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MUTANT DESIGN
BIOINFORMATICS
QUESTION
BIOPHYSICS
‘MOLECULAR
BIOLOGY’
©CMBI 2003
MUTANT DESIGN
Abstract
Protein folding,
structure, stability
BIOINFORMATICS
QUESTION
BIOPHYSICS
Applied
‘MOLECULAR
BIOLOGY’
Process
optimization
©CMBI 2003
MUTANT DESIGN
Three strong warnings and disclaimers:
1. I know nothing about MAKING mutants
2. Most times ‘evolutionary’ (that is grantwriting terminology for smart trial-anderror) beat design approaches.
3. Mutants are not always the best way to
answer questions. Often good oldfashioned protein chemistry, spectroscopy,
or even literature searches get you the
answer more quickly.
©CMBI 2003
WHY MUTATIONS
1.
Understand protein folding, structure, stability (against many
different things);
2. Atomic model validation (homology models, drug binding), or
abstract model validation (functional hypotheses);
3. Disrupting interactions, or make them permanent;
4. Protein activity is very hard to engineer;
5. Support for structure determination, e.g. Selenomethionine for
SAD or MAD, Cysteine for heavy-metal binding, solubility for
NMR; introduce fluorophore;
6. Humanization (normally more than just mutations);
7. Delete, or sometimes add post-translational modifications;
8. Purification tags, e.g. his-tag, flag-tag (not really mutations);
9. Temperature sensitive mutants;
10. Alanine or cysteine scan, or variants thereof;
11. ‘Mutate away’ metal binding sites;
Many mutations belong in more than one category…..
©CMBI 2003
PROTEIN STRUCTURE
F
OH
O
O
O
O
H
N
NH2
O
H
N
N
H
H
N
N
H
O
N
H
NH2
O
O
HO
O
NH
HN
NH2
HN
HN
H 2N
NH2
HN
OH
O
HO
O
strand
0.83
0.93
0.54
0.89
1.19
1.17
1.10
0.75
0.87
1.60
1.30
0.74
1.05
1.38
0.55
0.75
1.19
1.37
1.47
1.70
turn
0.66
0.95
1.46
1.56
1.19
0.74
0.98
1.56
0.95
0.47
0.59
1.01
0.60
0.60
1.52
1.43
0.96
0.96
1.14
0.50
Abstract
O
O
HN
H
N
N
H
O
N
H
OH
O
N
H
O
F
0.2
0.15
0.1
0.05
Ha(obs)-Ha(r.c.)
helix
Alanine
1.42
Arginine
0.98
Aspartic Acid 1.01
Asparagine
0.67
Cysteine
0.70
Glutamic Acid 1.39
Glutamine
1.11
Glycine
0.57
Histidine
1.00
Isoleucine
1.08
Leucine
1.41
Lysine
1.14
Methionine
1.45
Phenylalanine 1.13
Proline
0.57
Serine
0.77
Threonine
0.83
Tryptophan
1.08
Tyrosine
0.69
Valine
1.06
O
H
N
0
-0.05
MBH28A
-0.1
MBH28B
-0.15
-0.2
Applied
-0.25
-0.3
-0.35
Arg
Gly
Lys
Tyr
pFPhe
Thr
Asp
Asn
Gly
Ile
Thr
Tyr
pFPhe
Glu
Gly
Arg
residue
©CMBI 2003
PROTEIN STABILITY
ΔG = ΔH - TΔS
ΔG = -RT ln(K)
K = [Folded] / [Unfolded]
So, you can interfere either with the
folded, or with the unfolded form.
Choosing between ΔH and ΔS will be much
more difficult, because ΔG is a property of
the complete system, including H2O….
©CMBI 2003
PROTEIN STABILITY
Hydrophobic packing
Helix capping
Loop transplants
©CMBI 2003
PROTEIN STABILITY
A whole series of tricks can be applied:
Gly -> Any; Any -> Pro; Introduce hydrogen
bonds; Hydrophobic packing; Cys-Cys bridges;
Salt bridges; β-branched residues in β- strands;
Pestering water from the core; etc.
