In Silico Protein Structure and Function
From AI-Based to Hybrid Quantum Computing Approaches
- 1st Edition - June 1, 2027
- Latest edition
- Editors: Orkid Coskuner-Weber, Vladimir N. Uversky, Orkun Hasekioglu
- Language: English
In Silico Protein Structure and Function: From AI-Based to Hybrid Quantum Computing Approaches provides a comprehensive overview of computational techniques including classi… Read more
Description
Description
In Silico Protein Structure and Function: From AI-Based to Hybrid Quantum Computing Approaches provides a comprehensive overview of computational techniques including classical, AI-based, and hybrid quantum computing approaches, detailing algorithms such as generative AI and large language models, which is essential for scientists and engineers aiming to use these algorithms. Describes the latest cutting-edge AI-based and hybrid quantum computing approaches, providing readers with the knowledge needed to leverage these approaches to optimize developments in protein structure-function predictions, protein design, protein engineering and drug discovery. Rapid developments in computational biology related to AI-based and hybrid quantum computing tools often leave scientists and engineers needing resources to keep pace.
This book incorporates the latest algorithms, methodologies tools and applications, offering an up-to-date reference that informs readers of current trends. Protein structure and function prediction by AI-based and hybrid quantum computing tools demands both conceptual understanding and practical expertise. This book provides real-world applications and case studies that demonstrate how theoretical principles translate to practical scenarios, empowering readers to apply their knowledge in research settings. Practical examples illustrate how protein structure-function predictions are applied in protein design and protein engineering, drug discovery, and biotechnology areas. The latest trends, including applications in next-gen therapeutics, bioinformatics, and molecular biology are explored, providing readers with insights into where the field is heading, which is valuable for those who are preparing for the future of protein science, protein design, protein engineering, and molecular biology.
By offering clear explanations of complex topics and processes and highlighting real-world applications, In Silico Protein Structure and Protein Function: From AI-Based to Hybrid Quantum Computing Approaches equips readers to tackle modern challenges in protein design, protein engineering and drug development, ultimately fostering innovation across various sectors in life sciences, engineering, and industry
This book incorporates the latest algorithms, methodologies tools and applications, offering an up-to-date reference that informs readers of current trends. Protein structure and function prediction by AI-based and hybrid quantum computing tools demands both conceptual understanding and practical expertise. This book provides real-world applications and case studies that demonstrate how theoretical principles translate to practical scenarios, empowering readers to apply their knowledge in research settings. Practical examples illustrate how protein structure-function predictions are applied in protein design and protein engineering, drug discovery, and biotechnology areas. The latest trends, including applications in next-gen therapeutics, bioinformatics, and molecular biology are explored, providing readers with insights into where the field is heading, which is valuable for those who are preparing for the future of protein science, protein design, protein engineering, and molecular biology.
