Directed Molecular Evolution of ProteinsEdited by S. Brakmann
and K. JohnssonDirected Molecular Evolution of Proteins: or How to
Improve Enzymes for Biocatalysis.Edited by Susanne Brakmann and Kai
JohnssonCopyright 2002 Wiley-VCH Verlag GmbH & Co. KGaAISBNs:
3-527-30423-1 (Hardback); 3-527-60064-7 (Electronic)Related Titles
from Wiley-VCHKellner,R.; Lottspeich, F.; Meyer, H.
E.Microcharacterization of Proteins1999ISBN
3-527-30084-8Bannwarth,W.; Felder, E.; Mannhold, R.; Kubinyi, H.;
Timmermann, H.Combinatorial Chemistry. A Practical Approach2000ISBN
3-527-30186-0Gualtieri,F.; Mannhold, R.; Kubinyi, H.; Timmermann,
H.New Trends in Synthetic Medicinal Chemistry2001ISBN
3-527-29799-5Clark,D. E.; Mannhold, R.; Kubinyi, H.; Timmermann,
H.Evolutionary Algorithms in Molecular Design2000ISBN
3-527-30155-0Directed Molecular Evolution of Proteinsor How to
Improve Enzymes for BiocatalysisEdited bySusanne Brakmann and Kai
JohnssonThe Editor of this volumeDr. Susanne BrakmannAG Angewandte
Molekulare EvolutionInstitut fur Spezielle ZoologieUniversitat
LeipzigTalstrae 3304103 Leipzig, GermanyProf. Dr. Kai
JohnssonInstitute of Molecularand Biological ChemistrySwiss Federal
Institute ofTechnology LausanneCH-1015 Lausanne, SwitzerlandCover
Illustration Recent advances in automationand robotics have greatly
facilitated the high throughput screening for proteins with
desiredfunctions. Among other devices liquid handlingtools are
integral parts of most screening robots.Depicted are 96-channel
pipettors for the microliter-and submicroliter range (illustrations
kindlyprovided by Cybio AG, Jena).This book was carefully produced.
Nevertheless,editors, authors and publisher do not warrant
theinformation contained therein to be free of errors.Readers are
advised to keep in mind that state-ments, data, illustrations,
procedural detailsor other items may inadvertently be
inaccurate.Library of Congress Card No.:applied forBritish Library
Cataloguing-in-Publication DataA catalogue record for this book is
available fromthe British Library.Die Deutsche Bibliothek CIP
Cataloguing-in-Pub-lication DataA catalogue record for this
publication is availablefrom Die Deutsche Bibliothek. Wiley-VCH
Verlag GmbH, Weinheim 2002All rights reserved (including those of
translationin other languages). No part of this book may
bereproduced in any form by photoprinting, mi-crofilm, or any other
means nor transmitted ortranslated into machine language without
writtenpermission from the publishers.In this publication, even
without specific indi-cation, use of registered names, trademarks,
etc.,and reference to patents or utility models does notimply that
such names or any such information areexempt from the relevant
protective laws and reg-ulations and, therefore, free for general
use, nordoes mention of suppliers or of particular com-mercial
products constitute endorsement orrecommendation for use.Printed on
acid-free paper.Printed in the Federal Republic of
Germany.Composition Mitterweger &
PartnerKommunikationsgesellschaft mbH, PlankstadtPrinting
betz-druck GmbH, DarmstadtBookbinding Grobuchbinderei J.
SchafferGmbH & Co. KG, GrunstadtISBN 3-527-30423-1ContentsList
of Contributors XI1 Introduction 12 Evolutionary Biotechnology From
Ideas and Conceptsto Experiments and Computer Simulations 52.1
Evolution in vivo From Natural Selection to Population Genetics
52.2 Evolution in vitro From Kinetic Equations to Magic Molecules
82.3 Evolution in silico From Neutral Networks to Multi-stable
Molecules 162.4 Sequence Structure Mappings of Proteins 252.5
Concluding Remarks 263 Using Evolutionary Strategies to Investigate
the Structureand Function of Chorismate Mutases 293.1 Introduction
293.2 Selection versus Screening 303.2.1 Classical solutions to the
sorting problem 313.2.2 Advantages and limitations of selection
323.3 Genetic Selection of Novel Chorismate Mutases 333.3.1 The
selection system 353.3.2 Mechanistic studies 373.3.2.1 Active site
residues 373.3.2.2 Random protein truncation 423.3.3 Structural
studies 443.3.3.1 Constraints on interhelical loops 443.3.4
Altering protein topology 463.3.4.1 New quaternary structures
473.3.4.2 Stable monomeric mutases 493.3.5 Augmenting weak enzyme
activity 513.3.6 Protein design 533.4 Summary and General
Perspectives 57Directed Molecular Evolution of Proteins: or How to
Improve Enzymes for Biocatalysis.Edited by Susanne Brakmann and Kai
JohnssonCopyright 2002 Wiley-VCH Verlag GmbH & Co. KGaAISBNs:
3-527-30423-1 (Hardback); 3-527-60064-7 (Electronic)4 Construction
of Environmental Libraries for Functional Screeningof Enzyme
Activity 634.1 Sample Collection and DNA Isolation from
Environmental Samples 654.2 Construction of Environmental Libraries
684.3 Screening of Environmental Libraries 714.4 Conclusions 765
Investigation of Phage Display for the Directed Evolution of
Enzymes 795.1 Introduction 795.2 The Phage Display 795.3 Phage
Display of Enzymes 815.3.1 The expression vectors 815.3.1.1
Filamentous bacteriophages 815.3.1.2 Other phages 835.3.2
Phage-enzymes 845.4 Creating Libraries of Mutants 875.5 Selection
of Phage-enzymes 895.5.1 Selection for binding 895.5.2 Selection
for catalytic activity 905.5.2.1 Selection with substrate or
product analogues 905.5.2.2 Selection with transition-state
analogues 925.5.2.3 Selection of reactive active site residues by
affinity labeling 965.5.2.4 Selection with suicide substrates
985.5.2.5 Selections based directly on substrate transformations
1025.6 Conclusions 1086 Directed Evolution of Binding Proteins by
Cell Surface Display: Analysisof the Screening Process 1116.1
Introduction 1116.2 Library Construction 1136.2.1 Mutagenesis
1136.2.2 Expression 1146.3 Mutant Isolation 1156.3.1 Differential
labeling 1156.3.2 Screening 1196.4 Summary 124Acknowledgments 1247
Yeast n-Hybrid Systems for Molecular Evolution 1277.1 Introduction
1277.2 Technical Considerations 1307.2.1 Yeast two-hybrid assay
1307.2.2 Alternative assays 1417.3 Applications 1477.3.1
Protein-protein interactions 1477.3.2 Protein-DNA interactions
149Contents VI7.3.3 Protein-RNA interactions 1507.3.4 Protein-small
molecule interactions 1537.4 Conclusion 1558 Advanced Screening
Strategies for Biocatalyst Discovery 1598.1 Introduction 1598.2
Semi-quantitative Screening in Agar-plate Formats 1618.3
Solution-based Screening in Microplate Formats 1648.4 Robotics and
Automation 1699 Engineering Protein Evolution 1779.1 Introduction
1779.2 Mechanisms of Protein Evolution in Nature 1789.2.1 Gene
duplication 1799.2.2 Tandem duplication 180ba-barrels 1819.2.3
Circular permutation 1829.2.4 Oligomerization 1839.2.5 Gene fusion
1849.2.6 Domain recruitment 1849.2.7 Exon shuffling 1869.3
Engineering Genes and Gene Fragments 1879.3.1 Protein fragmentation
1889.3.2 Rational swapping of secondary structure elements and
domains 1899.3.3 Combinatorial gene fragment shuffling 1909.3.4
Modular recombination and protein folding 1949.3.5 Rational domain
assembly engineering zinc fingers 1999.3.6 Combinatorial domain
recombination exon shuffling 2009.4 Gene Fusion From Bi- to
Multifunctional Enzymes 2039.4.1 End-to-end gene fusions 2039.4.2
Gene insertions 2039.4.3 Modular design in multifunctional enzymes
2049.5 Perspectives 20810 Exploring the Diversity of Heme Enzymes
through Directed Evolution 21510.1 Introduction 21510.2 Heme
Proteins 21610.3 Cytochromes P450 21810.3.1 Introduction 21810.3.1
Mechanism 22010.3.2.1 The catalytic cycle 22010.3.2.2 Uncoupling
22210.3.2.3 Peroxide shunt pathway 22210.4 Peroxidases 22310.4.1
Introduction 22310.4.2 Mechanism 223VII10.4.2.1 Compound I
formation 22310.4.2.2 Oxidative dehydrogenation 22610.4.2.3
Oxidative halogenation 22610.4.2.4 Peroxide disproportionation
22610.4.2.5 Oxygen transfer 22710.5 Comparison of P450s and
Peroxidases 22710.6 Chloroperoxidase 22810.7 Mutagenesis Studies
22910.7.1 P450s 23010.7.1.1 P450cam 23010.7.1.2 Eukaryotic P450s
23010.7.2 HRP 23110.7.3 CPO 23110.7.4 Myoglobin (Mb) 23210.8
Directed Evolution of Heme Enzymes 23310.8.1 P450s 23310.8.2
Peroxidases 23410.8.3 CPO 23610.8.4 Catalase I 23610.8.5 Myoglobin
23710.8.6 Methods for recombination of P450s 23710.9 Conclusions
23811 Directed Evolution as a Means to Create Enantioselective
Enzymes for Usein Organic Chemistry 24511.1 Introduction 24511.2
Mutagenesis Methods 24711.3 Overexpression of Genes and Secretion
of Enzymes 24811.4 High-Throughput Screening Systems for
Enantioselectivity 25011.5 Examples of Directed Evolution of
Enantioselective Enzymes 25711.5.1 Kinetic resolution of a chiral
ester catalyzed by mutant lipases 25711.5.2 Evolution of a lipase
for the stereoselective hydrolysis of ameso-compound 26811.5.3
Kinetic resolution of a chiral ester catalyzed by a mutant esterase
26911.5.4 Improving the enantioselectivity of a transaminase
27011.5.5 Inversion of the enantioselectivity of a hydantoinase
27011.5.6 Evolving aldolases which accept both D- and
L-glyceraldehydes 27111.6 Conclusions 27312 Applied Molecular
Evolution of Enzymes Involved in Synthesis and Repairof DNA 28112.1
Introduction 28112.2 Directed Evolution of Enzymes 28212.2.1
Site-directed mutagenesis 28312.2.2 Directed evolution 284Contents
VIII12.2.3 Genetic damage 28512.2.4 PCR mutagenesis 28612.2.5 DNA
shuffling 28712.2.6 Substitution by oligonucleotides containing
random mutations(random mutagenesis) 28812.3 Directed Evolution of
DNA polymerases 28912.3.1 Random mutagenesis of Thermus aquaticus
DNA Pol I 29112.3.1.1 Determination of structural components for
Taq DNA polymerasefidelity 29212.3.1.2 Directed evolution of a RNA
polymerase from Taq DNA polymerase 29312.3.1.3 Mutability of the
Taq polymerase active site 29412.3.2 Random oligonucleotide
mutagenesis of Escherichia coli Pol I 29412.4 Directed Evolution of
Thymidine Kinase 29512.5 Directed Evolution of Thymidylate Synthase
29712.6 O6-Alkylguanine-DNA Alkyltransferase 30012.7 Discussion
30213 Evolutionary Generation versus Rational Design of Restriction
Endonucleaseswith Novel Specificity 30913.1 Introduction 30913.1.1
Biology of restriction/modification systems 30913.1.2 Biochemical
properties of type II restriction endonucleases 31013.1.3
Applications for type II restriction endonucleases 31113.1.4
Setting the stage for protein engineering of type II
restrictionendonucleases 31313.2 Design of Restriction
Endonucleases with New Specificities 31313.2.1 Rational design
31313.2.1.1 Attempts to employ rational design to change the
specificityof restriction enzymes 31314.2.1.1 Changing the
substrate specificity of type IIs restriction enzymesby domain
fusion 31613.2.1.3 Rational design to extend specificities of type
II restriction enzymes 31613.2.2 Evolutionary design of extended
specificities 31813.3 Summary and Outlook 32414 Evolutionary
Generation of Enzymes with Novel Substrate Specificities 32914.1
Introduction 32914.2 General Considerations 33114.3 Examples
33314.3.1 Group 1 33314.3.2 Group 2 33714.3.3 Group 3 33814.4
Conclusions 339Index 343IXList of ContributorsProf. Dr. Frances H.
