Protein Engineering

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.

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• Part I Directed Evolution 11 Continuous Evolution of Proteins In Vivo 3Alon Wellner, Arjun Ravikumar, and Chang C. Liu1.1 Introduction 31.2 Challenges in Achieving In Vivo Continuous Evolution 51.3 Phage-Assisted Continuous Evolution (PACE) 101.4 Systems That Allow In Vivo Continuous Directed Evolution 131.4.1 Targeted Mutagenesis in E. coli with Error-Prone DNA Polymerase I 131.4.2 Yeast Systems That Do Not Use Engineered DNA Polymerases for Mutagenesis 161.4.3 Somatic Hypermutation as a Means for Targeted Mutagenesis of GOIs 181.4.4 Orthogonal DNA Replication (OrthoRep) 201.5 Conclusion 22References 222 In Vivo Biosensors for Directed Protein Evolution 29Song Buck Tay and Ee Lui Ang2.1 Introduction 292.2 Nucleic Acid-Based In Vivo Biosensors for Directed Protein Evolution 322.2.1 RNA-Type Biosensors 322.2.2 DNA-Type Biosensors 352.3 Protein-Based In Vivo Biosensors for Directed Protein Evolution 372.3.1 Transcription Factor-Type Biosensors 372.3.2 Enzyme-Type Biosensors 412.4 Characteristics of Biosensors for In Vivo Directed Protein Evolution 442.5 Conclusions and Future Perspectives 45Acknowledgments 46References 463 High-Throughput Mass Spectrometry Complements Protein Engineering 57Tong Si, Pu Xue, Kisurb Choe, Huimin Zhao, and Jonathan V. Sweedler3.1 Introduction 573.2 Procedures and Instrumentation for MS-Based Protein Assays 593.3 Technology Advances Focusing on Throughput Improvement 623.4 Applications of MS-Based Protein Assays: Summary 633.4.1 Applications of MS-Based Assays: Protein Analysis 643.4.2 Applications of MS-Based Assays: Protein Engineering 663.5 Conclusions and Perspectives 68Acknowledgments 68References 694 Recent Advances in Cell Surface Display Technologies for Directed Protein Evolution 81Maryam Raeeszadeh-Sarmazdeh and Wilfred Chen4.1 Cell Display Methods 814.1.1 Phage Display 814.1.2 Bacterial Display Systems 834.1.3 Yeast Surface Display 844.1.4 Mammalian Display 854.2 Selection Methods and Strategies 864.2.1 High-Throughput Cell Screening 864.2.1.1 Panning 864.2.1.2 FACS 864.2.1.3 MACS 874.2.2 Selection Strategies 884.2.2.1 Competitive Selection (Counter Selection) 884.2.2.2 Negative/Positive Selection 894.3 Modifications of Cell Surface Display Systems 894.3.1 Modification of YSD for Enzyme Engineering 894.3.2 Yeast Co-display System 914.3.3 Surface Display of Multiple Proteins 914.4 Recent Advances to Expand Cell-Display Directed Evolution Techniques 934.4.1 μSCALE (Microcapillary Single-Cell Analysis and Laser Extraction) 934.4.2 Combining Cell Surface Display and Next-Generation Sequencing 944.4.3 PACE (Phage-Assisted Continuous Evolution) 944.5 Conclusion and Outlook 96References 975 Iterative Saturation Mutagenesis for Semi-rational Enzyme Design 105Ge Qu, Zhoutong Sun, and Manfred T. Reetz5.1 Introduction 1055.2 Recent Methodology Developments in ISM-Based Directed Evolution 1085.2.1 Choosing Reduced Amino Acid Alphabets Properly 1095.2.1.1 Limonene Epoxide Hydrolase as the Catalyst in Hydrolytic Desymmetrization 1095.2.1.2 Alcohol Dehydrogenase TbSADH as the Catalyst in Asymmetric Transformation of Difficult-to-Reduce Ketones 1105.2.1.3 P450-BM3 as the Chemo- and Stereoselective Catalyst in a Whole-Cell Cascade Sequence 1125.2.1.4 Multi-parameter Evolution Aided by Mutability Landscaping 1155.2.2 Further Methodology Developments of CAST/ISM 1175.2.2.1 Advances Based on Novel Molecular Biological Techniques and Computational Methods 1175.2.2.2 Advances Based on Solid-Phase Chemical Synthesis of SM Libraries 1185.3 B-FIT as an ISM Method for Enhancing Protein Thermostability 1205.4 Learning from CAST/ISM-Based Directed Evolution 1215.5 Conclusions and Perspectives 121Acknowledgment 124References 124Part II Rational and Semi-Rational Design 1336 Data-driven Protein Engineering 135Jonathan Greenhalgh, Apoorv Saraogee, and Philip A. Romero6.1 Introduction 1356.2 The Data Revolution in Biology 1366.3 