©CMBI 2003

MUTANT DESIGN

BIO-INFORMATICS

QUESTION

‘MOLECULARBIOLOGY’

BIOPHYSICS

©CMBI 2003

Abstract Protein folding,structure, stability

Applied Process optimization

MUTANT DESIGN

BIO-INFORMATICS

QUESTION

‘MOLECULARBIOLOGY’

BIOPHYSICS

©CMBI 2003

MUTANT DESIGN

Three strong warnings and disclaimers:

1. I know nothing about MAKING mutants2. Most times ‘evolutionary’ (that is grant-writing

terminology for smart trial-and-error) beat design approaches.

3. Mutants are not always the best way to answer questions. Often good old-fashioned protein chemistry, spectroscopy, or even literature searches get you the answer more quickly.

©CMBI 2003

WHY MUTATIONS1. 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

NH2

NH

HN

NH

NH

NH2HN

O

O

O

H2N

F

HN

NH

O

HO

O

OH

O

HN

O

HN

O

O

NH2

HN

O

NH

HN

NH

HN

NH

OHO

O

F

O

O OH

O

O

HN

HN NH2

O

OH

helix strand turnAlanine 1.42 0.83 0.66 Arginine 0.98 0.93 0.95Aspartic Acid 1.01 0.54 1.46Asparagine 0.67 0.89 1.56Cysteine 0.70 1.19 1.19Glutamic Acid 1.39 1.17 0.74 Glutamine 1.11 1.10 0.98Glycine 0.57 0.75 1.56Histidine 1.00 0.87 0.95Isoleucine 1.08 1.60 0.47Leucine 1.41 1.30 0.59Lysine 1.14 0.74 1.01Methionine 1.45 1.05 0.60Phenylalanine 1.13 1.38 0.60Proline 0.57 0.55 1.52Serine 0.77 0.75 1.43Threonine 0.83 1.19 0.96Tryptophan 1.08 1.37 0.96Tyrosine 0.69 1.47 1.14Valine 1.06 1.70 0.50

Abstract Applied

©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….

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LOCAL UNFOLDING

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WEAK SPOTS IN PROTEINS

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WEAK SPOT PROTECTION

©CMBI 2003

SUPPORT FOR EXPERIMENTS

1. Selenomethionine for Xray;2. Solubility (i.e. for NMR);3. Tags for purification (His-tag, Flag-tag, etc);4. Addition or removal of post-translational

modification sites;5. ‘Mutate away’ metal binding sites;6. Introduce fluorophore;7. Block binding, or make binding irreversible;8. 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

QWERTYASDFGRGHQWERTYASDTHRPMQWERTNMKDFGRKCQWERTNMKDTHRVWGray = conservedBlack = variableGreen = correlated mutations

©CMBI 2003

CORRELATED MUTATIONS SHAPE TREE

1 AGASDFDFGHKM2 AGASDFDFRRRL3 AGLPDFMNGHSI4 AGLPDFMNRRRV

©CMBI 2003

CORRELATION = INFORMATION

1, 2 and 5 bind calcium; 3 and 4 don’t. Which residues bind calcium?

1 ASDFNTDEKLRTTYI Ca+2 ASDFSTDEKLKTTYI Ca+3 LSFFTTDTKLATIYI4 LSHFLTDLKLATIYI5 ASDFTTDEKLALTYI Ca+

©CMBI 2003

AND NOW, THE VARIABLE RESIDUES

11 Red Main site12 Orange Support22 Yellow Communication23 Green Modulator site33 Blue The rest

20Entropy at i: Ei = pi ln(pi) i=1

Sequence variability is the number of residues that is present in more than 0.5% of all sequences.

Entropy = Information Variability = Chaos

Orange -> purpleOn this PC/beamer

©CMBI 2003

20Ei = pi ln(pi) i=1

Entropy - variability11 Red Main site12 Orange Support22 Yellow Communication23 Green Modulator site33 Blue The rest

Sequence variability is the number of residues that is present in more than 0.5% of all sequences.

Entropy = Information Variability = Chaos

©CMBI 2003

Diseases

0%

10%

20%

30%

40%

50%

60%

Box 11 Box 12 Box 22 Box 23 Box 33

Transcription

0%

5%

10%

15%

20%

Box 11 Box 12 Box 22 Box 23 Box 33

Coregulator

0%

10%

20%

30%

40%

Box 11 Box 12 Box 22 Box 23 Box 33

Dimerization

0%

10%

20%

30%

40%

Box 11 Box 12 Box 22 Box 23 Box 33

Ligand binding

0%

10%

20%

30%

Box 11 Box 12 Box 22 Box 23 Box 33

No mutations

0%

5%

10%

15%

20%

25%

Box 11 Box 12 Box 22 Box 23 Box 33

Entropy - Variability – Function*

*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.Ulbrich-Hofmann, A.d.Kreij.

©CMBI 2003

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