Transcript Protein Design with a Flexible Backbone

Protein Design with Backbone Optimization
Brian Kuhlman
University of North Carolina at Chapel Hill
Rationale for Flexible Backbone Design
• Amino acid mutations often result in backbone rearrangement.
• Backbone rearrangement can allow for more favorable
interactions with target ligands or substrates.
• Novel protein structures or complexes are generally not
designable without backbone optimization.
Flexible Backbone Design Protocols in Rosetta
• Design and backbone optimization of a selected region
of a protein (loop or terminus)
• Design and backbone optimization of a protein-protein
interface
• Design and backbone optimization over a whole
monomeric protein
Protein Design with Backbone Optimization
Starting structure – should resemble final target structure
Design optimal sequence for the protein
Optimize the backbone coordinates
Design final sequence for the protein
Backbone Optimization – Monte Carlo Minimization
1)
2)
3)
4)
random perturbation to phi,psi angles
very rapid rotamer optimization
gradient minimization in phi,psi space
accept moves based on the Metropolis criterion
(1)
(2)
start
(3)
For each cycle of backbone
optimization, ~2000 Monte
Carlo steps were performed
Only phi and psi were varied in the
backbone, all bond distances and
angles were idealized.
Typical Flexible Backbone Optimization Protocol
Design optimal sequence for the protein
~10 cycles
Allow the protein to relax in phi,psi space
During this procedure the –
1) the backbone moves ~ 2 Å RMSD
2) > 50% of the residues typically change identity
3) Lennard-Jones energies became comparable to those in
naturally occurring proteins
Flexible Backbone Design Protocols in Rosetta
• Design and backbone optimization of a selected region
of a protein (loop or terminus)
• Design and backbone optimization of a protein-protein
interface
• Design and backbone optimization over a whole
monomeric protein
Test case: redesign a loop in the context
of a well-folded protein
Protocol for loop design
•
Remove the WT loop
•
Build a new backbone for the loop
from PDB fragments
•
Iterate between designing a
sequence for the loop and
optimizing its conformation
Tenascin
Jenny Hu
Building the Starting Structures for Loop Design
• Select loops from the PDB that best
overlay with the takeoff residues
• Close the loops and remove clashes
with neighboring residues using 3residue fragment insertions, small
random perturbations to phi and psi
angles, and gradient-based
minimization ( low resolution scoring
function )
3 of the starting structures
selected for high resolution
design
Iterating Between Sequence Design and
Backbone Refinement
•
Sequence design: allow all
amino acids for residues in the
loop, neighboring amino acids
are free to adopt alternative
rotamers
Backbone refinement: small
random changes to phi and psi
angles followed by gradient
based minimization (same
energy function used for
sequence design and backbone
refinement)
-138
Design Simulation
-140
Rosetta Full Atom Energy
•
Backbone Refinement
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-144
-146
-148
-150
0
5
10
15
Iteration
Starting seq: LPTQLPVEG
Ending seq: QKTQLPVDG
20
Iterating Between Sequence Design and
Backbone Refinement
Blue: Starting structure / sequence
Green: Minimized structure / sequence
3 Loops Picked for Experimental Validation
( from 7200 flexible backbone design trajectories)
Designed Sequences
WT
L1
L3
L6
FKPLAEIDGI
SMQLSQLEGI
MPPSQPVDGF
ALPSRPLDGF
WT
Loop1
P24
M23
I31
I28
L28
I31
Loop3
Loop6
P23
L23
P24
P24
F31
F31
V28
L28
Fraction Unfolded
The Loop Designs are Folded
Crystal Structure of Loop3
Green: crystal structure
Purple: design model
Resolution: 1.45 Å
Crystal Structure of Loop6
pH = 3
Flexible Backbone Design Protocols in Rosetta
• Design and backbone optimization of a selected region
of a protein (loop or terminus)
• Design and backbone optimization of a protein-protein
interface
• Design and backbone optimization over a whole
monomeric protein
Protocol for Designing Binding Proteins
1) Rigid body docking of design
template on to the target
target
2) Fixed backbone sequence design
of interface residues
3) High resolution refinement of rigid
body orientation and scaffold loops
4) Identify design models that are
most likely to bind the target
Design scaffold
Andrew Leaver-Fay, Ramesh Jha, Glenn Butterfoss
Targeting the p21-Activated Kinase (PAK1)
PAK1 kinase
domain
PAK1
autoinhibitory
domain
Example of Designed Interface
Designed Protein
Target – PAK1
Andrew Leaver-Fay
Flexible Backbone Design Protocols in Rosetta
• Design and backbone optimization of a selected region
of a protein (loop or terminus)
• Design and backbone optimization of a protein-protein
interface
• Design and backbone optimization over a whole
monomeric protein
Successful Design of a Novel Protein Structure
(TOP7)
Red: Design model
Blue: crystal structure
Tm > 100 C°
DG°unf > 10 kcal / mol
Template for a b-Sandwich Protein
55
54
53
10
11
56
34
57
33
80
58
79
52
35
32
59
78
9
12
51
36
31
60
77
8
13
50
37
30
6
1
76
7
14
49
38
29
62
75
6
15
48
39
28
63
74
5
16
47
40
27
64
73
4
17
41
26
65
72
3
18
42
25
66
71
24
67
70
46
45
2
1
N
44
19
20
21
43
22
23
68
69
C
Starting structures for b-sheet Design
Current Status of b-sheet De Novo Design Project
4 sequences selected for experimental study from ~50,000
flexible backbone simulations
• All of them appear to adopt b-structure as evidenced
by circular dichroism
• NMR lines are broad
• Gel filtration indicates that they are not monomeric
What is missing from the b-sheet design process?
•
Do we need to do more conformational sampling to
find a backbone that is designable (positive
design)?
•
Do we need to explicitly destabilize alternative
backbone structures (negative design)?
Can we design a well-folded b-sandwich if we start with a
naturally occurring protein backbone?
1) Strip away naturally occuring side
chains.
2) Design a new sequence allowing all
amino acids at each sequence
position.
Resulting sequence
• 39% identical to WT
• 60% identical in the core
Target Structure: Tenascin
Redesigned Tenascin is Well-Folded
1D-NMR of Redesigned Tenascin
Redesigned Tenascin is more stable than Wild-Type
Tenascin
1.1
1
Fraction Unfolded
0.9
WT Tenascin
0.8
0.7
Redesigned
Tenascin
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
0
20
40
60
Temperature
80
100
Acknowledgements
Loop Design
Jenny Hu
Hengming Ke
Interface Design
Andrew Leaver-Fay
Glenn Butterfoss
Ramesh Jha
b-sheet Design
Jenny Hu