Statistical Representations of Protein Sequence, Structure, and Function 1386.3.1 Representing Protein Sequences 1386.3.2 Representing Protein Structures 1406.4 Learning the Sequence-Function Mapping from Data 1416.4.1 Supervised Learning (Regression/Classification) 1416.4.2 Unsupervised/Semisupervised Learning 1446.5 Applying Statistical Models to Engineer Proteins 1456.6 Conclusions and Future Outlook 147References 1487 Protein Engineering by Efficient Sequence Space Exploration Through Combination of Directed Evolution and Computational Design Methodologies 153Subrata Pramanik, Francisca Contreras, Mehdi D. Davari, and Ulrich Schwaneberg7.1 Introduction 1537.2 Protein Engineering Strategies 1547.2.1 Computer-Aided Rational Design 1557.2.1.1 FRESCO 1557.2.1.2 FoldX 1577.2.1.3 CNA 1587.2.1.4 PROSS 1597.2.1.5 ProSAR 1607.2.2 Knowledge Based Directed Evolution 1617.2.2.1 Iterative Saturation Mutagenesis (ISM) 1617.2.2.2 Mutagenic Organized Recombination Process by Homologous In Vivo Grouping (MORPHING) 1617.2.2.3 Knowledge Gaining Directed Evolution (KnowVolution) 1627.3 Conclusions and Future Perspectives 171Acknowledgments 171References 1718 Engineering Artificial Metalloenzymes 177Kevin A. Harnden, Yajie Wang, Lam Vo, Huimin Zhao, and Yi Lu8.1 Introduction 1778.2 Rational Design 1778.2.1 Rational Design of Metalloenzymes Using De Novo Designed Scaffolds 1778.2.2 Rational Design of Metalloenzymes Using Native Scaffolds 1798.2.2.1 Redesign of Native Proteins 1798.2.2.2 Cofactor Replacement in Native Proteins 1818.2.2.3 Covalent Anchoring in Native Protein 1848.2.2.4 Supramolecular Anchoring in Native Protein 1878.3 Engineering Artificial Metalloenzyme by Directed Evolution in Combination with Rational Design 1888.3.1 Directed Evolution of Metalloenzymes Using De Novo Designed Scaffolds 1888.3.2 Directed Evolution of Metalloenzymes Using Native Scaffolds 1898.3.2.1 Cofactor Replacement in Native Proteins 1898.3.2.2 Covalent Anchoring in Native Protein 1928.3.2.3 Non-covalent Anchoring in Native Proteins 1948.4 Summary and Outlook 200Acknowledgment 201References 2019 Engineered Cytochromes P450 for Biocatalysis 207Hanan Alwaseem and Rudi Fasan9.1 Cytochrome P450 Monooxygenases 2079.2 Engineered Bacterial P450s for Biocatalytic Applications 2109.2.1 Oxyfunctionalization of Small Organic Substrates 2119.2.2 Late-Stage Functionalization of Natural Products 2209.2.3 Synthesis of Drug Metabolites 2249.3 High-throughput Methods for Screening Engineered P450s 2279.4 Engineering of Hybrid P450 Systems 2299.5 Engineered P450s with Improved Thermostability and Solubility 2309.6 Conclusions 231Acknowledgments 232References 232Part III Applications in Industrial Biotechnology 24310 Protein Engineering Using Unnatural Amino Acids 245Yang Yu, Xiaohong Liu, and Jiangyun Wang10.1 Introduction 24510.2 Methods for Unnatural Amino Acid Incorporation 24610.3 Applications of Unnatural Amino Acids in Protein Engineering 24710.3.1 Enhancing Stability 24810.3.2 Mechanistic Study Using Spectroscopic Methods 24810.3.3 Tuning Catalytic Activity 25010.3.4 Tuning Selectivity 25210.3.5 Enzyme Design 25210.3.6 Protein Engineering Toward a Synthetic Life 25510.4 Outlook 25610.5 Conclusions 258References 25811 Application of Engineered Biocatalysts for the Synthesis of Active Pharmaceutical Ingredients (APIs) 265Juan Mangas-Sanchez, Sebastian C. Cosgrove, and Nicholas J. Turner11.1 Introduction 26511.1.1 Transferases 26611.1.1.1 Transaminases 26611.1.2 Oxidoreductases 26711.1.2.1 Ketoreductases 26711.1.2.2 Amino Acid Dehydrogenases 27111.1.2.3 Cytochrome P450 Monoxygenases 27211.1.2.4 Baeyer–Villiger Monoxygenases 27311.1.2.5 Amine Oxidases 27411.1.2.6 Hydroxylases 27611.1.2.7 Imine Reductases 27611.1.3 Lyases 27811.1.3.1 Ammonia Lyases 27811.1.4 Isomerases 27811.1.5 Hydrolases 27911.1.5.1 Esterases 27911.1.5.2 Haloalkane Dehalogenase 27911.1.6 Multi-enzyme Cascade 28111.2 Conclusions 282References 28712 Directing Evolution of the Fungal Ligninolytic Secretome 295Javier Viña-Gonzalez and Miguel Alcalde12.1 The