Part I Directed Evolution 1
1 Continuous Evolution of Proteins In Vivo 3
Alon Wellner, Arjun Ravikumar, and Chang C. Liu
1.1 Introduction 3
1.2 Challenges in Achieving In Vivo Continuous Evolution 5
1.3 Phage-Assisted Continuous Evolution (PACE) 10
1.4 Systems That Allow In Vivo Continuous Directed Evolution 13
1.4.1 Targeted Mutagenesis in E. coli with Error-Prone DNA Polymerase I 13
1.4.2 Yeast Systems That Do Not Use Engineered DNA Polymerases for Mutagenesis 16
1.4.3 Somatic Hypermutation as a Means for Targeted Mutagenesis of GOIs 18
1.4.4 Orthogonal DNA Replication (OrthoRep) 20
1.5 Conclusion 22
References 22
2 In Vivo Biosensors for Directed Protein Evolution 29
Song Buck Tay and Ee Lui Ang
2.1 Introduction 29
2.2 Nucleic Acid-Based In Vivo Biosensors for Directed Protein Evolution 32
2.2.1 RNA-Type Biosensors 32
2.2.2 DNA-Type Biosensors 35
2.3 Protein-Based In Vivo Biosensors for Directed Protein Evolution 37
2.3.1 Transcription Factor-Type Biosensors 37
2.3.2 Enzyme-Type Biosensors 41
2.4 Characteristics of Biosensors for In Vivo Directed Protein Evolution 44
2.5 Conclusions and Future Perspectives 45
Acknowledgments 46
References 46
3 High-Throughput Mass Spectrometry Complements Protein Engineering 57
Tong Si, Pu Xue, Kisurb Choe, Huimin Zhao, and Jonathan V. Sweedler
3.1 Introduction 57
3.2 Procedures and Instrumentation for MS-Based Protein Assays 59
3.3 Technology Advances Focusing on Throughput Improvement 62
3.4 Applications of MS-Based Protein Assays: Summary 63
3.4.1 Applications of MS-Based Assays: Protein Analysis 64
3.4.2 Applications of MS-Based Assays: Protein Engineering 66
3.5 Conclusions and Perspectives 68
Acknowledgments 68
References 69
4 Recent Advances in Cell Surface Display Technologies for Directed Protein Evolution 81
Maryam Raeeszadeh-Sarmazdeh and Wilfred Chen
4.1 Cell Display Methods 81
4.1.1 Phage Display 81
4.1.2 Bacterial Display Systems 83
4.1.3 Yeast Surface Display 84
4.1.4 Mammalian Display 85
4.2 Selection Methods and Strategies 86
4.2.1 High-Throughput Cell Screening 86
4.2.1.1 Panning 86
4.2.1.2 FACS 86
4.2.1.3 MACS 87
4.2.2 Selection Strategies 88
4.2.2.1 Competitive Selection (Counter Selection) 88
4.2.2.2 Negative/Positive Selection 89
4.3 Modifications of Cell Surface Display Systems 89
4.3.1 Modification of YSD for Enzyme Engineering 89
4.3.2 Yeast Co-display System 91
4.3.3 Surface Display of Multiple Proteins 91
4.4 Recent Advances to Expand Cell-Display Directed Evolution Techniques 93
4.4.1 μSCALE (Microcapillary Single-Cell Analysis and Laser Extraction) 93
4.4.2 Combining Cell Surface Display and Next-Generation Sequencing 94
4.4.3 PACE (Phage-Assisted Continuous Evolution) 94
4.5 Conclusion and Outlook 96
References 97
5 Iterative Saturation Mutagenesis for Semi-rational Enzyme Design 105
Ge Qu, Zhoutong Sun, and Manfred T. Reetz
5.1 Introduction 105
5.2 Recent Methodology Developments in ISM-Based Directed Evolution 108
5.2.1 Choosing Reduced Amino Acid Alphabets Properly 109
5.2.1.1 Limonene Epoxide Hydrolase as the Catalyst in Hydrolytic Desymmetrization 109