By offering clear explanations of complex topics and processes and highlighting real-world applications, In Silico Protein Structure and Protein Function: From AI-Based to Hybrid Quantum Computing Approaches equips readers to tackle modern challenges in protein design, protein engineering and drug development, ultimately fostering innovation across various sectors in life sciences, engineering, and industry
Key features
Key features
- In Silico Protein Structure and Function is an accessible resource for senior students, scientists, engineers, and professionals who want to deepen their understanding of protein structure and function predictions using the most up to date tools
- Describes the latest cutting-edge AI-based and hybrid quantum computing approaches, which are increasingly important in protein structure-function predictions, protein design, protein engineering, and drug discovery
- Incorporates the latest algorithms, methodologies and tools and applications, offering an up-to-date reference of current trends
- Includes a comprehensive overview of computational techniques including classical, AI-based and hybrid quantum computing approaches, detailing algorithms such as generative AI and large language models
- Practical examples are included, illustrating how protein structure-function predictions are applied in protein design, protein engineering, drug discovery and biotechnology areas. These case studies provide insights into solving real-world challenges using the advanced computational approaches covered
Readership
Readership
Researchers and scientists in academia and industry; computational biologists; structural biologists; molecular biologists; biochemists, biotechnologists
Table of contents
Table of contents
1. Fundamentals of Protein Structure and Function
2. Introduction to AI-Based Prediction Tools
3. Introduction to Hybrid Quantum Computing
4. Classical Computational Methods and Tools for Protein Structure-Function Predictions
5. Machine Learning in Protein Structure-Function Predictions
6. Deep Learning in Protein Structure-Function Predictions
7. Generative AI and Molecular Biology
8. Large Language Models and Molecular Biology
9. Hybrid Quantum Computing and Protein Structure-Function Predictions
10. Intrinsic Disorder Predictions in Proteins
11. Protein-Protein Interactions
12. Liquid-Liquid Phase Separation
13. Prediction of the Effects of Environmental Factors
14. Multi-domain and Complex Protein Structure Prediction
15. Mutagenesis and Protein Structure-Function Predictions
16. Protein Engineering and Design
17. Future Directions in Protein Structure-Function Predictions
2. Introduction to AI-Based Prediction Tools
3. Introduction to Hybrid Quantum Computing
4. Classical Computational Methods and Tools for Protein Structure-Function Predictions
5. Machine Learning in Protein Structure-Function Predictions
6. Deep Learning in Protein Structure-Function Predictions
7. Generative AI and Molecular Biology
8. Large Language Models and Molecular Biology
9. Hybrid Quantum Computing and Protein Structure-Function Predictions
10. Intrinsic Disorder Predictions in Proteins
11. Protein-Protein Interactions
12. Liquid-Liquid Phase Separation
13. Prediction of the Effects of Environmental Factors
14. Multi-domain and Complex Protein Structure Prediction
15. Mutagenesis and Protein Structure-Function Predictions
16. Protein Engineering and Design
17. Future Directions in Protein Structure-Function Predictions
Product details
Product details
- Edition: 1
- Latest edition
- Published: June 1, 2027
- Language: English
About the editors
About the editors
Orkid Coskuner-Weber
Editor
Dr Orkide Coşkuner Weber completed her PhD at the University of Cologne and then worked for many years both in academia and in a national laboratory in the United States. She worked as a postdoctoral fellow at Johns Hopkins University (Chemical and Biomolecular Engineering) for two years, as a postdoctoral fellow at Stanford University (SSRL) for two years and as a Research Assistant Professor at George Mason University. Her research interests include intrinsically disordered proteins and intrinsically disordered polymers
Affiliations and expertise
Turkish-German University, TurkeyVladimir N. Uversky
Editor
Prof. Vladimir N. Uversky, PhD, DSc, FRSB, FRSC, F.A.I.M.B.E., Professor at the Department of Molecular Medicine, Morsani College of Medicine, University of South Florida (USF), is a pioneer in the field of protein intrinsic disorder. He has made a number of groundbreaking contributions in the field of protein folding, misfolding, and intrinsic disorder. He obtained his PhD from Moscow Institute of Physics and Technology and D.Sc. from the Institute of Experimental and Theoretical Biophysics, Russian Academy of Sciences. Since 2010, Professor Uversky has worked at University of South Florida, where he works on various aspects of protein intrinsic disorder phenomenon and analysis of protein folding and misfolding processes. He has authored over 1250 scientific publications and edited several books and book series on protein structure, function, folding, misfolding, and intrinsic disorder. He also servs as an editor in a number of scientific journals.
Affiliations and expertise
Professor, Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, USAOrkun Hasekioglu
Editor
Dr Orkun Hasekioglu is Executive Vice President at TUBITAK, the Turkish Scientific and Technological Research Foundation. He is responsible for research institutions and international relations. He is also a member of the Board of Governors of TUBİTAK and the European Commission Joint Research Centers (JRC). He has BS, MS (Caltech) and PhD (RPI) degrees in electrical, computer and systems engineering, with a specialization in signal processing, communications systems and networks
Affiliations and expertise
Executive Vice President, TÜBİTAK, Ankara, Turkey