ArnoldChemical Engineering 21041California Institute of
Technology1201 East California BoulevardPasadena, California 91125,
USAProf. Dr. Stephen Benkovic, Dr. Stefan LutzDepartment of
ChemistryThe Pennsylvania State University414 Wartik
LaboratoryUniversity Park, Pennsylvania 16802, USAProf. Dr. Uwe
BornscheuerInstitut fur Chemie und
BiochemieErnst-Moritz-Arndt-UniversitatSoldmannstrae 1617487
GreifswaldProf. Dr. Virginia W. CornishColumbia
UniversityDepartment of Chemistry3000 Broadway, MC 3167New York, NY
10027-6948, USADr. Rolf DanielInstitut fur Mikrobiologie und
GenetikGeorg-August-UniversitatGrisebachstrae 837077 GottingenProf.
Dr. Jacques FastrezLaboratoire de Biochimie Physiqueet de Biopolyme
resUniversite Catholique de LouvainPlace L. Pasteur, 1.Bte 1BB-1348
Louvain-la-Neuve, BelgiumProf. Dr. Donald HilvertETH
HonggerbergLaboratorium fur Organische ChemieHCI, F339CH-8093
Zurich, SchweizProf. Dr. Lawrence A. LoebDepartment of
PathologySchool of MedicineUniversity of WashingtonBox
357705Seattle, Washington 981957705, USAProf. Dr. Alfred
PingoudInstitut fur
BiochemieJustus-Liebig-UniversitatHeinrich-Buff-Ring 5835392
GiessenProf. Dr. Manfred T. ReetzMax-Planck-Institut fur
KohlenforschungKaiser-Wilhelm-Platz 145740 MulheimProf. Dr. Peter
K. SchusterInstitut fur Theoretische Chemie und
StrahlenchemieUniversitat WienWahringerstrae 17A-1090 Wien,
OsterreichDr. Andreas SchwienhorstInstitut fur Mikrobiologie und
GenetikGeorg-August-UniversitatGrisebachstrae 837077 GottingenProf.
Dr. K. Dane WittrupDept. Chemical Engineering & Div.
Bioengineeringand Environmental HealthMassachusetts Institute of
TechnologyCambridge, MA 02139, USADirected Molecular Evolution of
Proteins: or How to Improve Enzymes for Biocatalysis.Edited by
Susanne Brakmann and Kai JohnssonCopyright 2002 Wiley-VCH Verlag
GmbH & Co. KGaAISBNs: 3-527-30423-1 (Hardback); 3-527-60064-7
(Electronic)Subject Indexaachilles' heel approach 314, 326acyclovir
298acyl-enzyme 99aequorin 170affinity labeling, selection 90, 9698
covalent 96 cystein 96 maturation 115agar plate assay 335 format,
screening 165aldol condensation 97aldolase 97, 275277, 340
D-2-keto-3-deoxygluconate (KDG) 276,340
2-keto-3-deoxy-6-phosphogluconate(KDPG) 275 pyruvate 276alkaline
phosphatase 86altering protein topology 4651amplification 29
biotechnology, evolution 14antibiotic catalytic, chorismate mutase
3335, 41,87 marker, yeast two-hybrid assay132 selection 31antibody
112 catalytic (see also chorismate mutase)3335, 41, 87, 249 phage
display (see there) 81aptamers 14Arg90, chorismate mutase
38AraC-LexA system 142AroH-class, chorismate mutase 33AroQ-class,
chorismate mutase 33, 57arthrobacter species 274assay achilles'
heel approach 314, 326 agar plate assay 335 automated analysis 324
protocol in microplate format 323 bacterial two-hybrid assay (see
there) 127,130143 biomedical applications 314 cleavage assay 326
colony assay 321 coupled transcription/translation 320 experimental
genetics 314 fluorescence assay 324, 326 growth assay 335
high-throughput approach 325 His6-tagged 323 HTS (high throughput
screening) 259,319 ee assays 259 molecular breeding 320 Ni2 -NTA
323 overlay assay 168 phage display 320 pipetting robot 324 protein
complementation assays(see there) 144 purification 323 restriction
fragment length poly-morphisms (RFLPs) 313 ribosome display 320
screening colorimetric assay 166 overlay assay 168 pH indicator
assay 274 shuffling 319ATP-binding polypeptides 57augmentation weak
enzyme activity 5152automation 172173auxotrophic marker 136Directed
Molecular Evolution of Proteins: or How to Improve Enzymes for
Biocatalysis.Edited by Susanne Brakmann and Kai JohnssonCopyright
2002 Wiley-VCH Verlag GmbH & Co. KGaAISBNs: 3-527-30423-1
(Hardback); 3-527-60064-7 (Electronic)avidity 82, 107azidothymidine
(AZT) 298bB2H (see bacterial two-hybrid) 142143BAC libraries
71bacillus subtilis chorismate mutase 33 lipase 272bacterial
display technique 25 two-hybrid assay (B2H) 142143 AraC-LexA system
142 bacterial activator system 142 k-repressor 142bacteriophage
7984 absorption coefficient 84 concentration 84 f1 81 fd 81 g3p
(see there) 8182 g8p (see there) 8182 M13 81 infection/infectivity
81, 84 lambda 83 morphogenesis 80 particle 81 phagemids 81
polyphage 84 replicative form 82 secretion 81 T4 83 western blot
84ba-barrel 185, 186, 193, 337 ba8-barrel 337BCNU
(1,3,-bis(2chloroethyl)1-nitrosourea) 302beta ba-barrel 185, 186,
193 ba8-barrel 337 b-galactosidase 86, 144, 338 b-lactamase 86, 89,
99, 107Bgl-I 312bias 288BIFL (burst integrated
fluorescencelifetime) 170173binary patterning 53biocatylysis 249
screening strategies, biocatalyst discovery(see also screening)
163164biological information 8bioluminescence 172biopanning
80821,3,-bis(2-chloroethyl)1-nitrosourea (BCNU)302blood coagulation
191breeding of molecules 16BsCM (B. subtilis, chorismate
mutase)3840, 4243 kinetic studies 43 redesign 39 truncation
4243b-type enzymes 221burkholderia cepacia 270burst integrated
fluorescence lifetime(BIFL) 170173ccalmodulin 103carbonic anhydrase
86carboxyl esterase (see also esterase) 333carboxy-terminus
82carotenogenic gene cluster 166catalase I 220, 230,
240241catalysis/catalytic antibodies 3335, 41, 87, 249 elution,
selection 107 fidelity 283 transition metal catalysis 249CCP
(cytochrome c peroxidase) 232, 239,339Cdc25 145cDNA display, phage
display 82 fos 82 jun 82cell surface display 3, 111124 expression
(see there) 114 library construction (see there) 113114 mutant
isolation (see there) 115124chaperonin GroEL 84 GroES 84chemical
inducers of dimerization (see protein-smallmolecule interactions)
153 tagging 1415chemoluminescence 172chimera 2chloromethylketones
98chloroperoxidase (see CPO) 220, 228,232233chorismate mutase 2959
AroH-class 33 AroQ-class 33, 57 B. subtilis (BsCM) 3840, 4243
kinetic studies 43Subject Index 344 redesign 39 truncation 4243
bacillus subtilis 33 biochemical characterization 56 catalytic
antibody 3335, 41, 87 1F7 33, 41 combinatorial mutagenesis 3840
randomization 56 computation studies 37 constraints on interhelial
loops44 E.coli, EcCM 3335, 41 electrostatic stabilization 3840
engineered hexamer, EcCM 48 monomer, EcCM 4849 310 helix 42
hydrophobic core 49 kinetic studies 39, 43 Kcat values 54 Km values
54 loop importance of loop length 48 insertion, EcCM 47 insertion,
MjCM 49 preferences, EcCM 44 mechanism 37 mechanistic studies 37
18O isotype effect at O 38 origins of thermostability, EcCM 49
phenotype - genotype, linkage 36 reaction catalized 3338 isotype
effects 38 redesign, BsCM 39 role of Arg90 38 saccharomyces
cerevisae 33, 41 selection system 35 chorismate mutase-deficient
bacterialstrain 54 KA12 34 liquid 49 pKIMP-UAUC 35 size-exclusion
chromatography 51 structural studies 44 transition state inhibitor
37 uncatalyzed reaction 37chromatography, size-exclusion,
chorismatemutase 51circular permutation 182, 186 ba-barrel 186
evolutionary advantages 186CLERY (combinatorial libraries
enhancedby recombination in yeast) 242cloning of DNA fragments 31
of functional genes from natural microbialconsortia 65
contermination of the purified DNA 65 extraction of DNA from soils
65CMCM (combinatorial multiple cassettemutagenesis) 267coefficient
of variance (CV) 121combinatorial syntheses of chemical
compoundlibraries 29 techniques 1complementation 293 heterologous
72compound I 224, 227, 235, 239, 241coumermycin 153coupling
efficiency of P450s (uncoupling)226, 234covalent 96, 99
intermediate 99 selection 96CPO (chloroperoxidase) 220, 228, 232233
directed evolution of 240 instability to peroxide 233 mutagenesis
of 235 reactions catalyzed 228 stability to H2O2 233Cre-Lox
83cutoff fluorescence selection 119120 purity 120 yield 120CV
(coefficient of variance) 121cyclin-dependent kinase 2
147cytochrome b-562 44 c 220 peroxidase (CCP) 232, 239, 339 P450
monooxygenase 222227,231235, 237238, 241243, 338339 catalytic cycle
225 comparison to peroxidase 231232 coupling efficiency 226
directed evolution of 237238 Km for H2O2 231232 mutagenesis of
233235 P450 1A1 241242 P450 1A2 241242 P450 2B4 235 P450 BM3
237238, 241Subject Index 345 P450cam 222, 226227, 233234,237, 339
P450 2E1 235 P450SPa 232 reactive intermediated 224 reactions
catalyzed 222, 224 recombination of 241242dD-2keto-3-deoxygluconate
(KDG) 276, 340Darwinian evolution 8, 51, 57DBD (see DNA binding
domain) 127,130134DD-peptidase 86de novo design protein design 53
rational design 27 tailored enzymes 57degenerate oligonucleotide 87
DNA syntheses, oligonucleotide-directed29degree of neutrality
17dehydrogenase 35 activity 72 oxidative dehydrogenation 230
prephenate 35dexamethasone dexamethasone-FK506 153
dexamethasone-methotrexate CID 153dGTP, 8-oxo-dGTP 288differential
labeling 115118digital imaging 167directed evolution (see also
evolution) 5659,249278, 327 back-crossing 266 cassette mutagenesis
252 colony picker 262 DNA shuffling 252 enzyme variants 250 hot
regions 267 hot spots 265 mutant gene 250 phage display 261 protein