5.2.1.2 Alcohol Dehydrogenase TbSADH as the Catalyst in Asymmetric Transformation of Difficult-to-Reduce Ketones 110
5.2.1.3 P450-BM3 as the Chemo- and Stereoselective Catalyst in a Whole-Cell Cascade Sequence 112
5.2.1.4 Multi-parameter Evolution Aided by Mutability Landscaping 115
5.2.2 Further Methodology Developments of CAST/ISM 117
5.2.2.1 Advances Based on Novel Molecular Biological Techniques and Computational Methods 117
5.2.2.2 Advances Based on Solid-Phase Chemical Synthesis of SM Libraries 118
5.3 B-FIT as an ISM Method for Enhancing Protein Thermostability 120
5.4 Learning from CAST/ISM-Based Directed Evolution 121
5.5 Conclusions and Perspectives 121
Acknowledgment 124
References 124
Part II Rational and Semi-Rational Design 133
6 Data-driven Protein Engineering 135
Jonathan Greenhalgh, Apoorv Saraogee, and Philip A. Romero
6.1 Introduction 135
6.2 The Data Revolution in Biology 136
6.3 Statistical Representations of Protein Sequence, Structure, and Function 138
6.3.1 Representing Protein Sequences 138
6.3.2 Representing Protein Structures 140
6.4 Learning the Sequence-Function Mapping from Data 141
6.4.1 Supervised Learning (Regression/Classification) 141
6.4.2 Unsupervised/Semisupervised Learning 144
6.5 Applying Statistical Models to Engineer Proteins 145
6.6 Conclusions and Future Outlook 147
References 148
7 Protein Engineering by Efficient Sequence Space Exploration Through Combination of Directed Evolution and Computational Design Methodologies 153
Subrata Pramanik, Francisca Contreras, Mehdi D. Davari, and Ulrich Schwaneberg
7.1 Introduction 153
7.2 Protein Engineering Strategies 154
7.2.1 Computer-Aided Rational Design 155
7.2.1.1 FRESCO 155
7.2.1.2 FoldX 157
7.2.1.3 CNA 158
7.2.1.4 PROSS 159
7.2.1.5 ProSAR 160
7.2.2 Knowledge Based Directed Evolution 161
7.2.2.1 Iterative Saturation Mutagenesis (ISM) 161
7.2.2.2 Mutagenic Organized Recombination Process by Homologous In Vivo Grouping (MORPHING) 161
7.2.2.3 Knowledge Gaining Directed Evolution (KnowVolution) 162
7.3 Conclusions and Future Perspectives 171
Acknowledgments 171
References 171
8 Engineering Artificial Metalloenzymes 177
Kevin A. Harnden, Yajie Wang, Lam Vo, Huimin Zhao, and Yi Lu
8.1 Introduction 177
8.2 Rational Design 177
8.2.1 Rational Design of Metalloenzymes Using De Novo Designed Scaffolds 177
8.2.2 Rational Design of Metalloenzymes Using Native Scaffolds 179
8.2.2.1 Redesign of Native Proteins 179
8.2.2.2 Cofactor Replacement in Native Proteins 181
8.2.2.3 Covalent Anchoring in Native Protein 184
8.2.2.4 Supramolecular Anchoring in Native Protein 187
8.3 Engineering Artificial Metalloenzyme by Directed Evolution in Combination with Rational Design 188
8.3.1 Directed Evolution of Metalloenzymes Using De Novo Designed Scaffolds 188
8.3.2 Directed Evolution of Metalloenzymes Using Native Scaffolds 189
8.3.2.1 Cofactor Replacement in Native Proteins 189
8.3.2.2 Covalent Anchoring in Native Protein 192
8.3.2.3 Non-covalent Anchoring in Native Proteins 194
8.4 Summary and Outlook 200
Acknowledgment 201
References 201
