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E-raamat: Protein Engineering: Tools and Applications

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  • Sari: Advanced Biotechnology
  • Ilmumisaeg: 02-Aug-2021
  • Kirjastus: Blackwell Verlag GmbH
  • Keel: eng
  • ISBN-13: 9783527815111
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Protein Engineering: Tools and ApplicationsSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Advanced Biotechnology
  • Ilmumisaeg: 02-Aug-2021
  • Kirjastus: Blackwell Verlag GmbH
  • Keel: eng
  • ISBN-13: 9783527815111
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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. 

Part I Directed Evolution
1(132)
1 Continuous Evolution of Proteins In Vivo
3(26)
Alon Wellner
Arjun Ravikumar
Chang C. Liu
1.1 Introduction
3(2)
1.2 Challenges in Achieving In Vivo Continuous Evolution
5(5)
1.3 Phage-Assisted Continuous Evolution (PACE)
10(3)
1.4 Systems That Allow In Vivo Continuous Directed Evolution
13(9)
1.4.1 Targeted Mutagenesis in E. coli with Error-Prone DNA Polymerase
13(3)
1.4.2 Yeast Systems That Do Not Use Engineered DNA Polymerases for Mutagenesis
16(2)
1.4.3 Somatic Hypermutation as a Means for Targeted Mutagenesis of GOIs
18(2)
1.4.4 Orthogonal DNA Replication (OrthoRep)
20(2)
1.5 Conclusion
22(7)
References
22(7)
2 In Vivo Biosensors for Directed Protein Evolution
29(28)
Song Buck Tay
Ee Lui Ang
2.1 Introduction
29(3)
2.2 Nucleic Acid-Based In Vivo Biosensors for Directed Protein Evolution
32(5)
2.2.1 RNA-Type Biosensors
32(3)
2.2.2 DNA-Type Biosensors
35(2)
2.3 Protein-Based In Vivo Biosensors for Directed Protein Evolution
37(7)
2.3.1 Transcription Factor-Type Biosensors
37(4)
2.3.2 Enzyme-Type Biosensors
41(3)
2.4 Characteristics of Biosensors for In Vivo Directed Protein Evolution
44(1)
2.5 Conclusions and Future Perspectives
45(12)
Acknowledgments
46(1)
References
46(11)
3 High-Throughput Mass Spectrometry Complements Protein Engineering
57(24)
Tong Si
Pu Xue
Kisurb Choe
Huimin Zhao
Jonathan V. Sweedler
3.1 Introduction
57(2)
3.2 Procedures and Instrumentation for MS-Based Protein Assays
59(3)
3.3 Technology Advances Focusing on Throughput Improvement
62(1)
3.4 Applications of MS-Based Protein Assays: Summary
63(5)
3.4.1 Applications of MS-Based Assays: Protein Analysis
64(2)
3.4.2 Applications of MS-Based Assays: Protein Engineering
66(2)
3.5 Conclusions and Perspectives
68(13)
Acknowledgments
68(1)
References
69(12)
4 Recent Advances in Cell Surface Display Technologies for Directed Protein Evolution
81(13)
Maryam Raeeszadeh-Sarmazdeh
Wilfred Chen
4.1 Cell Display Methods
81(5)
4.1.1 Phage Display
81(2)
4.1.2 Bacterial Display Systems
83(1)
4.1.3 Yeast Surface Display
84(1)
4.1.4 Mammalian Display
85(1)
4.2 Selection Methods and Strategies
86(3)
4.2.1 High-Throughput Cell Screening
86(1)
4.2.1.1 Panning
86(1)
4.2.1.2 FACS
86(1)
4.2.1.3 MACS
87(1)
4.2.2 Selection Strategies
88(1)
4.2.2.1 Competitive Selection (Counter Selection)
88(1)
4.2.2.2 Negative/Positive Selection
89(1)
4.3 Modifications of Cell Surface Display Systems
89(4)
4.3.1 Modification of YSD for Enzyme Engineering
89(2)
4.3.2 Yeast Co-display System
91(1)
4.3.3 Surface Display of Multiple Proteins
91(2)
4.4 Recent Advances to Expand Cell-Display Directed Evolution Techniques
93(1)
4.4.1 nSCALE (Microcapillary Single-Cell Analysis and Laser Extraction)
93(1)
4.4.2 Combining Cell Surface Display and Next-Generation Sequencing