sequence space 262 random mutagenesis (see there) 250, 290
saturation mutagenesis 252 selection methods 251 site-directed
mutagenesis 250 specificity 316 in vivo selection
261display/display techniques 2, 25 bacterial 25 cell surface
display 3, 111124 mRNA display 2 phage display (see there) 1, 25,
79108 ribosomal 2, 25 yeast display system 112dithiothreitol (DTT)
101diversity 1DNA achilles' heel 314 applied molecular evolution of
enzymesinvolved in synthesis and repair of DNA283307 artificial
chromosomes 314 binding domain 127 and transcription activation
domains,yeast two-hybrid assay 130134 binding protein 314 cDNA (see
there) 82 cleavage domain 318 cloning of DNA fragments 31
degenerate oligonucleotide-directedDNA syntheses 29 environmental
(see there) 6465, 165, 168 extraction 66 gene replacement 314
targeting 314 isolation of soil and sediment samples 67
manipulation 326 methylation 314 methylphosphonate 317 non-specific
DNA 315 O6-alkylguanine-DNA alkyltransferase283, 302304
PNA-assisted rare cleavage 314 polymerase 106, 284, 291293, 295
crystal structure 292 directed evolution of RNA polymerasefrom Taq
DNA polymerase 295 E. coli 284, 296297 exonucleolytic proofreading
292 fidelity 291 low fidelity mutants 295 mismatch repair 292 motiv
A 292 motiv B 292 motiv C 293 mutator 295 replication 291
temperature-sensitive mutant 293 thermus aquaticus 284 DNA Pol 1
293295 mutability 296 plasticity 296Subject Index 346 protein-DNA
interactions (see protein)149150 rat DNA Pol-b 293 recognition
domain 318 repair and replication 283 shuffling 2, 51, 87, 114,
194196, 252,289290, 338339 chimeric genes 289 directed evolution
252 exon shuffling 205 family DNA shuffling 195 limitation 195
mutagenesis methods 252 of mutant DNA fragments 29 triple-helix 314
formation 314domain 3D-domain swapping 187 recruitment 182, 188190
additional domains 189 chemistry 190 deleterious modification 189
domain swapping 187 regulation 189 substrate specificity 189dPTP
288DTT (dithiothreitol) 101eEcoRI 312EcoRV 312, 315enantioselective
enzymes 249278enantioselectivity (E) 98, 335, 337 Quick E 337
selectivity factor E 261enantioselectivity of enzymes
3endonuclease, restriction (see there) 311327engineering/engineered
chorismate mutase 4849 hexamer, EcCM 48 monomer, EcCM 4849 genes
and gene fragments 191206 protein folding (see there) 198203
protein fragmentation (see there)192193 in silico protein
engineering 212 protein evolution 181212 rational protein
engineering 193194 reverse engineering 185, 192environmental
libraries (see also libraries)2, 6376 collection 65 sampling sites
65 storage 65 transport 65 construction of 6871 activity-based
screening 68 BAC libraries 71 cosmid library 69 hosts 68 insert
size 68 plasmid library 69 protocols 69 vector selection 68 DNA
(see also there) 6465 environmental DNA libraries 165, 168
isolation 65 follow-up analysis 75 screening of 7175 activity-based
strategies 71 sequence-based approaches 74enzyme 249 applied
molecular evolution of enzymesinvolved in synthesis and repair of
DNA283307 b-type enzymes 221 enantioselective 249278
enantioselectivity of enzymes 3 enzyme-catalyzed pericyclic
reactions 33 ITCHY (incremental truncation for thecreation of
hybrid enzymes) 196, 199 oligomerization, role in enzyme
function187 proximal ligand of heme enzymes 220,227228, 231, 235238
screening 164 secretion of 252 type II restriction enzymes 312,
315, 318epicurian coli 335336 XL1-Red 273epoxide hydrolase 254epPCR
(see error-prone)equilibrium screening 111, 115 ligand
concentration 115117error model 12 rate of replication 12 threshold
12, 14 critical replication accuracy 14 phase transition
14error-prone PCR 1, 87, 113, 251, 338Escherichia coli (E. coli)
3335, 41 chorismate mutase (EcCM) 3335, 41 loop insertion 47 loop
preferences 44 DNA polymerase I 296297Subject Index 347esterase
273, 333 carboxyl esterase 333 lipase - esterase activity
72evolution/evolutionary process amino acid exchange 317 ancestral
proteins 312 applied molecular evolution of enzymesinvolved in
synthesis and repair of DNA283307 biotechnology 527 amplification
14 diversification 14 selection 14 constants 17 chorismate mutases
(see there) 2959 Darwinian 8, 51, 57 design 6, 327 directed (see
there) 5659, 249278, 327 efficiency 17 evolutionary pressure 317
gene shuffling 327 heme enzymes, directed evolution237242
homologous recombination 318 neutral 7, 16 optimization,
evolutionary 165 population 17 protein biosynthesis 317 punctated
24 selective advantage 314 in silico 16 size 17 strategies 29
success 17 techniques 1 in test tube 8 in vitro 323exon shuffling
(in nature) 190191,204205 DNA shuffling 205 lox-Cre recombination
204 mechanism 190 significance 190 trans-splicing group II intron
ribozymes 204 inteins 205exploitation of natural products
71expression 114 eucaryotic secretory pathway 114 non-eucaryotic
114 posttranslational events 114 proteolysis 114 solubility 114
stability maturation 114 surface expression level 114ff1 phagemide,
bacteriophages 81FACS-based screening 164FCS (fluorescence
correlation spectroscopy)171174fd phagemide, bacteriophages 815FdUR
301fibrinolysis 191FIDA (fluorescence intensity
distributionanalysis) 171174fidelity of catalysis 283
conformational changes 294 DNA polymerases 291 dNTP binding step
294fitness 56 differential 5 mean 6fitness, landscape 285FK506
153FKBP12-repamycin-associated protein 154FKBP12-repamycin-binding
domain 154FkpA 85flow cytometry 120 reactor 10fluorescamine
170172fluorescent/fluorescence assay 324, 326 burst integrated
fluorescence lifetime(BIFL) 170173 correlation spectroscopy (FCS)
171174 cutoff fluorescence selection (see there)119120 fluorescein
fluorescence 122123 green fluorescent protein (GFP) 166 intensity
distribution analysis (FIDA)171174 labeling 115 intensity 115
two-color fluorescent labeling 124 polarization 171 resonance
energy transfer (FRET) 129,171 screening, fluorescence-based
1715fluorouracil (5-FU) 299Fok-I 312, 318follow-up analysis,
environmental libraries75fos 82Subject Index 348mid-Fourier
transform infrared spectroscopy(infrared spectroscopy)
173fractionation method 66FRET (fluorescence resonance energy
trans-fer) 129, 171fusion protein 82 proteolytic degradation 85
western blot 84gg3p infection, bacteriophages 8182 signal sequence
82g8p infection, bacteriophages 8182 signal sequence
82galactosidase 144, 334 b-galactosidase 86, 144, 338 protein
complementation assays 144ganciclovir 298gene carotenogenic gene
cluster 166 combinatorial gene fragment shuffling194 duplication
183184 ba-barrels 185 isolated copies 183 mechansims 183 outcomes
183 proteases 184185 tandem 183 fusion 182, 188 aromatic amino acid
pathway 188 concerted expression 188 end-to-end 207 in-frame 207
reporter gene 207 solubility 207 transporter proteins 207 insertion
207209 allosteric regulation 208 biosensor 208 regulatory function
188 substrate channeling 188 overexpression of genes 252
recruitment 184 sharing 184 shuffling 6genetic algorithms 277
damage 287288 alkylation 288 chemicals 288 deamination 288
frameshifts 288 intercalation 288 transitions 288 transversions 288
X-rays 288 population (see there) 5 selection 30genotypephenotype
mapping 10, 17geotrichum candidum 270GFP (green fluorescent
protein) 166Gibbs reagent 237glutathione transferase 103
S-transferase 86glycinamide ribonucleotide transformylase(PurN)
201glycosidase 99growth assay 335GTPase 86guaiacol 339guanyl
nuclotide exchange factor 145hhalo formation screening 167Hamming
distance, sequence space 8hapten 94helix/helial, 310 helix,
chorismate mutase 42 constraints on interhelical loops 44
interhelical turn sequences 51 selection advantage 51heme enzymes
3, 219242 chloroperoxidase (see there) 232233 comparison of P450s
and peroxidases231232 cytochrome P450 (see there) 222227 directed
evolution (see evolution)237242 heme proteins 220221 mutagenesis
studies 233236 peroxidases (see there) 227231 proximal ligand of
220, 227228, 231,235238hemoglobin (Hb) 228hepatitis delta virus RNA
21herpes simplex virus type 1 (HSV-1) 297heterologous
complementation 72high throughput instrumentation 115 screening
(see HTS) 32, 58, 73, 165,250, 254262, 274HIS3 reporter 143HIS6RPro
Mnt variant 149HIV protease 149 reverse transcriptase 293Subject
Index 349homogenous time resolved fluorescence171173horseradish
peroxidase (HRP) 167, 227, 230,232, 235, 239240HTS (high throughput
screening) 32, 58, 73,165, 250, 254262, 274 automation 259
capillary array electrophoresis 254 chemical sensors 261 circular
dichroism 259 desymmetrization 256 ee assays 259 electrospray
ionization mass spectro-metry 254 fluorescence 259 gas
chromatography 254 IR-thermography 254 laser-induced fluorescence
detection(LIF) 257 microchips 256 microtiter plates 257 pH
indicator assay 274 pseudo-eantiomers 254 pseudo-prochiral
compounds 254 reaction microarrays 260 robotics 259hybrid bacterial
two-hybrid (B2H) 143 ITCHY (incremental truncation for thecreation