9 Engineered Cytochromes P450 for Biocatalysis 207
Hanan Alwaseem and Rudi Fasan
9.1 Cytochrome P450 Monooxygenases 207
9.2 Engineered Bacterial P450s for Biocatalytic Applications 210
9.2.1 Oxyfunctionalization of Small Organic Substrates 211
9.2.2 Late-Stage Functionalization of Natural Products 220
9.2.3 Synthesis of Drug Metabolites 224
9.3 High-throughput Methods for Screening Engineered P450s 227
9.4 Engineering of Hybrid P450 Systems 229
9.5 Engineered P450s with Improved Thermostability and Solubility 230
9.6 Conclusions 231
Acknowledgments 232
References 232
Part III Applications in Industrial Biotechnology 243
10 Protein Engineering Using Unnatural Amino Acids 245
Yang Yu, Xiaohong Liu, and Jiangyun Wang
10.1 Introduction 245
10.2 Methods for Unnatural Amino Acid Incorporation 246
10.3 Applications of Unnatural Amino Acids in Protein Engineering 247
10.3.1 Enhancing Stability 248
10.3.2 Mechanistic Study Using Spectroscopic Methods 248
10.3.3 Tuning Catalytic Activity 250
10.3.4 Tuning Selectivity 252
10.3.5 Enzyme Design 252
10.3.6 Protein Engineering Toward a Synthetic Life 255
10.4 Outlook 256
10.5 Conclusions 258
References 258
11 Application of Engineered Biocatalysts for the Synthesis of Active Pharmaceutical Ingredients (APIs) 265
Juan Mangas-Sanchez, Sebastian C. Cosgrove, and Nicholas J. Turner
11.1 Introduction 265
11.1.1 Transferases 266
11.1.1.1 Transaminases 266
11.1.2 Oxidoreductases 267
11.1.2.1 Ketoreductases 267
11.1.2.2 Amino Acid Dehydrogenases 271
11.1.2.3 Cytochrome P450 Monoxygenases 272
11.1.2.4 Baeyer–Villiger Monoxygenases 273
11.1.2.5 Amine Oxidases 274
11.1.2.6 Hydroxylases 276
11.1.2.7 Imine Reductases 276
11.1.3 Lyases 278
11.1.3.1 Ammonia Lyases 278
11.1.4 Isomerases 278
11.1.5 Hydrolases 279
11.1.5.1 Esterases 279
11.1.5.2 Haloalkane Dehalogenase 279
11.1.6 Multi-enzyme Cascade 281
11.2 Conclusions 282
References 287
12 Directing Evolution of the Fungal Ligninolytic Secretome 295
Javier Vina-Gonzalez and Miguel Alcalde
12.1 The Fungal Ligninolytic Secretome 295
12.2 Functional Expression in Yeast 297
12.2.1 The Evolution of Signal Peptides 297
12.2.2 Secretion Mutations in Mature Protein 300
12.2.3 The Importance of Codon Usage 301
12.3 Yeast as a Tool-Box in the Generation of DNA Diversity 302
12.4 Bringing Together Evolutionary Strategies and Computational Tools 305
12.5 High-Throughput Screening (HTS) Assays for Ligninase Evolution 306
12.6 Conclusions and Outlook 309
Acknowledgments 309
References 310
13 Engineering Antibody-Based Therapeutics: Progress and Opportunities 317
Annalee W. Nguyen and Jennifer A. Maynard
13.1 Introduction 317
13.2 Antibody Formats 318
13.2.1 Human IgG1 Structure 318
13.2.2 Antibody-Drug Conjugates 319
13.2.3 Bispecific Antibodies 320
13.2.4 Single Domain Antibodies 321
13.2.5 Chimeric Antigen Receptors 321
13.3 Antibody Discovery 322
13.3.1 Antibody Target Identification 322
13.3.1.1 Cancer and Autoimmune Disease Targets 323
13.3.1.2 Infectious Disease Targets 323
13.3.2 Screening for Target-Binding Antibodies 324
13.3.2.1 Synthetic Library Derived Antibodies 324
