent
routines, i.e. the library is completely generic
Currently, RNA secondary structures and lattice proteins are
implemented
It is easy to extend the functionality to other discrete systems

39. Conclusion
Discrete models allow a detailed study of the energy surface
Barrier trees represent the landscape topology
The lower part of the energy landscape is accessible by a flooding
approach
Macrostate folding kinetics reduces simulation time drastically
A path sampling approach yields low-energy refolding paths and is a
valuable tool for further kinetics studies

40. Conclusion
Discrete models allow a detailed study of the energy surface
Barrier trees represent the landscape topology
The lower part of the energy landscape is accessible by a flooding
approach
Macrostate folding kinetics reduces simulation time drastically
A path sampling approach yields low-energy refolding paths and is a
valuable tool for further kinetics studies

41. Conclusion
Discrete models allow a detailed study of the energy surface
Barrier trees represent the landscape topology
The lower part of the energy landscape is accessible by a flooding
approach
Macrostate folding kinetics reduces simulation time drastically
A path sampling approach yields low-energy refolding paths and is a
valuable tool for further kinetics studies

42. Conclusion
Discrete models allow a detailed study of the energy surface
Barrier trees represent the landscape topology
The lower part of the energy landscape is accessible by a flooding
approach
Macrostate folding kinetics reduces simulation time drastically
A path sampling approach yields low-energy refolding paths and is a
valuable tool for further kinetics studies

43. Conclusion
Discrete models allow a detailed study of the energy surface
Barrier trees represent the landscape topology
The lower part of the energy landscape is accessible by a flooding
approach
Macrostate folding kinetics reduces simulation time drastically
A path sampling approach yields low-energy refolding paths and is a
valuable tool for further kinetics studies

44. Conclusion
Discrete models allow a detailed study of the energy surface
Barrier trees represent the landscape topology
The lower part of the energy landscape is accessible by a flooding
approach
Macrostate folding kinetics reduces simulation time drastically
A path sampling approach yields low-energy refolding paths and is a
valuable tool for further kinetics studies

45. References
Christoph Flamm, Ivo Hofacker, Peter Stadler
Rolf Backofen, Sebastian Will, Martin Mann
M. T. Wolfinger, W. A. Svrcek-Seiler, C. Flamm, I. L. Hofacker, and P. F. Stadler.
Efficient computation of RNA folding dynamics.
J. Phys. A: Math. Gen., 37(17):4731–4741, 2004.
M. T. Wolfinger, S. Will, I. L. Hofacker, R. Backofen, and P. F. Stadler.
Exploring the lower part of discrete polymer model energy landscapes.
Europhys. Lett., 74(4):725–732, 2006.
Ch. Flamm, W. Fontana, I.L. Hofacker, and P. Schuster.
RNA folding at elementary step resolution.
RNA, 6:325–338, 2000
C. Flamm, I. L. Hofacker, P. F. Stadler, and M. T. Wolfinger.
Barrier trees of degenerate landscapes.
Z. Phys. Chem., 216:155–173, 2002.