94(1)
4 A3 PACE (Phage-Assisted Continuous Evolution)
94(11)
4.5 Conclusion and Outlook
96(9)
References
97(8)
5 Iterative Saturation Mutagenesis for Semi-rational Enzyme Design
105(28)
Ge Qu
Zhoutong Sun
Manfred T. Reetz
5.1 Introduction
105(3)
5.2 Recent Methodology Developments in ISM-Based Directed Evolution
108(12)
5.2.1 Choosing Reduced Amino Acid Alphabets Properly
109(1)
5.2.1.1 Limonene Epoxide Hydrolase as the Catalyst in Hydrolytic Desymmetrization
109(1)
5.2.1.2 Alcohol Dehydrogenase TbSADH as the Catalyst in Asymmetric Transformation of Difficult-to-Reduce Ketones
110(2)
5.2.1.3 P450-BM3 as the Chemo- and Stereoselective Catalyst in a Whole-Cell Cascade Sequence
112(3)
5.2.1.4 Multi-parameter Evolution Aided by Mutability Landscaping
115(2)
5.2.2 Further Methodology Developments of CAST/ISM
117(1)
5.2.2.1 Advances Based on Novel Molecular Biological Techniques and Computational Methods
117(1)
5.2.2.2 Advances Based on Solid-Phase Chemical Synthesis of SM Libraries
118(2)
5.3 B-FIT as an ISM Method for Enhancing Protein Thermostability
120(1)
5.4 Learning from CAST/ISM-Based Directed Evolution
121(1)
5.5 Conclusions and Perspectives
121(12)
Acknowledgment
124(1)
References
124(9)
Part II Rational and Semi-Rational Design
133(110)
6 Data-driven Protein Engineering
135(18)
Jonathan Greenhalgh
Apoorv Saraogee
Philip A. Romero
6.1 Introduction
135(1)
6.2 The Data Revolution in Biology
136(2)
6.3 Statistical Representations of Protein Sequence, Structure, and Function
138(3)
6.3.1 Representing Protein Sequences
138(2)
6.3.2 Representing Protein Structures
140(1)
6.4 Learning the Sequence-Function Mapping from Data
141(4)
6.4.1 Supervised Learning (Regression/Classification)
141(3)
6.4.2 ~ Unsupervised/Semisupervised Learning
144(1)
6.5 Applying Statistical Models to Engineer Proteins
145(2)
6.6 Conclusions and Future Outlook
147(6)
References
148(5)
7 Protein Engineering by Efficient Sequence Space Exploration Through Combination of Directed Evolution and Computational Design Methodologies
153(24)
Subrata Pramanik
Francisco Contreras
Mehdi D. Davari
Ulrich Schwaneberg
7.1 Introduction
153(1)
7.2 Protein Engineering Strategies
154(17)
7.2.1 Computer-Aided Rational Design
155(1)
7.2.1.1 FRESCO
155(2)
7.2.1.2 FoldX
157(1)
7.2.1.3 CNA
158(1)
7.2.1.4 PROSS
159(1)
7.2.1.5 ProSAR
160(1)
7.2.2 Knowledge Based Directed Evolution
161(1)
7.2.2.1 Iterative Saturation Mutagenesis (ISM)
161(1)
7.2.2.2 Mutagenic Organized Recombination Process by Homologous In Vivo Grouping (MORPHING)
161(1)
7.2.2.3 Knowledge Gaining Directed Evolution (KnowVolution)
162(9)
7.3 Conclusions and Future Perspectives
171(6)
Acknowledgments
171(1)
References
171(6)
8 Engineering Artificial Metalloenzymes
177(30)
Kevin A. Hamden
Yajie Wang
Lam Vo
Huimin Zhao
Yi Lu
8.1 Introduction
177(1)
8.2 Rational Design
177(11)
8.2.1 Rational Design of Metalloenzymes Using De Novo Designed Scaffolds
177(2)
8.2.2 Rational Design of Metalloenzymes Using Native Scaffolds
179(1)
8.2.2.1 Redesign of Native Proteins
179(2)
8.2.2.2 Cofactor Replacement in Native Proteins
181(3)
8.2.2.3 Covalent Anchoring in Native Protein
184(3)
8.2.2.4 Supramolecular Anchoring in Native Protein
187(1)
8.3 Engineering Artificial Metalloenzyme by Directed Evolution in Combination with Rational Design
188(12)
8.3.1 Directed Evolution of Metalloenzymes Using De Novo Designed Scaffolds
188(1)
8.3.2 Directed Evolution of Metalloenzymes Using Native Scaffolds