of hybrid enzymes) 196, 199 split-hybrid system 137 yeast n-hybrid
systems for molecularevolution 127158 yeast two-hybrid assay (see
there) 127,130141hydantoinase 274hypercube, sequence space 8iIGPS
(indole-3glycerol phosphatesynthase) 337improving enzyme activity
5152in silico evolution 16 protein engineering 212indigo
338indirubin 338indole-3-glycerol phosphate synthase
(IGPS)337infrared spectroscopy (mid-Fourier transforminfrared
spectroscopy) 173instrumentation, high-throughout 115intersectuib
theorem 19iron response protein-iron response elementinteraction
152ITCHY (incremental truncation for thecreation of hybryd enzymes)
196, 199jJun- 82kKA12 (see also chorismate mutase) 35Kcat values,
chorismate mutase 54Km values chorismate mutase 54 for H2O2,
cytochrome P450s 231KDG (D-2keto-3-deoxygluconate)276, 340kinetic
equations 10 fitness weighting term 10 parallel reactions 10
production terms 10 folding 21 algorithm 21 elementary steps 21
resolution 249 screening 115117 competition 117 label intensity 117
unlabeled ligand competior 117llabeling differential 115118
fluorescent 115b-lactamase 86, 89, 99, 107k-bacteriophage 83library
1 of altered genes 288 BAC libraries 71 CLERY (combinatorial
libraries enhancedby recombination in yeast) 242 combinatorial
2930, 87 combinatorial syntheses of chemicalcompound libraries 29
construction 113114 cosmid 69 creation 88 diversity 88, 11
environmental (see there) 2, 6376 plasmid 69 quality 88 screen
random libraries of RNAmolecules 152Subject Index 350 selection
from large combinatoriallibraries 33 transformation 88lipase 3,
253, 333 ab hydrolase fold 270 bacillus subtilis lipase 272 lipase
- esterase activity 72 conformational flexibility 270 oxyanion hole
271liquid handling 173175low copy plasmids 52luciferase
172luminescence 172 bioluminescence 172 chemoluminescence 172lysis,
direct lysis of cells 66 method 66lysozyme 86mM13 phagemide,
bacteriophages 81maleimide 106marker antibiotic 132 auxotrophic 136
counter-selective 132master sequence 916Mb (myoglobin) 221, 228,
236238, 241 directed evolution of 241 mutagenesis of 236
peroxygenase activity 236metagenome 63L-methionine 274methotrexate
homodimer 153Michael addition 94microbial culture collection 164
diversity 6366 cloning of functional genes fromnatrural microbial
consortia (see there)65 recovery or fractionation ob microbialcells
66microplate (microtiter plate) 168, 175misincorporations 288MNNG
(N-methyl-N'-nitro-N-nitro-guanidine) 302molecular evolution 1
biopolymers 5 concepts 5 origins 5 phylogeny 6monovalent display
83mRNA display 2MS2 coat protein-stem-loop RNA interac-tion
152multi-stable molecules 16mutagenesis 113114, 250251, 285
chorismate mutase, combinatorialmutagenesis 3840 combinatorial
multiple cassette muta-genesis (CMCM) 267 CPO (chloroperoxidase)
235 cytochrome P450 monooxygenase233235 directed evolution 320 DNA
shuffling 252, 285 gene shuffling 327 heme enzymes, mutagenesis
studies233236 methods 251 cassette mutagenesis 252 error-prone
polymerase chain reaction(epPCR) 251, 327, 338 saturatiion
mutagenesis 252 Mb (myoglobin) 236 molecular breeding 320
nucleoside analogues 327 optimal mutagenesis rate 113 peroxidase
235 polymerase chain reaction (see PCR) 285,288289 protein folding
202 random (see there) 194, 250, 285, 290291 saturation (see there)
1, 113, 252, 337 shuffling 320 site-directed 57, 250, 285
specificity 316 spiked oligodeoxynucleotide 323 in vitro evolution
323mutant cloud 11 isolation 115124 differential labeling 115118
screening 119124mutation 6, 29, 290 adaptive 6 degenerative
oligonucleotides 290 rates 6 selectively neutral 6 within limited
regions 290mutator strain 335myoglobin (see Mb) 221, 228, 236238,
241Subject Index 351nNa+/H+antiporter 72NADH 222, 226NAD(P)H 222,
226nanoplate (silicon wafer) 175177natural evolution 263
exploitation of natural products 71 selection 5networks, neutral
16, 1821 connected/connectedness 19, 21 extended 21neural networks
277neutral degree of neutrality 17 evolution 7, 16 networks (see
there) 16, 1821 selectively neutral mutation 6n-hybrid, yeast
n-hybrid systems for mole-cular evolution
127158N-methyl-N'-nitro-N-nitroguanidine(MNNG) 302non-ribosomal
peptide synthetases(see NRPSs) 208209, 211normalization 122novel
substrate specificities 331340NRPSs (non-ribosomal peptide
synthetases)208209, 211 combinatorial approaches 211 domain
exchange 209 module exchange 209 rational engineering 209,
211nuclease 86nucleotide analogs 288290o18O isotype effect at O
38Ob-replicase 8O4-methylated thymine (O4mt) 304O6-alkylguanine-DNA
alkyltransferase 283,302304O6-benzylguanine (BG) 303oligomerization
182, 187 role in enzyme function 187 substrate shielding
187oligonucleotide, degenerated 87 DNA syntheses, degenerate
oligonucleo-tide-directed 29overlay assay 168oxidative
dehydrogenation 230 halogenation 2308oxo-dGTP 288ppanning procedure
2PCR (polymerase chain reaction) 1, 87, 113,251, 288289 error-prone
(epPCR) 1, 87, 113, 251, 338 mutagenesis methods 251 mutagenesis
288289 error rate 288 manganese 288 mutation frequency 288 Taq
polymerase 288 sexual PCR 289PDZ domain with new specifities
149penicillin acylase 86 penicillin G 167, 170peptides that bound
target proteins 147percolation phenomenon 19peroxidase 220224,
227231, 235,238240 chloroperoxidase (see there) 220, 232233
comparison to cytochrome P450s231232 cytochrome c peroxidase (CCP)
232, 239,339 directed evolution of 238240 disproportionation 230
horseradish (HRP) 167, 227, 230, 232, 235,239240 mechanism 227231
mutagenesis of 235 reactions catalyzed 227228peroxide
disproportionation 230 shunt pathway (see also peroxygenase)226227,
231232, 237238peroxygenase 227, 231233, 236 activity, myoglobin
(Mb) 236pH indicator 166 assay 274, 335 HTS 274phage display 1, 25,
79108 antibodies 81 Fab 81 Fv 81 bacterial Sec system 82 cDNA
display 82 of enzymes 8187 monovalent display 83 phage shock
promotor 83 polyvalent display 82 technique 25phage-enzymes,
selection 89107 affinity 89Subject Index 352 binding 89 biopanning
89phenotype 31 mapping 10 space 17phospholipase 86, 339phospholipid
334phosphonate, transition-state analogues 94phosphonylating agents
102phosphoribosylanthranilate isomerase(PRAI) 337pKIMP-UAUC (see
also chorismate mutase)35PKSs (polyketide synthetases) 208210
combinatorial engineering 210 domain exchange 209 iterative (type
II) PKSs 210 minimal PKSs 210 module exchange 209 rational
engineering 209plasminogen 86, 98polyketide synthetases (see PKSs)
208210polymerase 86, 106 chain reaction (see PCR) 1, 87, 113,251,
288289 DNA polymerase (see there) 106, 284,291293population
genetics 5 differential equations 5 recombination 5PRAI
(phosphoribosylanthranilate isomer-ase) 337prephenate dehydratase
35 dehydrogenase 35probability 119 poisson random number 119
statistical confidence 119proFARI 337protease 86protein ancestral
312 complementation assays 144 adenylate cyclase 144
b-galactosidase 144 dihydrofolate reductase 144 ubiquitin 144
degradation, ubiquitin-mediated 145 engineering 165, 193194
evolution in nature, mechanisms of182191 blood coagulation 191
circular permutation (see there) 182, 186 domain recruitment (see
there) 182,188190 exon shuffling in nature (see there)190191
fibrinolysis 191 gene duplication (see there) 183184 gene fusion
(see there) 182, 188 modular protein evolution 191 oligomerization
(see there) 182, 187 tandem duplication 184186 folding 198203 in
the context of protein engineering198 folding pathway 202 modular
engineering 200 mutagenesis 202 problem of incorrect folding 200
fragment complementation 192 fragmentation 192 ba-barrel 193
permissive sites 192 green fluorescent protein (GFP) 144, 166
homogenous time resolved fluorescence171173 in silico protein
engineering 212 localization, to detect protein-protein
in-teractions 145 Cdc25 145 guanyl nucleotide exchange factor 145
Ras nucleotide exchange factors 145 protein-DNA interactions 149150
contrast to phage display 150 HIS6RPro Mnt variant 149 zinc finger
variants 150 protein-protein interactions 147149 HIV protease 149
identify peptides that bound targetproteins 147 inhibit
cyclin-dependent kinase 2 147 PDZ domain with new specifities 149
for the retinoblastoma protein 147 protein-RNA interactions 150153
iron response protein iron responseelement interaction 152 MS2 coat
protein-stem-loop RNAinteraction 152 screen random libraries of
RNAmolecules 152 protein-small molecule interactions153155
coumermycin 153 dexamethasone-FK506 153 dexamethasone-methotrexate
CID 153Subject Index 353 FKBP12repamycin-binding domain154
FKBP12-repamycin-associated protein154 FK506 153 methotrexate
homodimer 153 Tat-TAR interaction 152 sequence space 2526, 53 holes
26 occurence of function 26 simple lattice models 26 SHIPREC
(sequence-homology indepen-dent protein recombination) 196,241242
topology, altering 4651proteolytic degradation of fusion protein
85protoporphyrin IX 220221proximal ligand of heme enzymes
220,227228, 231, 235238pseudomonas (p.) p. aeruginosa 252 p.
