Protein Structure Prediction and Analysis, Volume 146 offers a comprehensive exploration of the methodologies, tools, and techniques utilized in the prediction and analysis of protein structures. The book delves into the fundamental principles of protein folding, modeling, and computational simulations, providing insights into the intricate process of predicting the three-dimensional structures of proteins. Sections in this new release include coverage of Current approaches for protein structure prediction and design, Protein Modelling, Prediction servers and applications in molecular modelling, Toward accurate in silico prediction of antigen binding affinities in antibody engineering, Molecular Blueprints: Guiding Drug Discovery through Protein Structure Analysis, and much more.
Additional sections focus on Protein Structure Prediction Using Dynamic Speciation Evolutionary Algorithm with Problem Information Aggregation, Tau protein structure and dynamics, Tau interacts with G-protein coupled receptors (GPCRs), How to accurately predict nanobody structure: Classical physics-based simulations or deep learning approaches, Medicinal Chemistry-based Novel Therapeutics for Tauopathies in Alzheimer’s Disease, Mapping the Pathogenic Nexus: Gene Overlap and Protein Interaction Networks in Alzheimer's and Breast Cancer as a Precursor to Protein Structure Prediction and Analysis, and much more.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xvii 7. Chapter One: Revolutionizing structural biology: AI-driven protein structure prediction from AlphaFold to next-generation innovations - p. 1 8. Chapter Two: Toward accurate in silico prediction of antigen binding affinities for antibody engineering - p. 21 9. Chapter Three: Molecular blueprints: Guiding drug discovery through protein structure analysis - p. 37 10. Chapter Four: Protein structure prediction with evolutionary algorithm - p. 101 11. Chapter Five: How to accurately predict nanobody structure: Classical physics-based simulations or deep learning approaches - p. 129 12. Chapter Six: Medicinal chemistry-based novel herapeutics for Tauopathies in Alzheimer’s disease - p. 151 13. Chapter Seven: Computational analysis of natural compound inhibitors targeting KPC-2 and KPC-3 carbapenemases in Klebsiella pneumoniae: Virtual screening and molecular dynamics studies - p. 177 14. Chapter Eight: Investigating the mechanisms of Plantago major and Plantago lanceolata in non-small cell lung cancer: A study of their bioactive phytocompounds through network pharmacology, molecular docking, and integrated computational approaches - p. 207 15. Chapter Nine: Tau protein structure and dynamics - p. 241 16. Chapter Ten: G-protein coupled receptors (GPCRs) interacts with Tau protein in Alzheimer’s disease - p. 259 17. Chapter Eleven: Mapping the pathogenic nexus: Gene overlap and protein interaction networks in Alzheimer’s and breast cancer as a precursor to protein structure prediction and analysis - p. 275 18. Chapter Twelve: Tau proteotasis in Alzheimer’s disease - p. 333 19. Chapter Thirteen: SUMO inhibits Tau aggregation in Alzheimer’s disease - p. 355 20. Chapter Fourteen: Apolipoprotein E and Tau interaction in Alzheimer's disease - p. 375 21. Chapter Fifteen: Cholesterol metabolism regulates Tauopathy in Alzheimer’s disease - p. 401 22. Chapter Sixteen: Deciphering the impact of ABCA4 genetic variants of unknown significance in inherited retinal disease through computational and functional approaches - p. 423 23. Chapter Seventeen: The structure-based approaches to computing viral fitness - p. 461 24. Chapter Eighteen: Structural insights from neuraminidase diversity: Implications for selectivity in anti-infective and anticancer strategies - p. 499 25. Chapter Nineteen: Machine learning and molecular modeling based design of nanobodies targeting human serotonin transporter and receptor - p. 535
Protein Structure Prediction and Analysis, Volume 146 offers a comprehensive exploration of the methodologies, tools, and techniques utilized in the prediction and analysis of protein structures. The book delves into the fundamental principles of protein folding, modeling, and computational simulations, providing insights into the intricate process of predicting the three-dimensional structures of proteins. Sections in this new release include coverage of Current approaches for protein structure prediction and design, Protein Modelling, Prediction servers and applications in molecular modelling, Toward accurate in silico prediction of antigen binding affinities in antibody engineering, Molecular Blueprints: Guiding Drug Discovery through Protein Structure Analysis, and much more.
Additional sections focus on Protein Structure Prediction Using Dynamic Speciation Evolutionary Algorithm with Problem Information Aggregation, Tau protein structure and dynamics, Tau interacts with G-protein coupled receptors (GPCRs), How to accurately predict nanobody structure: Classical physics-based simulations or deep learning approaches, Medicinal Chemistry-based Novel Therapeutics for Tauopathies in Alzheimer’s Disease, Mapping the Pathogenic Nexus: Gene Overlap and Protein Interaction Networks in Alzheimer's and Breast Cancer as a Precursor to Protein Structure Prediction and Analysis, and much more.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xvii 7. Chapter One: Revolutionizing structural biology: AI-driven protein structure prediction from AlphaFold to next-generation innovations - p. 1 8. Chapter Two: Toward accurate in silico prediction of antigen binding affinities for antibody engineering - p. 21 9. Chapter Three: Molecular blueprints: Guiding drug discovery through protein structure analysis - p. 37 10. Chapter Four: Protein structure prediction with evolutionary algorithm - p. 101 11. Chapter Five: How to accurately predict nanobody structure: Classical physics-based simulations or deep learning approaches - p. 129 12. Chapter Six: Medicinal chemistry-based novel herapeutics for Tauopathies in Alzheimer’s disease - p. 151 13. Chapter Seven: Computational analysis of natural compound inhibitors targeting KPC-2 and KPC-3 carbapenemases in Klebsiella pneumoniae: Virtual screening and molecular dynamics studies - p. 177 14. Chapter Eight: Investigating the mechanisms of Plantago major and Plantago lanceolata in non-small cell lung cancer: A study of their bioactive phytocompounds through network pharmacology, molecular docking, and integrated computational approaches - p. 207 15. Chapter Nine: Tau protein structure and dynamics - p. 241 16. Chapter Ten: G-protein coupled receptors (GPCRs) interacts with Tau protein in Alzheimer’s disease - p. 259 17. Chapter Eleven: Mapping the pathogenic nexus: Gene overlap and protein interaction networks in Alzheimer’s and breast cancer as a precursor to protein structure prediction and analysis - p. 275 18. Chapter Twelve: Tau proteotasis in Alzheimer’s disease - p. 333 19. Chapter Thirteen: SUMO inhibits Tau aggregation in Alzheimer’s disease - p. 355 20. Chapter Fourteen: Apolipoprotein E and Tau interaction in Alzheimer's disease - p. 375 21. Chapter Fifteen: Cholesterol metabolism regulates Tauopathy in Alzheimer’s disease - p. 401 22. Chapter Sixteen: Deciphering the impact of ABCA4 genetic variants of unknown significance in inherited retinal disease through computational and functional approaches - p. 423 23. Chapter Seventeen: The structure-based approaches to computing viral fitness - p. 461 24. Chapter Eighteen: Structural insights from neuraminidase diversity: Implications for selectivity in anti-infective and anticancer strategies - p. 499 25. Chapter Nineteen: Machine learning and molecular modeling based design of nanobodies targeting human serotonin transporter and receptor - p. 535