189(1)
8.3.2.1 Cofactor Replacement in Native Proteins
189(3)
8.3.2.2 Covalent Anchoring in Native Protein
192(2)
8.3.2.3 Non-covalent Anchoring in Native Proteins
194(6)
8.4 Summary and Outlook
200(7)
Acknowledgment
201(1)
References
201(6)
9 Engineered Cytochromes P450 for Biocatalysis
207(36)
Hanan Alwaseem
Rudi Fasan
9.1 Cytochrome P450 Monooxygenases
207(3)
9.2 Engineered Bacterial P450s for Biocatalytic Applications
210(17)
9.2.1 Oxyfunctionalization of Small Organic Substrates
211(9)
9.2.2 Late-Stage Functionalization of Natural Products
220(4)
9.2.3 Synthesis of Drug Metabolites
224(3)
9.3 High-throughput Methods for Screening Engineered P450s
227(2)
9.4 Engineering of Hybrid P450 Systems
229(1)
9.5 Engineered P450s with Improved Thermostability and Solubility
230(1)
9.6 Conclusions
231(12)
Acknowledgments
232(1)
References
232(11)
Part III Applications in Industrial Biotechnology
243(134)
10 Protein Engineering Using Unnatural Amino Acids
245(20)
Yang Yu
Xiaohong Liu
Jiangyun Wang
10.1 Introduction
245(1)
10.2 Methods for Unnatural Amino Acid Incorporation
246(1)
10.3 Applications of Unnatural Amino Acids in Protein Engineering
247(9)
10.3.1 Enhancing Stability
248(1)
10.3.2 Mechanistic Study Using Spectroscopic Methods
248(2)
10.3.3 Tuning Catalytic Activity
250(2)
10.3.4 Tuning Selectivity
252(1)
10.3.5 Enzyme Design
252(3)
10.3.6 Protein Engineering Toward a Synthetic Life
255(1)
10.4 Outlook
256(2)
10.5 Conclusions
258(7)
References
258(7)
11 Application of Engineered Biocatalysts for the Synthesis of Active Pharmaceutical Ingredients (APIs)
265(30)
Juan Mangas-Sanchez
Sebastian C. Cosgrove
Nicholas J. Turner
11.1 Introduction
265(17)
11.1.1 Transferases
266(1)
11.1.1.1 Transaminases
266(1)
11.1.2 Oxidoreductases
267(1)
11.1.2.1 Ketoreductases
267(4)
11.1.2.2 Amino Acid Dehydrogenases
271(1)
11.1.2.3 Cytochrome P450 Monoxygenases
272(1)
11.1.2.4 Baeyer-Villiger Monoxygenases
273(1)
11.1.2.5 Amine Oxidases
274(2)
11.1.2.6 Hydroxylases
276(1)
11.1.2.7 Imine Reductases
276(2)
11.1.3 Lyases
278(1)
11.1.3.1 Ammonia Lyases
278(1)
11.1.4 Isomerases
278(1)
11.1.5 Hydrolases
279(1)
11.1.5.1 Esterases
279(1)
11.1.5.2 Haloalkane Dehalogenase
279(2)
11.1.6 Multi-enzyme Cascade
281(1)
11.2 Conclusions
282(13)
References
287(8)
12 Directing Evolution of the Fungal Ligninolytic Secretome
295(22)
Javier Vina-Gonzalez
Miguel Alcalde
12.1 The Fungal Ligninolytic Secretome
295(2)
12.2 Functional Expression in Yeast
297(5)
12.2.1 The Evolution of Signal Peptides
297(3)
12.2.2 Secretion Mutations in Mature Protein
300(1)
12.2.3 The Importance of Codon Usage
301(1)
12.3 Yeast as a Tool-Box in the Generation of DNA Diversity
302(3)
12.4 Bringing Together Evolutionary Strategies and Computational Tools
305(1)
12.5 High-Throughput Screening (HTS) Assays for Ligninase Evolution
306(3)
12.6 Conclusions and Outlook
309(8)
Acknowledgments
309(1)
References
310(7)
13 Engineering Antibody-Based Therapeutics: Progress and Opportunities
317(36)
Annalee W. Nguyen
Jennifer A. Maynard
13.1 Introduction
317(1)
13.2 Antibody Formats
318(4)
13.2.1 Human IgGl Structure
318(1)
13.2.2 Antibody-Drug Conjugates
319(1)
13.2.3 Bispecific Antibodies
320(1)
13.2.4 Single Domain Antibodies
321(1)
13.2.5 Chimeric Antigen Receptors
321(1)
13.3 Antibody Discovery
322(6)
13.3.1 Antibody Target Identification
322(1)
13.3.1.1 Cancer and Autoimmune Disease Targets
323(1)
13.3.1.2 Infectious Disease Targets
323(1)
