Details

Protein Engineering

Tools and Applications
Advanced Biotechnology 1. Aufl.

von: Huimin Zhao, Sang Yup Lee, Jens Nielsen, Gregory Stephanopoulos
153,99 €
Verlag:	Wiley-VCH
Format:	PDF
Veröffentl.:	02.08.2021
ISBN/EAN:	9783527815098
Sprache:	englisch
Anzahl Seiten:	432

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Titelbeschreibung

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

Inhaltsverzeichnis

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

Autorenportrait

Dr. Huimin Zhao is the Steven L. Miller Chair of chemical and biomolecular engineering, and professor of chemistry, biochemistry, biophysics, and bioengineering at the University of Illinois at Urbana-Champaign (UIUC). He received his B.S. degree in Biology from the University of Science and Technology of China in 1992 and his Ph.D. degree in Chemistry from the California Institute of Technology in 1998 under the guidance of Dr. Frances Arnold. Prior to joining UIUC in 2000, he was a project leader at the Industrial Biotechnology Laboratory of the Dow Chemical Company. He was promoted to full professor in 2008. Dr. Zhao served as a consultant for over 10 companies such as Pfizer, Maxygen, BP, Gevo, and zuChem, and a Scientific Advisory Board member of Gevo, Myriant Technologies, Toulouse White Biotechnology (TWB) and AgriMetis. He was a member of National Academies' study group on Industrialization of Biology: A Roadmap to Accelerate Advanced Manufacturing of Chemicals. Dr. Zhao has authored and co-authored over 260 research articles and over 20 issued and pending patent applications with several being licensed by industry. In addition, he has given plenary, keynote or invited lectures in over 290 international meetings, universities, industries, and research institutes. His primary research interests are in the development and applications of synthetic biology tools to address society's most daunting challenges in health, energy, and sustainability, and in the fundamental aspects of enzyme catalysis, cell metabolism, and gene regulation.