The main thing is that you should first know
WHY the protein is unstable.
Abstract: F
U
Applied: F
LU
I
©CMBI 2003
MUTATIONS ‘SHOULD’ ADD UP
©CMBI 2003
BUT THEY DON’T….
©CMBI 2003
LOCAL UNFOLDING
©CMBI 2003
WEAK SPOTS IN PROTEINS
©CMBI 2003
WEAK SPOT PROTECTION
©CMBI 2003
SUPPORT FOR EXPERIMENTS
1.
2.
3.
4.
5.
6.
7.
8.
Selenomethionine for Xray;
Solubility (i.e. for NMR);
Tags for purification (His-tag, Flag-tag, etc);
Addition or removal of post-translational
modification sites;
‘Mutate away’ metal binding sites;
Introduce fluorophore;
Block binding, or make binding irreversible;
Etcetera.
©CMBI 2003
PREDICT MUTATIONS FROM ALIGNMENTS
It is rapidly becoming apparent that multiple
sequence alignments are the most powerful
tool in bioinformatics.
And that is also true for mutation design.
If you can predict something that nature has
done already, success is almost guaranteed.
©CMBI 2003
CONSERVED, VARIABLE, OR IN-BETWEEN
QWERTYASDFGRGH
QWERTYASDTHRPM
QWERTNMKDFGRKC
QWERTNMKDTHRVW
Gray = conserved
Black = variable
Green = correlated mutations
©CMBI 2003
CORRELATED MUTATIONS SHAPE TREE
1
2
3
4
AGASDFDFGHKM
AGASDFDFRRRL
AGLPDFMNGHSI
AGLPDFMNRRRV
©CMBI 2003
CORRELATION = INFORMATION
1, 2 and 5 bind calcium; 3 and 4 don’t.
Which residues bind calcium?
1
2
3
4
5
ASDFNTDEKLRTTYI
ASDFSTDEKLKTTYI
LSFFTTDTKLATIYI
LSHFLTDLKLATIYI
ASDFTTDEKLALTYI
Ca+
Ca+
Ca+
©CMBI 2003
AND NOW, THE VARIABLE RESIDUES
Entropy at i:
20
Ei = S pi ln(pi)
i=1
Sequence variability is the
number of residues that is
present in more than 0.5%
of all sequences.
11 Red
12 Orange
22 Yellow
23 Green
33 Blue
Main site
Support
Communication
Modulator site
The rest
Entropy
= Information
Variability = Chaos
Orange -> purple
On this PC/beamer
©CMBI 2003
Entropy - variability
11 Red
12 Orange
22 Yellow
23 Green
33 Blue
Main site
Support
Communication
Modulator site
The rest
20
Entropy
= Information
Ei = S pi ln(pi) Variability = Chaos
i=1
Sequence variability is the number of
residues that is present in more than
0.5% of all sequences.
©CMBI 2003
Entropy - Variability – Function*
Diseases
Transcription
60%
20%
50%
15%
40%
30%
10%
20%
5%
10%
0%
0%
Box 11
Box 12
Box 22
Box 23
Box 33
Box 11
Coregulator
Box 12
Box 22
Box 23
Box 33
Box 23
Box 33
Box 23
Box 33
Dimerization
40%
40%
30%
30%
20%
20%
10%
10%
0%
0%
Box 11
Box 12
Box 22
Box 23
Box 33
Box 11
Box 12
Box 22
No mutations
Ligand binding
25%
30%
20%
20%
15%
10%
10%
5%
0%
0%
Box 11
Box 12
Box 22
Box 23
Box 33
Box 11
Box 12
Box 22
*This is for nuclear hormone receptors
©CMBI 2003
Acknowledgements
V.G.H.Eijsink, B.v.d.Burg,
G.Venema, B.Stulp, J.R.v.d.Zee,
H.J.C.Berendsen, B.Hazes,
B.W.Dijkstra, O.R.Veltman,
B.v.d.Vinne, F.Hardy, F.Frigerio,
W.Aukema, J.Mansfeld, R.UlbrichHofmann, A.d.Kreij.
©CMBI 2003
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