Fungal Ligninolytic Secretome 29512.2 Functional Expression in Yeast 29712.2.1 The Evolution of Signal Peptides 29712.2.2 Secretion Mutations in Mature Protein 30012.2.3 The Importance of Codon Usage 30112.3 Yeast as a Tool-Box in the Generation of DNA Diversity 30212.4 Bringing Together Evolutionary Strategies and Computational Tools 30512.5 High-Throughput Screening (HTS) Assays for Ligninase Evolution 30612.6 Conclusions and Outlook 309Acknowledgments 309References 31013 Engineering Antibody-Based Therapeutics: Progress and Opportunities 317Annalee W. Nguyen and Jennifer A. Maynard13.1 Introduction 31713.2 Antibody Formats 31813.2.1 Human IgG1 Structure 31813.2.2 Antibody-Drug Conjugates 31913.2.3 Bispecific Antibodies 32013.2.4 Single Domain Antibodies 32113.2.5 Chimeric Antigen Receptors 32113.3 Antibody Discovery 32213.3.1 Antibody Target Identification 32213.3.1.1 Cancer and Autoimmune Disease Targets 32313.3.1.2 Infectious Disease Targets 32313.3.2 Screening for Target-Binding Antibodies 32413.3.2.1 Synthetic Library Derived Antibodies 32413.3.2.2 Host-Derived Antibodies 32513.3.2.3 Immunization 32513.3.2.4 Pairing the Light and Heavy Variable Regions 32613.3.2.5 Humanization 32713.3.2.6 Hybrid Approaches to Antibody Discovery 32813.4 Therapeutic Optimization of Antibodies 32813.4.1 Serum Half-Life 32813.4.1.1 Antibody Half-Life Extension 32913.4.1.2 Antibody Half-Life Reduction 33113.4.1.3 Effect of Half-Life Modification on Effector Functions 33113.4.2 Effector Functions 33113.4.2.1 Effector Function Considerations for Cancer Therapeutics 33213.4.2.2 Effector Function Considerations for Infectious Disease Prophylaxis and Therapy 33313.4.2.3 Effector Function Considerations for Treating Autoimmune Disease 33413.4.2.4 Approaches to Engineering the Effector Functions of the IgG1 Fc 33413.4.3 Tissue Localization 33513.4.4 Immunogenicity 33513.4.4.1 Reducing T-Cell Recognition 33613.4.4.2 Reducing Aggregation 33613.5 Manufacturability of Antibodies 33613.5.1 Increasing Antibody Yield 33713.5.1.1 Codon Usage 33713.5.1.2 Signal Peptide Optimization 33713.5.1.3 Expression Optimization 33813.5.2 Alternative Production Methods 33813.6 Conclusions 339Acknowledgments 339References 33914 Programming Novel Cancer Therapeutics: Design Principles for Chimeric Antigen Receptors 353Andrew J. Hou and Yvonne Y. Chen14.1 Introduction 35314.2 Metrics to Evaluate CAR-T Cell Function 35414.3 Antigen-Recognition Domain 35614.3.1 Tuning the Antigen-Recognition Domain to Manage Toxicity 35614.3.2 Incorporation of Multiple Antigen-Recognition Domains to Engineer “Smarter” CARs 35614.3.3 Novel Antigen-Recognition Domains to Enhance CAR Modularity 35914.3.4 Engineering CARs that Target Soluble Factors 36014.4 Extracellular Spacer 36014.5 Transmembrane Domain 36214.6 Signaling Domain 36214.6.1 First- and Second-Generation CARs 36214.6.2 Combinatorial Co-stimulation 36314.6.3 Other Co-stimulatory Domains: ICOS, OX40, TLR2 36414.6.4 Additional Considerations for CAR Signaling Domains 36414.7 High-Throughput CAR Engineering 36614.8 Novel Receptor Modalities 367References 369Part IV Applications in Medical Biotechnology 37715 Development of Novel Cellular Imaging Tools Using Protein Engineering 379Praopim Limsakul, Chi-Wei Man, Qin Peng, Shaoying Lu, and Yingxiao Wang15.1 Introduction 37915.2 Cellular Imaging Tools Developed by Protein Engineering 38015.2.1 Fluorescent Proteins 38015.2.1.1 The FP Color Palette 38015.2.1.2 Photocontrollable Fluorescent Proteins 38115.2.1.3 Other Engineered Fluorescent Proteins 38315.2.2 Antibodies and Protein Scaffolds 38315.2.2.1 Antibodies 38315.2.2.2 Antibody-Like Protein Scaffolds 38415.2.2.3 Directed Evolution 38415.2.3 Genetically Encoded Non-fluorescent Protein Tags 38515.3 Application in Cellular Imaging 38615.3.1 Cell Biology Applications 38615.3.1.1 Localization 38615.3.1.2 Cell Signaling 38715.3.2 Application in Diagnostics and Medicine 39015.3.2.1 Detection 39015.3.2.2 Screening for Drugs 39215.4 Conclusion and Perspectives 393References 394Index 403