fluorescence 273PurN (glycinamide ribonucleotide trans-formylase)
201push-pull mechanism 228229, 239 of O-O bound cleavage
239putidaredoxin 222qquasi-species 916 mutant distribution 12
stable stationary solution 12Quick E 337rrandom mutagenesis 194,
250, 290291 genetic damage 285 oligonucleotide 284285, 290 promotor
sequences 291 protein truncation 4243 replication 13 walk,
diffusion 7Ras nucleotide exchange factors 145rat DNA Pol-b
293reactive immunization, selection 97REBASE, Database
313recombination 1, 241242 CLERY (combinatorial libraries
enhancedby recombination in yeast) 242 cytochrome P450s 241242
lox-Cre recombination 204 population genetics 5 sexual 29, 51
SHIPREC (sequence-homology indepen-dent protein recombination)
196,241242reductase 86, 144, 222reeingineering quaternary
structures 47regioselectivity 94relay series 22repamycin,
FKBP12repamycin-bindingdomain 154replica plating 74repressed
transactivator system 137resorufin esters 336restriction
endonucleases with novel specifi-city (see also RM systems) 311327
8 base pair cutters 313 Bgl-I 312 chimeric nuclease 318 crystal
structures 312 DNA cleavage domain 318 DNA recognition domain 318
EcoRI 312 EcoRV 312, 315 Fok-I 312, 318 gene technology 313
PNA-assisted rare cleavage 314 rare cutters 314 recognition site
314 restriction fragment length poly-morphisms (RFLPs) 313 type II
restriction endonuclease 312, 326 enzymes 312, 315,
318retinoblastoma protein 147retrovirus mediated gene transfer
302reverse engineering 185 Y2H system 137RFLPs (restriction
fragment length poly-morphisms) 313rhizomucor miehei
270ribonuclease A 86ribonucleotide transformylase,
glycinamide(PurN) 201ribosome/ribosomal display 2, 25 inefficient
ribosome binding sites 52ribozymes 14, 278RM systems (see also
restriction endo-nucleases) 311327 methyltransferase activity 311
restriction endonuclease activity 311 selfish genetic elements
312Subject Index 354RNA folding 17 hepatitis delta virus RNA 21
information carrier 16 magic molecule 16 minimal free energy 17
mRNA (see there) 2 protein-RNA interactions (see protein)150153
rRNA (see there) 64 secondary structures 17 world 16robotics 173174
screening, robotic 120Rop 4416S rRNA 64ssaccharomyces cerevisae,
chorismate mu-tase 33, 41saturation mutagenesis 1, 113, 252, 337
hotspot 113scaffoldings 290SCRATCHY 198199 homology-independent
fragment shuf-fling 198screening 2, 3031, 58, 111124, 163176 agar
plate format 165 colorimetric assay 166 of environmental libraries
(see there)7175 enzyme/enzyme activity 6376, 164 equilibria (see
there) 111, 115 facilitated 31 FACS-based 164 fluorescence-based
171 halo formation 167 high throughput (see HTS) 32, 58, 73,
165,250, 254262, 274 kinetic (see there) 111, 115117 overlay-assay
168 oversampling 119 robotic 120 procedure 2 screen random
libraries of RNA mole-cules 152 solution-based 167 statistic 111
strategies for biocatalyst discovery163164 visual signal
166scytalone dehydratase 166Sec system, bacterial 82selection 2,
2930, 165167 advantages 3233, 46 in vivo selection schemes 58
affinity 81, 8990, 93 labeling 90, 9698 enantioselectivity of
enzymes 3 antibiotic 31 binding 89 biopanning 89 biotechnology 14
catalytic activity 90107 elution 107 covalent 96, 99 cutoff
fluorescence selection (see there)119120 cystein 96
enantioselectivity 98 genetic 30 from large combinatorial libraries
33 limitations 3233 mutation, selectively neutral 6 natural 5 of
phage-enzymes (see there) 89107 product analogues 90 reactive
immunization 97 regioselectivity 94 substrate 90, 102 turnover 102
with suicide substrates 98102 sulfonamide 93 transition-state
analogues (see there)9295 unanticipated or undesired solutions 33
vector 68selectivity factor E 261SELEX 1, 14sequence sequence -
structure relations 17 space 2, 53, 284286 Hamming distance 8
hypercube 8 protein sequence space (see there)2526, 53 structure
mappings of proteins 2526sexual recombination 29, 51shape space
covering 18shikimate pathway 33, 34SHIPREC (sequence-homology
independentprotein recombination) 196, 241242shuffling
combinatorial gene fragment shuffling194Subject Index 355 DNA (see
there) 2, 51, 87, 114, 194196,252, 289290, 338339 family shuffling
289 of mutant DNA fragments 29 mutagenesis 327 SCRATCHY,
homology-independentfragment shuffling 198199r-complex 94silicon
wafer (nanoplate) 175177single pass 121Skp 85small molecule-protein
interactions (see pro-tein) 153155SNase (staphylococcal
ribonuclease) 102solution-based screening 167specificity 189,
315320 artificial substrates 326 canonical site 319 chimeric
nuclease 318, 326 direct readout 316 directed evolution 316 domain
recruitment, substrate specificity189 extended 320321 flanking
sequence effect 319 indirect readout 316 intra- and intermodular
interactions 326 modified substrates 317 novel substrate
specificities (see there)331340 rational design 315 relaxed 317
site directed mutagenesis 316 transition state analogs 315, 320,
327 water-mediated contact 319 zinc fingers 318split-hybrid system
137staggered extension process (StEP) 339staphylococcal
ribonuclease (SNase) 102StEP (staggered extension process)
339stereoselective synthesis 249steric hindrance 333stop codon
85structure common 18 sequence structure mappings of proteins2526
sequence - structure relations 17subriligase protease 86substrate
analogues 9093subtiligases 104subtilisin 86, 102103 subtilisin E
168suicide substrates, selection with 98102sulfonamide,
transition-state analogues9295superoxide dismutase 87tT4
bacteriophage 83Tat-TAR interaction 152tertiary structure
290thermostability, origins of EcCM, chorismatemutase 49thermus
aquaticus 284 DNA Pol 1 293295 high fidelity mutants 295thioredoxin
87thymidine kinase 297299 herpes simplex virus type 1 (HSV-1)
297thymidylate synthase 283, 299302 methylation of dUMP 299thymitaq
301thymydine kinase 283transactivator system, repressed
137transaminase 274transformation of cells 31 efficiencies 32
microorganism 58transition 2223 major or discontinous 23 metal
catalysis 249 minor or continuous 22transition-state analogues,
selection 9295 phosphonate 94 sulfonamide 93trans-splicing inteins
205 exon shuffling 205trypsin 86uubiquitin-mediated protein
degradation 145uncoupling (coupling efficiency of P450s)226,
234urate oxidase 87vvisual signal screening 166wweak augmentation
weak enzyme activity5152 promotor 52Subject Index 356yY2H (see
yeast, two-hybrid assay) 127,130141yeast CLERY (combinatorial
libraries enhancedby recombination in yeast) 242 display system 112
n-hybrid systems for molecular evolution127158 two-hybrid assay
(Y2H) 127, 130141 activation domain 131 antibiotic marker 132
auxotrophic marker 136 bacterial two-hybrid assay (see there) 142
counter-selective markers 132 DNA-binding and transcription
activa-tion domains 130134 false negatives 140141 false positives
139140 negative selection 137 promoter 136 reporters 134137 reverse
Y2H system 137 variations 137zzinc finger 203204 specificity 318
variants, protein-DNA interaction 150Subject Index
3571IntroductionKai Johnsson, and Susanne BrakmannThe application
of evolutionary and combinatorial techniques to study and solve
com-plex biological and chemical problems has become one of the
most dynamic fields inchemistry and biology. The book presented
here is a loose collection of articles aimingto provide an overview
of the current state of the art of the directed evolution of
pro-teins as well as highlighting the challenges and possibilities
in the field that lie ahead.Although the first examples of directed
molecular evolution date back to the pioneer-ing experiments of S.
Spiegelman et al. and of M. Eigen and W. Gardiner, who pro-posed
that evolutionary approaches be adapted for the engineering of
biomolecules [1,2], it was the success of methods such as phage
display for in vitro selection of peptidesand proteins as well the
selection of functional nucleic acids using the SELEX proce-dure
(Systematic Evolution of Ligands by Exponential enrichment) that
brought thepower of this concept to the attention of the general
scientific community [3, 4]. Inthe last decade, directed evolution
has become a key technology for biomolecule en-gineering. The
success of the evolutionary approach, however, not only depends
onthe potency of the method itself but is also a result of the
limitations of alternativeapproaches, as our lack of understanding
of the structure-function relationship ofproteins in general
hinders the rational design of biomolecules with new func-tions.
What are the prerequisites for a successful directed evolution
experiment?In its broadest sense, (directed) evolution can be
considered as repeated cycles of var-iation followed by selection.
In the first chapter of the book, the underlying principlesof this
concept and their application to the evolutionary design of
biomolecules arereviewed by P. Schuster one of the pioneers in the
field of molecular evolution.Naturally, the first step of each
evolutionary project is the creation of diversity. Themost
straightforward approach to create a library of proteins is to
introduce randommutations into the gene of interest by techniques
such as error-prone PCR or satura-tion mutagenesis. The success of
random mutagenesis strategies is witnessed by theirample
appearances in the different chapters of this book describing case
studies ofparticular classes of proteins and enzymes. In addition,
recombination of mutantDirected Molecular Evolution of Proteins: or
How to Improve Enzymes for Biocatalysis.Edited by Susanne Brakmann
and Kai JohnssonCopyright 2002 Wiley-VCH Verlag GmbH & Co.
KGaAISBNs: 3-527-30423-1 (Hardback); 3-527-60064-7
(Electronic)genes by DNAshuffling or related techniques can be used
to create additional diversityand to accumulate rapidly beneficial
and additive point mutations [5]. This is a keytechnique that also
surfaces in the majority of the chapters. The sequence
spacesearched by these approaches is, however, quite limited. DNA
shuffling betweenhomologous genes, which has also been called
family shuffling, allows yet unexploredregions of sequence space to
be accessed [6]. In the chapter by S. Lutz and S. J. Ben-kovic, an
approach to create chimeras even between non-homologous genes and
itsapplication in protein engineering is described.An interesting
alternative to the generation of libraries with in vitro methods is
thegeneration of so-called environmental libraries, described by R.
