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Protein Engineering. Tools and Applications. Edition No. 1. Advanced Biotechnology

  • ID: 5186756
  • Book
  • September 2021
  • 432 Pages
  • John Wiley and Sons Ltd

A one-stop reference that reviews protein design strategies to applications in industrial and medical biotechnology

Protein Engineering: Tools and Applications is a comprehensive resource that offers a systematic and comprehensive review of the most recent advances in the field, and contains detailed information on the methodologies and strategies behind these approaches. The authors - noted experts on the topic - explore the distinctive advantages and disadvantages of the presented methodologies and strategies in a targeted and focused manner that allows for the adaptation and implementation of the strategies for new applications.

The book contains information on the directed evolution, rational design, and semi-rational design of proteins and offers a review of the most recent applications in industrial and medical biotechnology. This important book:

  • Covers technologies and methodologies used in protein engineering
  • Includes the strategies behind the approaches, designed to help with the adaptation and implementation of these strategies for new applications
  • Offers a comprehensive and thorough treatment of protein engineering from primary strategies to applications in industrial and medical biotechnology
  • Presents cutting edge advances in the continuously evolving field of protein engineering

Written for students and professionals of bioengineering, biotechnology, biochemistry, Protein Engineering: Tools and Applications offers an essential resource to the design strategies in protein engineering and reviews recent applications.

Note: Product cover images may vary from those shown

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 Viña-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

Note: Product cover images may vary from those shown
Huimin Zhao
Note: Product cover images may vary from those shown