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rotein Design with Backbone Optimizati Brian Kuhlman University of North Carolina at Chapel Hill
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Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Jan 20, 2016

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Page 1: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Protein Design with Backbone Optimization

Brian Kuhlman

University of North Carolina at Chapel Hill

Page 2: 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.

Page 3: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

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

Page 4: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

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

Page 5: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

start

1) random perturbation to phi,psi angles2) very rapid rotamer optimization3) gradient minimization in phi,psi space4) accept moves based on the Metropolis criterion

For each cycle of backbone optimization, ~2000 Monte Carlo steps were performed

Backbone Optimization – Monte Carlo Minimization

(1)

(2)

(3) Only phi and psi were varied in the backbone, all bond distances and angles were idealized.

Page 6: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Design optimal sequence for the protein

Allow the protein to relax in phi,psi space

~10 cycles

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

Typical Flexible Backbone Optimization Protocol

Page 7: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

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

Page 8: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Test case: redesign a loop in the context of a well-folded protein

Tenascin

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

Jenny Hu

Page 9: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

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 3-residue 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

Page 10: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

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)

-150

-148

-146

-144

-142

-140

-138

0 5 10 15 20

Iteration

Ro

se

tta

Fu

ll A

tom

En

erg

y

Design Simulation

Backbone Refinement

Starting seq: LPTQLPVEGEnding seq: QKTQLPVDG

Page 11: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Iterating Between Sequence Design and Backbone Refinement

Blue: Starting structure / sequence

Green: Minimized structure / sequence

Page 12: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

3 Loops Picked for Experimental Validation( from 7200 flexible backbone design trajectories)

Designed Sequences

WT FKPLAEIDGIL1 SMQLSQLEGIL3 MPPSQPVDGFL6 ALPSRPLDGF

Page 13: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

WT Loop1

Loop3 Loop6

P24 M23

L28

I31

I28 I31

V28 L28

F31

P24

F31

P23

P24

L23

Page 14: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

The Loop Designs are FoldedF

ract

ion

Unf

olde

d

Page 15: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Crystal Structure of Loop3

Green: crystal structurePurple: design model

Resolution: 1.45 Å

Page 16: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

pH = 3

Crystal Structure of Loop6

Page 17: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

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

Page 18: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Protocol for Designing Binding Proteins

target

Design scaffold

1) Rigid body docking of design template on to the 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

Andrew Leaver-Fay, Ramesh Jha, Glenn Butterfoss

Page 19: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Targeting the p21-Activated Kinase (PAK1)

PAK1 kinase domain

PAK1 autoinhibitory domain

Page 20: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Example of Designed Interface

Target – PAK1

Designed Protein

Andrew Leaver-Fay

Page 21: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

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

Page 22: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Successful Design of a Novel Protein Structure (TOP7)

Red: Design modelBlue: crystal structure

Tm > 100 C°G°unf > 10 kcal / mol

Page 23: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

N

54 55 56 57 C

1

2

3

4

5

6

7

8

9

10 11

12

13

14

15

16

17

18

19

20 21 22 23

52

24

25

26

27

28

29

30

31

32

3334

35

36

37

38

39

40

41

42

4344

45

46

47

48

49

50

51

53 58

59

60

61

62

63

64

65

66

67

68 69

70

71

72

73

74

75

76

77

78

79

80

Template for a -Sandwich Protein

Page 24: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Starting structures for -sheet Design

Page 25: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Current Status of -sheet De Novo Design Project

4 sequences selected for experimental study from ~50,000 flexible backbone simulations

• All of them appear to adopt -structure as evidenced by circular dichroism

• NMR lines are broad

• Gel filtration indicates that they are not monomeric

Page 26: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

What is missing from the -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)?

Page 27: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Can we design a well-folded -sandwich if we start with a naturally occurring protein backbone?

Target Structure: Tenascin

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

Page 28: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Redesigned Tenascin is Well-Folded

1D-NMR of Redesigned Tenascin

Page 29: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Redesigned Tenascin is more stable than Wild-Type Tenascin

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

0 20 40 60 80 100

Temperature

Fra

ctio

n U

nfo

lded

WT Tenascin

RedesignedTenascin

Page 30: Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill.

Acknowledgements

Loop DesignJenny HuHengming Ke

Interface DesignAndrew Leaver-FayGlenn ButterfossRamesh Jha

-sheet DesignJenny Hu