Daniel. Here, advan-tage is taken of natural microbial diversity by
isolating and cloning environmentalDNA and by using the resulting
libraries to search for novel biocatalysts.After the creation of
diversity, i.e. the generation of a library of different mutants,
theprotein(s) with the desired phenotype (function or activity)
have to be selected fromthelibrary. This can be achieved by either
selection or screening procedures. The principaladvantage of
selection is that much larger libraries can be examined: the number
ofclones that can be subjected to selection is, in general, five
orders of magnitudes abovethose that can be sorted by advanced
screening methods. Impressive examples for thepower of true
selection, where the survival of the host is directly coupled to
the desiredphenotype, can be found in the chapters written by D.
Hilvert et al. and J. F. Davidson etal.. The major challenge of
most selection approaches is to couple the desired pheno-type, such
as the catalysis of an industrially important reaction, to the
survival of thehost. But what can be done if the desired phenotype
cannot provide a direct selectiveadvantage to a given host
organism? Different approaches appear feasible: if the de-sired
property binds to a given molecule, display systems for the protein
of interestsuch as phage display, ribosomal display or mRNA
display, and the subsequent in vitroselection of binders by
so-called panning procedures are established technologies [3, 7,8].
A recent publication by the group of J. W. Szostak describes the
employment of invitro selection of functional proteins from
libraries of completely randomized 80mers(actual library size
$1013) using mRNA display. This work highlights the power of
invitro selection, and is a striking example of an experiment that
would simply be im-possible to perform using screening procedures
[9]. In the chapter written by P. Sou-million and J. Fastrez, an
interesting extension of this approach, the in vitro selection
ofnovel enzymatic activities using phage display, is reviewed.
Here, clever selectionschemes link the immobilization of the phage
to the desired reactivity.Another approach to the selection of
biomolecules with novel functionalities, i.e.binding, or even
enzymatic activity, is based on the yeast two- and three-hybrid
sys-tem. The potential and limitations of these and related
approaches are reviewed in thechapter contributed by the group of
V. W. Cornish et al.1 Introduction 2Despite their inferiority in
terms of number of clones examined, screening proce-dures have
become increasingly important over the last years. One important
reasonfor this is the enormous technological progress that has been
achieved in automationand miniaturization, allowing up to
106different mutants to be screened in a reason-able timeframe. An
overview of advanced screening strategies is given in the article
ofA. Schwienhost. In the chapter written by K. D. Wittrup a
discussion of the prerequi-sites for a successful screening process
is given, analyzing the outcome of the directedevolution of
proteins displayed on cell surfaces as a function of the screening
condi-tions. The power of intelligently designed screening
processes is demonstrated in thefollowing contributions: M. T.
Reetz and K.-E. Jaeger describe screening techniques toengineer the
enantioselectivity of enzymes; T. Lanio et al. present their
approaches forthe evolutionary generation of restriction
endonucleases, U. T. Bornscheuer reports onthe functional
optimization of lipases, and last but not least, P. C. Cirino and
F. H.Arnold give an overview of directed evolution experiments with
heme enzymes.Clearly, there are various developments and
applications in the field of directedevolution that are not covered
by any of the articles published in this book. Neverthe-less, we
hope to provide a snapshot of this rapidly developing field that
will inspire andsupport scientists with different backgrounds and
intentions in planning their ownexperiments.Finally, we would like
to thank all authors for their contributions, and P. Golitz andK.
Kriese of Wiley-VCH for their continuous motivation and help in
getting this bookpublished.References[1] S. Spiegelman, I. Haruna,
I. B. Holland,G. Beaudreau, D. Mills, Proc. Natl. Acad. Sci.USA
1965, 54, 919927.[2] M. Eigen, W. Gardiner, Pure Appl. Chem.1984,
56, 967978.[3] G. P. Smith, Science 1985, 28, 13151317.[4] a) C.
Tuerk, L. Gold, Science 1990, 249,505510; b) A. D. Ellington, J. W.
Szostak,Nature 1990, 346, 818822.References[5] W. P. Stemmer,
Nature 1994, 370, 389391.[6] A. Crameri, S. A. Raillard, E.
Bermudez,W. P. Stemmer, Nature 1998, 391, 288291.[7] J. Hanes, A.
Pluckthun, Proc. Natl. Acad.Sci. USA 1997, 91, 49374942.[8] R. W.
Roberts, J. W. Szostak, Proc. Natl.Acad. Sci. USA 1997, 94,
1229712302.[9] A. D. Keefe, J. W. Szostak, Nature 2001,
410,715718.1 Introduction 32Evolutionary Biotechnology From Ideas
and Conceptsto Experiments and Computer SimulationsPeter
SchusterResearch on biological evolution entered the realm of
science in the 19th century withthe centennial publications by
Charles Darwin and Gregor Mendel. Molecular modelsfor evolution
under controlled conditions became available only in the second
half ofthe twentieth century after the initiation of molecular
biology. This chapter presents anaccount of the origins of
molecular evolution and develops the concepts that have led
tosuccessful applications in the evolutionary design of biopolymers
with predefinedproperties and functions.2.1Evolution in vivo From
Natural Selection to Population GeneticsNature is the unchallenged
master in design by variation and selection and sinceCharles
Darwin's epochal publication of the Origin of Species [1, 2] the
basic prin-ciples of the mechanism behind natural selection have
become known. Darwin de-duced his principle of evolution from
observations in the field and compared spe-cies adapted to their
natural habitats with the results achieved through artificial
selec-tion by animal breeders and in nursery gardens. Natural
selection introduces changesin populations by differential fitness,
which is tantamount to the instantaneous dif-ferences in the
numbers of decedents between two competing variants. In
artificialselection the animal breeder or the gardener interferes
with the natural selection pro-cess by discarding the part of the
progeny with undesired properties. Only shortly afterthe
publication of Darwin's Book of the Century the quantitative rules
of geneticswere discovered by Gregor Mendel [1, 2]. It took,
nevertheless, about seventy yearsbefore Darwin's theory was united
successfully with the consequences of Mendel'sresults in the
development of population genetics [2, 3].The differential
equations of population genetics are commonly derived for
sexuallyreplicating species and thus deal primarily with
recombination as the dominant sourceDirected Molecular Evolution of
Proteins: or How to Improve Enzymes for Biocatalysis.Edited by
Susanne Brakmann and Kai JohnssonCopyright 2002 Wiley-VCH Verlag
GmbH & Co. KGaAISBNs: 3-527-30423-1 (Hardback); 3-527-60064-7
(Electronic)of variation. Mutation is considered as a rather rare
event. In evolutionary design ofbiopolymers the opposite is true:
Mutation is the common source of variation andrecombination occurs
only with special experiments, gene shuffling [4], for exam-ple. In
the formulation of the problem we shall consider here the asexual
case exclu-sively. The mathematical expression dealing with
selection through differential fitnessis then of the formdxkdt = xk
(fknj=1 fjxj) = xk(fk)Y k = 1; 2; F F F ; n: (1)The fraction of
variant Ikis denoted by xkwith rk xk = 1; fk is its fitness value.
Accord-ingly, we introduced f = rk fk xk as the mean fitness of the
population. The mathe-matical role of fis to maintain the
normalization of variables. The interpretation of Eq.(1) is
straightforward: Whenever the differential fitness, fk-f, of a
variant Ik is positiveor its fitness is above average, fk>f,
dxk/dt is positive and this variant will increase infrequency. The
opposite is true if fk kcr = 1 j1=j1; and (7a)Gk is partitioned, if
kk < kcr = 1 j1=j1: (7b)Connectedness of a neutral network,
implying that it consists of a single component, isimportant for
evolutionary optimization. Populations usually cover a connected
area insequence space and they migrate (commonly) by the Hamming
distance moved. Ac-cordingly, if they are situated on a particular
component of a neutral network, they canreach all sequences of this
component. If the single component of the connected neu-tral
network of a common structure spans all sequence space, a
population on it cantravel by random drift through whole sequence
space.Neutral networks connect sequences forming the same secondary
structure of mini-mumfree energy. Every sequence, however, forms a
great number of sub-optimal struc-tures, which are also computable
by suitable algorithms. Seen froma given structure Sk,the neutral
set Gk is surrounded by the set of compatible sequences Ck. This
set containsall sequences which form Sk as sub-optimal or minimum
free energy structure. By tak-ing two structures at random, say Sj
and Sk, and considering the two sets of compatiblesequences, Cjand
Ck, it was proven[42] that the intersection is always non-empty:
CjCk =. In other words, this intersection theoremcan be expressed
by: Given an arbitrary pairof structures, there will be at least
one sequence that can adopt both structures3).3) It is important to
stress that the intersection theoremcannot be extended to three or
more structures: Forthree or more structures there may but need not
exist a sequence that can form all of them [42].2.3 Evolution in
silico From Neutral Networks to Multi-stable Molecules 19Fig. 2.8.
Neutral networks in sequence space.The pre-image of the structure
in the lower part ofthe figure is a connected neutral network
span-ning whole sequence space. Networks of thisclass are typical
for frequent structures. The upperpart of the figure shows an
example of a parti-tioned network, which consists of one
giantcomponent and many small islands. Connectivityis determined by
the mean fraction of neutralneighbors, kk, of the pre-image of the
corre-sponding structure, Sk, in sequence space.2 Evolutionary
Biotechnology From Ideas and Concepts to Experiments and Computer
Simulations 20The existence of extended and connected neutral
networks in RNA sequence spacewas proven by an elegant experiment
recently published by Erik Schultes and DavidBartel [43]. At the
starting point for their work were two ribozymes of known
structureswith chain length k=88: (i) an RNA ligase evolved in the
laboratory [44], and (ii) anatural cleavage ribozyme isolated from
hepatitis delta virus RNA [45]. The two struc-tures have no base
pair in common and apparently no common phylogenetic history.Then,
an RNA sequence was designed and synthesized at the intersection of
the twoneutral networks of the reference structures. This means
that a chimeric sequence wassynthesized which was compatible with
both structures. The chimera did form bothstructures on folding and
showed both activities, although they were substantiallyweaker than
those of the reference ribozymes, the ligase and the cleavage
ribo-zyme, respectively. Only two or three selected point mutations
or base pair exchangesare required, however, to reach full
catalytic efficiency. Still, the two optimized RNAmolecules have a
Hamming distance of about forty from their reference
sequences.Next, Schultes and Bartel explored further the mutational
neighborhoods and foundneutral paths of Hamming distance around 40,
by preparing and analyzing series ofRNA sequences, along which
neighboring sequences differing in a single base or basepair only.
Without interruption these two neutral paths lead from the chimeric
RNAwith both catalytic activities to the two reference ribozymes.