13.3.2 Screening for Target-Binding Antibodies
324(1)
13.3.2.1 Synthetic Library Derived Antibodies
324(1)
13.3.2.2 Host-Derived Antibodies
325(1)
13.3.2.3 Immunization
325(1)
13.3.2.4 Pairing the Light and Heavy Variable Regions
326(1)
13.3.2.5 Humanization
327(1)
13.3.2.6 Hybrid Approaches to Antibody Discovery
328(1)
13.4 Therapeutic Optimization of Antibodies
328(8)
13.4.1 Serum Half-Life
328(1)
13.4.1.1 Antibody Half-Life Extension
329(2)
13.4.1.2 Antibody Half-Life Reduction
331(1)
13.4.1.3 Effect of Half-Life Modification on Effector Functions
331(1)
13.4.2 Effector Functions
331(1)
13.4.2.1 Effector Function Considerations for Cancer Therapeutics
332(1)
13.4.2.2 Effector Function Considerations for Infectious Disease Prophylaxis and Therapy
333(1)
13.4.2.3 Effector Function Considerations for Treating Autoimmune Disease
334(1)
13.4.2.4 Approaches to Engineering the Effector Functions of the IgGl Fc
334(1)
13.4.3 Tissue Localization
335(1)
13.4.4 Immunogenicity
335(1)
13.4.4.1 Reducing T-Cell Recognition
336(1)
13.4.4.2 Reducing Aggregation
336(1)
13.5 Manufacturability of Antibodies
336(3)
13.5.1 Increasing Antibody Yield
337(1)
13.5.1.1 Codon Usage
337(1)
13.5.1.2 Signal Peptide Optimization
337(1)
13.5.1.3 Expression Optimization
338(1)
13.5.2 Alternative Production Methods
338(1)
13.6 Conclusions
339(14)
Acknowledgments
339(1)
References
339(14)
14 Programming Novel Cancer Therapeutics: Design Principles for Chimeric Antigen Receptors
353(24)
Andrew J. Hou
Yvonne Y. Chen
14.1 Introduction
353(1)
14.2 Metrics to Evaluate CAR-T Cell Function
354(2)
14.3 Antigen-Recognition Domain
356(4)
14.3.1 Tuning the Antigen-Recognition Domain to Manage Toxicity
356(1)
14.3.2 Incorporation of Multiple Antigen-Recognition Domains to Engineer "Smarter" CARs
356(3)
14.3.3 Novel Antigen-Recognition Domains to Enhance CAR Modularity
359(1)
14.3.4 Engineering CARs that Target Soluble Factors
360(1)
14.4 Extracellular Spacer
360(2)
14.5 Transmembrane Domain
362(1)
14.6 Signaling Domain
362(4)
14.6.1 First-and Second-Generation CARs
362(1)
14.6.2 Combinatorial Co-stimulation
363(1)
14.6.3 Other Co-stimulatory Domains: ICOS, OX40, TLR2
364(1)
14.6.4 Additional Considerations for CAR Signaling Domains
364(2)
14.7 High-Throughput CAR Engineering
366(1)
14.8 Novel Receptor Modalities
367(10)
References
369(8)
Part IV Applications in Medical Biotechnology
377(17)
15 Development of Novel Cellular Imaging Tools Using Protein Engineering
379(15)
Praopim Limsakul
Chi-Wei Man
Qin Peng
Shaoying Lu
Yingxiao Wang
15.1 Introduction
379(1)
15.2 Cellular Imaging Tools Developed by Protein Engineering
380(6)
15.2.1 Fluorescent Proteins
380(1)
15.2.1.1 The FP Color Palette
380(1)
15.2.1.2 Photocontrollable Fluorescent Proteins
381(2)
15.2.1.3 Other Engineered Fluorescent Proteins
383(1)
15.2.2 Antibodies and Protein Scaffolds
383(1)
15.2.2.1 Antibodies
383(1)
15.2.2.2 Antibody-Like Protein Scaffolds
384(1)
15.2.2.3 Directed Evolution
384(1)
15.2.3 Genetically Encoded Non-fluorescent Protein Tags
385(1)
15.3 Application in Cellular Imaging
386(7)
15.3.1 Cell Biology Applications
386(1)
15.3.1.1 Localization
386(1)
15.3.1.2 Cell Signaling
387(3)
15.3.2 Application in Diagnostics and Medicine
390(1)
15.3.2.1 Detection
390(2)
15.3.2.2 Screening for Drugs
392(1)
15.4 Conclusion and Perspectives
393(1)
References 394(9)
Index 403
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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