13.3.2.2 Host-Derived Antibodies 325
13.3.2.3 Immunization 325
13.3.2.4 Pairing the Light and Heavy Variable Regions 326
13.3.2.5 Humanization 327
13.3.2.6 Hybrid Approaches to Antibody Discovery 328
13.4 Therapeutic Optimization of Antibodies 328
13.4.1 Serum Half-Life 328
13.4.1.1 Antibody Half-Life Extension 329
13.4.1.2 Antibody Half-Life Reduction 331
13.4.1.3 Effect of Half-Life Modification on Effector Functions 331
13.4.2 Effector Functions 331
13.4.2.1 Effector Function Considerations for Cancer Therapeutics 332
13.4.2.2 Effector Function Considerations for Infectious Disease Prophylaxis and Therapy 333
13.4.2.3 Effector Function Considerations for Treating Autoimmune Disease 334
13.4.2.4 Approaches to Engineering the Effector Functions of the IgG1 Fc 334
13.4.3 Tissue Localization 335
13.4.4 Immunogenicity 335
13.4.4.1 Reducing T-Cell Recognition 336
13.4.4.2 Reducing Aggregation 336
13.5 Manufacturability of Antibodies 336
13.5.1 Increasing Antibody Yield 337
13.5.1.1 Codon Usage 337
13.5.1.2 Signal Peptide Optimization 337
13.5.1.3 Expression Optimization 338
13.5.2 Alternative Production Methods 338
13.6 Conclusions 339
Acknowledgments 339
References 339
14 Programming Novel Cancer Therapeutics: Design Principles for Chimeric Antigen Receptors 353
Andrew J. Hou and Yvonne Y. Chen
14.1 Introduction 353
14.2 Metrics to Evaluate CAR-T Cell Function 354
14.3 Antigen-Recognition Domain 356
14.3.1 Tuning the Antigen-Recognition Domain to Manage Toxicity 356
14.3.2 Incorporation of Multiple Antigen-Recognition Domains to Engineer “Smarter” CARs 356
14.3.3 Novel Antigen-Recognition Domains to Enhance CAR Modularity 359
14.3.4 Engineering CARs that Target Soluble Factors 360
14.4 Extracellular Spacer 360
14.5 Transmembrane Domain 362
14.6 Signaling Domain 362
14.6.1 First- and Second-Generation CARs 362
14.6.2 Combinatorial Co-stimulation 363
14.6.3 Other Co-stimulatory Domains: ICOS, OX40, TLR2 364
14.6.4 Additional Considerations for CAR Signaling Domains 364
14.7 High-Throughput CAR Engineering 366
14.8 Novel Receptor Modalities 367
References 369
Part IV Applications in Medical Biotechnology 377
15 Development of Novel Cellular Imaging Tools Using Protein Engineering 379
Praopim Limsakul, Chi-Wei Man, Qin Peng, Shaoying Lu, and Yingxiao Wang
15.1 Introduction 379
15.2 Cellular Imaging Tools Developed by Protein Engineering 380
15.2.1 Fluorescent Proteins 380
15.2.1.1 The FP Color Palette 380
15.2.1.2 Photocontrollable Fluorescent Proteins 381
15.2.1.3 Other Engineered Fluorescent Proteins 383
15.2.2 Antibodies and Protein Scaffolds 383
15.2.2.1 Antibodies 383
15.2.2.2 Antibody-Like Protein Scaffolds 384
15.2.2.3 Directed Evolution 384
15.2.3 Genetically Encoded Non-fluorescent Protein Tags 385
15.3 Application in Cellular Imaging 386
15.3.1 Cell Biology Applications 386
15.3.1.1 Localization 386
15.3.1.2 Cell Signaling 387
15.3.2 Application in Diagnostics and Medicine 390
15.3.2.1 Detection 390
15.3.2.2 Screening for Drugs 392
15.4 Conclusion and Perspectives 393
References 394
Index 403