This result presents adirect proof for a sequence space-wide
extension of the two neutral networks aswell as an experimental
confirmation of the existence of a non-empty intersectionof the two
compatible sets. The existence of multi-stable RNA molecules has
beenderived also by means of a recently developed kinetic folding
algorithm [46], whichresolves the folding process to elementary
steps involving single base pairs. Applica-tion to sequences at the
intersection of structures allows the design of moleculesswitching
between two or more conformations with predefined rate constants
[47].Computer simulations of evolution in sequence space through
replication and mu-tation in populations of RNAmolecules under the
conditions of a flowreactor (Fig. 2.4)were carried out first in the
1980s [48]. Typical sustainable population sizes are be-tween one
thousand and one hundred thousand molecules. The mutation rate,
p,is adjusted to the chain lengths of the molecules so that the
majority of mutationevents leads to single point mutations and
double mutations in a single replicationevent are very rare. Basic
to these in silico studies is a straightforward introductionof
phenotypes, represented by molecular structures, into the model
(Fig. 2.9). Everynewly formed genotype produced in the population
by an off-the-cloud mutation (Fig.2.5) is folded into its minimum
free energy structure and the resulting structure isevaluated to
yield the replication rate of fitness value of the new molecular
variant.These early studies of evolution in silico provided already
clear evidence for the punc-tuated nature of the optimization
process and neutral drift during the epochs of phe-notypic stasis,
independent of whether the simulations were conceived to aim at
one2.3 Evolution in silico From Neutral Networks to Multi-stable
Molecules 21particular target structure or at some property shared
by several classes of structures.Later on, further studies on
neutral evolution were performed with the goal to checkthe
diffusion approximation of random drift [10]. A more recent
investigation [49, 50]explored and revealed the mechanism of
punctuated evolution. A typical plot of thecourse of the
mutation-selection process is shown in Fig. 2.10: The mean distance
totarget of the population (which is a measure of fitness in these
simulation experi-ments) is plotted against time and shows
pronounced punctuation. Adaptive periodsare interrupted by long
epochs of stasis with respect to fitness. Evolution in
genotypespace, however, neither slows down nor stops on the mean
fitness plateaus [51]. In-spection of the sequence distribution of
the population provides new insights into theprocess.An
evolutionary trajectory leading from an initial population to the
final state ischaracterized by a uniquely defined time-ordered
series of phenotypes, called the relayseries [49]. It can also be
understood as a series of transitions between pairs of con-secutive
phenotypes in the relay series. Transitions are off-the-cloud
mutations lead-ing to newphenotypes and fall into two classes: (i)
minor or continuous transitions andFig. 2.9. Evolutionary dynamics
with pheno-types. The sketch shows a sequence of eventsfollowing an
off-the-cloud mutation and leadingan innovation, which consists in
the incorporationof a new mutant into the
replication-mutationensemble: (i) A new variant sequence, Ik,
iscreated through a mutation, Ij Ik, (ii) the se-quence is
converted into a structure, Sk = (Ik),and (iii) the fitness of the
new phenotype isdetermined by means of the mapping fk = f
(Sk).Eventually, the new variant is fully integrated intothe
replication-mutation ensemble.2 Evolutionary Biotechnology From
Ideas and Concepts to Experiments and Computer Simulations 22(ii)
major or discontinuous transitions.4)Minor transitions between
structures occurwith high frequency and involve changes that are
easy to accomplish with a singlepoint mutation, like opening or
closing of single base pairs adjacent to stacks. Open-ing of stacks
with marginal stability also falls into this class. The sequence
constraint islow: Almost every sequence forming the initial
structure yields the final structure of aminor transition on one or
a few different single point mutations. Major transitionsbetween
structures require simultaneous changes in several adjacent and/or
distantbase pairs and occur at single point mutations with low
probability only. Major transi-tions are characterized by strong
constraints on initial sequences. In other words, theyrequire
special initial sequences and thus occur with low probability when
averagedover the entire neutral network.Analysis of the dynamics on
the plateaus of constant fitness falls into one of twodifferent
scenarios: (i) Neutral evolution in the conventional sense
consisting of chan-ging genotypes that give rise to the same
phenotype or phenotypic stasis expressed by asingle phenotype on
the relay series, and (ii) a neutral random walk on a subset
ofclosely related phenotypes of identical fitness, which are
accessible from each otherthrough minor transitions, that manifest
itself by a sometimes large number of stepsin the relay series with
frequent repetitions of particular phenotypes. Very rarely,
fit-ness neutral major transitions are also observed inside fitness
plateaus. As we shall seebelow the two scenarios are not very
different in reality: Scenario (ii) is readily con-verted into
scenario (i) by an increase in population size. Each
quasi-stationary epochends with a major transition that is
accompanied by a gain in fitness. Astraightforwardinterpretation of
this finding suggests that the population undertakes a random
searchduring the epochs of phenotypic stasis until a mutant
sequence is produced that in-itiates a fitness improving major
transition. A cascade of fitness improving minortransitions
commonly follows the major transition, and the close neighborhood
ofthe new variant is thereby instantaneously explored.The
explanation given above is strongly supported by the dynamics
observed ingenotype space. When the population enters a fitness
plateau the distribution of gen-otypes is very narrow (Fig. 2.10).
Then, while the population diffuses on a neutralsubspace of
sequence space, the width of the mutant cloud increases steadily
andseems to approach a saturation phase. Instantaneously, when the
population reachesthe end of the fitness plateau, the width of the
distribution drops as the populationpasses a bottleneck in genotype
space. This picture of population dynamics on theneutral subset,
slow spread and fast contraction, is complemented by a recordingof
the migration of the population center through sequence space. On
the plateau,during the spread of the distribution, the center is
almost stationary or drifts very4) The choice of the adjectives
continuousand discontinuous points to topological relations between
thepre-images of the corresponding structures in sequence space
[52].2.3 Evolution in silico From Neutral Networks to Multi-stable
Molecules 23slowly. At the end of the quasi-stationary epoch,
however, the velocity of the populationcenter shows a sharp peak
corresponding to a jump in sequence space. Major transi-tions lead
to genotypes, which represent bottlenecks for evolutionary
optimization.Individual trajectories of evolution in the flow
reactor are not reproducible in detail.Relay series of different
computer runs under identical conditions5)involve
differentstructures and the corresponding genotypes have sequences
that diverge from initialconditions. Almost all quantities, for
example the number of replications required toreach the target or
the number of minor transitions, show widely scattered
distribu-Fig. 2.10. Variability in genotype space duringpunctuated
evolution. Shown are the results of asimulation of RNA optimization
towards a tRNAtarget with population size N = 3000 and muta-tion
rate p = 0.001 per site and replication event:(i) The trace of the
underlying trajectory recordingthe average distance from target,
(gray,left ordinate scaled by 0.22, or full length is 50)and (ii)
two plots of different measures of evo-lution in genotype space,
the migration of thepopulation center (with dt = 8000replications)
and the width of the population, against time expressed as the
totalnumber of replications performed until time t.The upper plot
is a measure of genotype diversityand shows the mean Hamming
distance withinthe population (, dotted line, right ordi-nate). The
lower curve presents the Hammingdistance between the centers of the
population attimes t and t+dt (, full line, left ordi-nate) and
measures the drift velocity of the po-pulation center. The arrow
indicates a sharp peakof at the end of the second longplateau,
which reaches a height of Hammingdistance ten.5) Identical
conditions here means that everything was chosen to be the same
except the seeds of therandom number generator.2 Evolutionary
Biotechnology From Ideas and Concepts to Experiments and Computer
Simulations 24tions. Population size effects on the evolutionary
processes are pronounced. The num-ber of replications increases
with population size, a dramatic effect is seen with thenumber of
minor transitions: It decreases by a factor of about four in the
range be-tween N= 1000 and N= 100000 molecules. The number of major
transitions, however,shows only small scatter and is remarkably
constant in this range of population sizes.Modeling of neutral
evolution by means of a birth-and-death process provides
astraightforward interpretation of this result: Minor transitions
have a sufficientlyhigh probability of occurrence such that
frequent variants, once formed, stay in a lar-ger population and do
not reappear in further steps of the relay series. The low
sen-sitivity of the numbers of major transitions to both population
size and sequence ofrandom events, however, makes them candidates
for constants of evolution: Theyrepresent essential innovations and
their number appears to depend only on initialand final
state.2.4Sequence Structure Mappings of ProteinsIn this section we
do not aim at a presentation of the current state of the art in
thedesign of proteins by variation and selection. This will be done
in great detail in theother chapters of this volume. What we shall
try to do instead is a comparison of resultsderived for proteins
and RNA molecules to point out common features as well
asdifferences.The experimental results of selection and evolution
of molecules derived here camemainly from investigations on RNA
molecules and this simply because RNA is bettersuited for studies,
since (i) RNA unites the properties of genotype and phenotype inone
and the same molecule, and (ii) the bases in the base pairs of the
stacking regionsof RNA are complementary (AU, GC, and sometimes
GU). These relations are funda-mental for the simple logic for
secondary structure formation, and have no counterpartin proteins.
In addition, RNA secondary structures play almost always the role
of anintermediate in the kinetic folding process and thus have a
physical meaning. A thirdproblem with the evolutionary design of
proteins is the problem to link messengersand function carriers.
This can be solved elegantly by the various display
techniques:phage, bacterial, ribosomal display and others. Another
elegant method based on acovalent link between RNA and protein has
been used in a paper discussed below[53]. Although variation
selection methods are available for proteins, they cannotcompare
successfully with the ease of selection procedures when both
propertiesare contained in the same molecule like in the case of
RNA.Protein sequence space was postulated as a useful tool for
discussing protein evolu-tion already in 1970 [54]. Later on most
extensive model studies were more or less2.4 Sequence Structure
Mappings of Proteins 25confined to rather simple lattice models
[55]. Systematic studies on random sequencemodel proteins [56] gave
two important results: (i) more sequences than structures,and (ii)
a few common folds compared to a great variety of rare folds. The
secondfinding was also obtained by different stability
considerations [57]. It is worth noticingthat the frequency
distribution of protein lattice fold is remarkably similar to that
ofRNA molecules with random sequences of the same chain length
[40]. Shape spacecovering as observed with RNAs does not hold for
lattice proteins [41].Neutral networks [41] represent more or less
the basic and most important feature ofgenotype-phenotype mappings.
