Computational and Experimental Approaches in Protein Engineering and Design
Will be published approx. on 10. November 2026
Book
Paperback/Softback
600 pages
978-0-443-44536-1 (ISBN)
Description
Computational and Experimental Approaches in Protein Engineering and Design offers a one-stop resource for students, researchers, and practitioners. Cutting-edge techniques in AI and machine learning applications are expertly covered in the book, empowering readers with the tools they need to tackle modern challenges in biotechnology and therapeutics. Step-by-step protocols provide readers with actionable guidance to transition smoothly from computational modelling to laboratory implementation. The inclusion of real-world case studies makes the theoretical concepts relatable and actionable and immediately applicable to solving complex problems, such as producing protein-based vaccines for immunotherapy.
The book's focus on synthetic biology positions it at the forefront of innovation, appealing to a broad audience, from those involved in tissue engineering to those exploring next-generation therapeutics. With its forward-looking approach, emphasizing machine-learning-driven protein design and nanobody engineering, this book is an indispensable resource for researchers preparing to navigate the future of biotechnology.
The book's focus on synthetic biology positions it at the forefront of innovation, appealing to a broad audience, from those involved in tissue engineering to those exploring next-generation therapeutics. With its forward-looking approach, emphasizing machine-learning-driven protein design and nanobody engineering, this book is an indispensable resource for researchers preparing to navigate the future of biotechnology.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 229 mm
Width: 152 mm
Weight
449 gr
ISBN-13
978-0-443-44536-1 (9780443445361)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Dr Aditya Kumar Padhi is an Assistant Professor at the School of Biochemical Engineering, Indian Institute of Technology (IIT-BHU), Varanasi, India. He earned his Ph.D. from the Indian Institute of Technology, Delhi (IIT Delhi) in 2015 and currently leads the Laboratory for Computational Biology and Biomolecular Design (LCBD) at IIT-BHU. His lab specializes in Computational Biology, Protein Design, Structural and Translational Informatics, Computational Biophysics, Antimicrobial Resistance, Predictive Modelling, Biomolecular Simulations, Conformational Analyses, Machine Learning, and Disease Mechanisms Professor Timir Tripathi is a Professor of Molecular Biology, School of Life Sciences, North-Eastern Hill University, Shillong, India. Earlier, he served as the Regional Director of Indira Gandhi National Open University. His previous role was Senior Assistant Professor and Principal Investigator at the Department of Biochemistry, NEHU, Shillong. He holds a Ph.D. from Jawaharlal Nehru University, New Delhi. His primary research focus is studying the conformational dynamics, interaction, and stabilization of the complexes formed by intrinsically disordered neuropathological protein aggregates, their properties of liquid-liquid phase separation, interaction, and roles in nucleocytoplasmic transport in neurodegenerative diseases. Professor Tripathi is an Associate Fellow of the Indian National Science Academy, New Delhi, and an elected member of the National Academy of Sciences, India, the Royal Society of Chemistry, and the Royal Society of Biology, UK
Content
1. Fundamentals of protein structure and function
2. Introduction to the principles of protein engineering
3. Computational methods and tools for protein design and optimization
4. Experimental methods in protein engineering: techniques and applications
5. Engineering antibodies for therapeutic applications
6. Techniques and applications of directed evolution
7. Machine learning and artificial intelligence in protein design
8. Computational protein design for disease mutations and antimicrobial resistance
9. Case studies in protein engineering: successes and challenges
10. Designing protein-based vaccines for immunotherapy
11. Nanobody engineering for next-generation therapeutics
12. Molecular dynamics simulations in protein design and engineering
13. Biophysical characterization methods for evaluating therapeutic proteins
14. Incorporating non-natural amino acids in protein design and expanding the chemical space of proteins.
15. Methods to enhance protein stability and solubility for improved functional efficacy
16. Engineering allosteric regulation and control mechanisms in proteins
17. Design of protein scaffolds for biomaterials and tissue engineering
18. High-throughput screening techniques for identifying therapeutic proteins
19. Advancements in protein conjugation techniques for engineering and design applications
20. Challenges in AI-based approaches for intrinsically disordered proteins
21. The role of machine learning in drug discovery and therapeutic protein design
2. Introduction to the principles of protein engineering
3. Computational methods and tools for protein design and optimization
4. Experimental methods in protein engineering: techniques and applications
5. Engineering antibodies for therapeutic applications
6. Techniques and applications of directed evolution
7. Machine learning and artificial intelligence in protein design
8. Computational protein design for disease mutations and antimicrobial resistance
9. Case studies in protein engineering: successes and challenges
10. Designing protein-based vaccines for immunotherapy
11. Nanobody engineering for next-generation therapeutics
12. Molecular dynamics simulations in protein design and engineering
13. Biophysical characterization methods for evaluating therapeutic proteins
14. Incorporating non-natural amino acids in protein design and expanding the chemical space of proteins.
15. Methods to enhance protein stability and solubility for improved functional efficacy
16. Engineering allosteric regulation and control mechanisms in proteins
17. Design of protein scaffolds for biomaterials and tissue engineering
18. High-throughput screening techniques for identifying therapeutic proteins
19. Advancements in protein conjugation techniques for engineering and design applications
20. Challenges in AI-based approaches for intrinsically disordered proteins
21. The role of machine learning in drug discovery and therapeutic protein design