1. ©CMBI 2003 MUTANT DESIGN BIO- INFORMATICS QUESTION ‘MOLECULAR BIOLOGY’ BIOPHYSICS G Vriend CMBI KUN Nijmegen Netherlands http://www.cmbi.kun.nl/gv/ “Vriend@CMBI.KUN.NL”

2. ©CMBI 2003 Abstract Protein folding, structure, stability Applied Process optimization MUTANT DESIGN BIO- INFORMATICS QUESTION ‘MOLECULAR BIOLOGY’ BIOPHYSICS

3. ©CMBI 2003 WARNING 1.I know nothing about actually MAKING mutants, I only talk about that…. 2.Most times evolutionary* approaches beat design approaches. 3.Mutants are not always the best way to answer questions: 4.Often protein chemistry, spectroscopy, or Google get you the answer more quickly. *’Evolutionary’ is grant-speak for trial-and-error by a bacterium…

4. ©CMBI 2003 THE W W W OF MUTATIONS Why make a mutation? Where to make the mutation? Which mutation to make?

5. ©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; The topics in red will be illustrated over the next ten minutes. Many mutations belong in more than one category…..

6. ©CMBI 2003 PROTEIN STRUCTURE helix strand turn Alanine 1.42 0.83 0.66 Arginine 0.98 0.93 0.95 Aspartic Acid 1.01 0.54 1.46 Asparagine 0.67 0.89 1.56 Cysteine 0.70 1.19 1.19 Glutamic Acid 1.39 1.17 0.74 Glutamine 1.11 1.10 0.98 Glycine 0.57 0.75 1.56 Histidine 1.00 0.87 0.95 Isoleucine 1.08 1.60 0.47 Leucine 1.41 1.30 0.59 Lysine 1.14 0.74 1.01 Methionine 1.45 1.05 0.60 Phenylalanine 1.13 1.38 0.60 Proline 0.57 0.55 1.52 Serine 0.77 0.75 1.43 Threonine 0.83 1.19 0.96 Tryptophan 1.08 1.37 0.96 Tyrosine 0.69 1.47 1.14 Valine 1.06 1.70 0.50 AbstractApplied Luis Serrano Marjolijn Roeters Jan van Hest

7. ©CMBI 2003 PROTEIN STABILITY Δ G = Δ H - T Δ S Δ G = -RT ln(K) K = [Folded] / [Unfolded] So, you can interfere either with the folded, or with the unfolded form. Choosing between Δ H and Δ S will be much more difficult, because Δ G is a property of the complete system, including H 2 O….

8. ©CMBI 2003 PROTEIN STABILITY Hydrophobic packing Helix capping Loop transplants

9. ©CMBI 2003 PROTEIN STABILITY A whole series of simple 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; Put AMELK in a helix; Make turns more turn-like; Ask Luis Serrano; etc. The main thing is to first find out WHY the protein is unstable. Abstract: F UApplied: F LU I

10. ©CMBI 2003 MUTATIONS SHOULD ‘ADD UP’ But normally they don’t:

11. ©CMBI 2003 ABSTRACT MODEL VALIDATION APPLIEDABSTRACT One weak spot Many weak spots Two weak spots

12. ©CMBI 2003 PROTEIN STABILITY SUMMARY Your best bets are: 1. Proline in surface loop 2. Helix capping 3. Gly -> Ala 4. Salt-bridges Just make sure your mutation is at the surface because you never get it right anyway, and water is very forgiving. Find out first why the protein is unstable

13. ©CMBI 2003 MUTANTS FOR MODEL VALIDATION -----ALEISYQVSTHAPLQVALKYIISAK---- -----ILQISYYVNSN--IQAFSQFILTAK---- -----ILQISYYVNS--NIQAFSQFILTAK---- -----ILQISYYVN--SNIQAFSQFILTAK---- Asp is good at position 1 of helix. Asn -> Asp mutation was stabilizing, from which it was concluded that helix 8 kept its length. 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.

14. ©CMBI 2003 TEMPERATURE SENSITIVE MUTANTS cause (e.g. temperature) effecteffect Make a series of mildly destabilizing mutants, e.g., Ile->Val; Tyr->Phe. They should be far away from the active site and ‘near’ the surface. Combine these mutants, and if something ‘does not work’ keep it anyway. Life uses no more than two logarithms, and one hydrogen bond can easily be two logarithms. Δ G=2kCal/Mole Δ T=(here) 7º This is an N=1 experiment (!) by Ariel Blocker. Wt Ts

15. ©CMBI 2003 PREDICT MUTATIONS FROM ALIGNMENTS Multiple sequence alignments are the most powerful tool in bioinformatics. And that is also true for mutation design; multiple sequence alignment analyses can help decide where to mutate. If nature has done it once, you can do it twice. Conserved residues are very important. Correlated mutations indicate function. Variability comes in gradations.

16. ©CMBI 2003 CORRELATED MUTATIONS 1 AGASDFDFGHKM Gray is conserved 2 AGASDFDFRRRL Black is variable 3 AGLPDFMNGHSI Red/green are 4 AGLPDFMNRRRV correlated mutations Correlated mutations guarantee a function.

17. ©CMBI 2003 20 E i =  p i ln(p i ) i=1 Entropy - variability 11 Red Main site 12 Orange Support 22 YellowCommunication 23 GreenModulator site 33 BlueThe rest Sequence variability is the number of residues that is present in more than 0.5% of all sequences. Entropy = Information Variability = Chaos With Laerte Oliveira

18. ©CMBI 2003 MUTANT ROTAMERS Predicting rotamers is very difficult. Position specific rotamers often can help. The rest of the protein does not move around when you make a mutant*. *Enzo di Filippis, and recall Anna Tramontano’s seminar

19. ©CMBI 2003 A short break for a word from our sponsors Laerte Oliveira Our industrial sponsors: FLORENCEFLORENCE HORNHORN Wilma KuipersWeesp Bob Bywater Copenhagen Nora vd WendenThe Hague Mike SingerNew Haven Laerte OliveiraSao Paulo Ad IJzermanLeiden Margot BeukersLeiden Fabien CampagneNew York Øyvind EdvardsenTroms Ø Simon FolkertsmaFrisia Henk-Jan JoostenWageningen Joost van DurmaBrussels David Lutje HulsikUtrecht Tim HulsenGoffert Manu BettlerLyon Elmar Krieger Simon Folkertsma David Tim AdjeMargot Fabien Manu (And from our friends!) and Unilever WHAT IF