Although protein structure and function has beendiscussed with
respect to neutrality for a very long time, direct evidence for
neutralityand neutral networks came only recently fromempirical
potentials and neural networkstudies [58, 59]. Other investigations
on protein foldability landscapes are in generalagreement with the
existence of extended neutral networks too [60, 61].It is worth
mentioning in this context that there seems to be a general
differencebetween RNA and protein landscapes: Certain amino acid
composition ratios betweenhydrophobic and hydrophilic amino acids
presumably give rise to insoluble aggregatesand this may lead to
holes in protein sequence space. Perhaps, the concept of
holeyadaptive landscapes as favored in a series of recent papers on
models of evolution [62]might be useful in this context.Finally,
two experimental results are highly relevant in this context: The
first studyon true random sequence proteins [53] revealed that the
occurrence of function inprotein sequence space has approximately
the same probability, 1012, as in RNA se-quence space. The second
remarkable finding showed that very different structures
ofproteins, with no sequence homology, of course, gave rise to the
binding affinity toAMP, the target molecule. More studies following
along the line of this elegant experi-ment will provide the desired
insight in protein sequence-structure mapping. Thesecond experiment
was done four years ago [63]: Two protein molecules with 50
%sequence homology have entirely different structures. A fully
b-sheet structure wasturned into an a-helix bundle by changing only
half of the amino acid residues. En-tirely different structures can
be found at not too large Hamming distances in se-quence
space.2.5Concluding RemarksWhat distinguishes the evolutionary
strategy from conventional or rational design?The primary and most
important issue is that we need not know the structure thatyields
the desired function. It is sufficient to derive an assay that
allows for testingwhether or not a candidate molecule has the
desired property. At the current state2 Evolutionary Biotechnology
From Ideas and Concepts to Experiments and Computer Simulations
26of the art, de novo rational design of biopolymers gives very
poor results and as long asthis deficiency in structure prediction
methods cannot be overcome, evolutionarysearch for function will be
superior.Variation and selection turns out to be an enormously
potent tool for improvementalso in vitro. Why this is so, does not
trivially follow from the nature of randomsearches. The efficiency
of Monte-Carlo methods may work very poorly as weknow from other
optimization problems. The intrinsic regularities of
genotype-phe-notype mappings with high degrees of neutrality and
very wide scatter of the points insequence space, which lead to the
same or very similar solutions, are the clues toevolutionary
success.AcknowledgementsThe work reported here was supported
financially by the Austrian Fond zur Forderungder
wissenschaftlichen Forschung (FWF), Projects P-13093-GEN,
P-13887-MOB, and P-14898-MAT as well as by the Jubilaumsfond der
Osterreichischen Nationalbank, ProjectNo.7813.References[1] K.
Sander, Biologie in unserer Zeit, 1988, 18,161167 (in German).[2]
G. de Beer, Notes and Records of the RoyalSociety of London 1964,
19, 192226.[3] R. A. Fisher, The genetical theory of
naturalselection, Oxford University Press, Oxford(UK), 1930.[4] W.
P. C. Stemmer, Proc. Natl. Acad. Sci.USA, 1994, 91, 1074710751.[5]
M. Kimura, The neutral theory of molecularevolution, Cambridge
University Press,Cambridge (UK), 1983.[6] J. L. King, T. H. Jukes,
Science, 1969,788798.[7] S. F. Elena, V. S. Cooper, R. E.
Lenski,Science 1996, 272, 18021804.[8] D. Papadopoulos, D.
Schneider, J. M.Meier-Eiss, W. Arber, R. E. Lenski, M. Blot,Proc.
Natl. Acad. Sci. USA, 1999, 96,38073812.[9] B. Derida, L. Peliti,
Bull. Math. Biol., 1991,53, 355382.[10] M. A. Huynen, P. F.
Stadler, W. Fontana,Proc. Natl. Acad. Sci. USA 1996, 93,397401.[11]
M. Eigen, W. C. Gardiner, Pure Appl.Chem. 1984, 56,
967978.References[12] S. Spiegelman, Quart. Rev. Biophys., 1971,4,
213253.[13] H. F. Judson, The eighth day of creation,Jonathan Cape,
London,1979.[14] R. W. Hamming, Coding and informationtheory,
2nded., Prentice Hall, EnglewoodCliffs, NJ, 1989.[15] P. F.
Stadler, G. P. Wagner. Evol. Comp.,1998, 5, 241275.[16] M. Eigen,
Naturwissenschaften, 1971, 58,465523.[17] E. Domingo, J. J.
Holland, Annu. Rev.Microbiol., 1997, 51, 151178.[18] C. K.
Biebricher, W. C. Gardiner,Biophys. Chem., 1997, 66, 179192.[19] M.
Eigen, P. Schuster, Naturwissenschaften,1977, 64, 541565.[20] M.
Eigen, J. McCaskill, P. Schuster,Adv. Chem. Phys., 1989, 75,
149263.[21] J. W. Drake, Proc. Natl. Acad. Sci. USA,1991, 88,
71607164.[22] J. W. Drake, Proc. Natl. Acad. Sci. USA,1993, 90,
41714175.[23] J. W. Drake, B. Charlesworth,D. Charlesworth, J. F.
Crow. Genetics,1998, 148, 166716862.5 Concluding Remarks 27[24] P.
Schuster, J. Swetina, Bull. Math. Biol.,1988, 50, 635660.[25] C. L.
Burch, L. Chao, Nature, 2000, 406,625628.[26] C. O. Wilke, J. L.
Wang, C. Ofria, R. E.Lenski, C. Adami, Nature, 2001,
412,331333.[27] A. D. Ellington, J. W. Szostak, Nature,1990, 346,
818822.[28] C. Tuerk, L. Gold, Science, 1990, 249,505510.[29]
A.Watts, G. Schwarz, Biophys. Chem.,1997, 66 (2/3), 67284.[30] D.
S. Wilson, J. W. Szostak, Ann. Rev.Biochem., 1999, 68, 611147.[31]
L. Gold, C. Tuerk, P. Allen, J. Binkley,D. Brown, L. Green, S.
MacDougal,D. Schneider, D. Tasset, S. R. Eddy. In:R. F. Gestland,
J. F. Atkins, eds. The RNAworld. Cold Spring Harbor Press,
Plain-view, NY, 1993, pp. 497509.[32] A. A. Beaudry, G.F. Joyce,
Science, 1992,257, 635641.[33] R. R. Breaker, Chem. Rev., 1997,
97,371390.[34] N. Lehman, G. F. Joyce, Current Biology,1993, 3,
723734.[35] D. P. Bartel, J. W. Szostak, Science, 1993,261,
14111418.[36] R. F. Gesteland, J. F. Atkins, eds. The RNAworld.
Cold Spring Harbor Press, Plain-view, NY, 1993.[37] P. Schuster,
Biol. Chem., 2001, 382,in press.[38] P. Higgs, Quart. Rev.
Biophys., 2000, 33,199253.[39] P. Schuster, P. F. Stadler, In: M.
J. C.Crabbe, M. Drew, A. Konopka, Handbookof Computational
Chemistry, Marcel Dek-ker, New York, 2001, in press.[40] W. Gruner,
R. Giegerich, D. Strothmann,C. Reidys, J. Weber, I. L. Hofacker,P.
F. Stadler, P. Schuster, Mh. Chem., 1996,127, 355389.[41] P.
Schuster, W. Fontana, P. F. Stadler,I. L. Hofacker, Proc. Roy. Soc.
London B,1994, 255, 279284.[42] C. Reidys, P. F. Stadler,
P.Schuster, Bull.Math. Biol., 1997, 59, 339397.[43] E. A. Schultes,
D. P. Bartel, Science, 2000,289, 448452.[44] E. H. Ekland, J. W.
Szostak, D. P. Bartel,Science, 1995, 269, 364370.[45] A. T.
Perotta, M. D. Been, J. Mol. Biol.,1998, 279, 361373.[46] C. Flamm,
W. Fontana, I. L. Hofacker,P. Schuster, RNA, 2000, 6, 325338.[47]
C. Flamm, I. L. Hofacker, S. Maurer-Stroh,P. F. Stadler, M. Zehl,
RNA 2001, 7,254265.[48] W. Fontana, P. Schuster, Biophys.
Chem.,1987, 26, 123147.[49] W. Fontana, P. Schuster, Science,
1998,280, 14511455.[50] P. Schuster, W. Fontana, Physica D,
1999,133, 427452.[51] P. Schuster, A. Wernitznig, Is there
aconstant number of evolutionary innovationsrequired to reach a
given target? Preprint,2001.[52] B. M. Stadler, P. F. Stadler, G.
P. Wagner,W. Fontana, J. Theor. Biol., 2002, in press.[53] A. D.
Keefe, J. W. Szostak, Nature, 2001,410, 715718.[54] J. Maynard
Smith, Nature 1970, 225,563564.[55] K. Yue, K. M. Fiebig, P. D.
Thomas,H. S. Chan, E. I. Shakhnovich, K. A. Dill,Proc. Natl. Acad.
Sci. USA, 1993, 90,19421946.[56] H. Li, R. Helling, C. Tang, N.
Wingreen,Science, 1996, 273, 666669.[57] S. Govindarajan, R. A.
Goldstein, Proc.Natl. Acad. Sci. USA, 1996, 93, 33413345.[58] A.
Babajide, I. L. Hofacker, M. J. Sippl,P. F. Stadler, Folding &
Design, 1997, 2,261269.[59] A. Babajide, R. Farber, I. L.
Hofacker,J. Inman, A. S. Lapedes, P. F. Stadler,J. Theor. Biol,
2001, 212, 3540.[60] S. Govindarajan, R. A. Goldstein,
Biopoly-mers, 1997, 42, 427438.[61] S. Govindarajan, R. A.
Goldstein, Proteins,1997, 29, 461466.[62] S. Gavrilets, Trends in
Ecology andEvolution, 1997, 12, 307312.[63] S. Dalal, S.
Balasubramanian, L. Regan,Nat. Struct. Biol., 1997, 4, 548552.2
Evolutionary Biotechnology From Ideas and Concepts to Experiments
and Computer Simulations 283Using Evolutionary Strategies to
Investigate the Structureand Function of Chorismate Mutases1)Donald
Hilvert, Sean V. Taylor, and Peter Kast3.1IntroductionEvolution is
the slow and continual process by which all living species
diversify andbecome more complex. Through recursive cycles of
mutation, selection and amplifi-cation, new traits accumulate in a
population of organisms [1]. Those that provide anadvantage under
prevailing environmental conditions are passed from one
generationto the next. Since ancient times, man has exploited
evolution in a directed way toproduce plants and animals with
useful characteristics. Crossbreeding individualswith favorable
traits successfully harnesses sexual recombination, one of the
mostpowerful evolutionary strategies to generate new variants. From
these crossings, pro-geny with improved features are chosen for
additional breeding cycles, thus channel-ing the course of
development.Biologists and chemists have recently begun to use
evolutionary strategies to studyand tailor the properties of
individual molecules rather than whole organisms. Anarray of
methods has been developed to generate diversity in populations of
mole-cules. Depending on the experiment, mutagenesis might entail
degen